gt enzymes 2015 ggmm 2015

GT Enzymes 2015 (25-27 Mai 2015)

et

GGMM 2015 (26-28 Mai 2015)

à SETE

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Bienvenue !

Bonjour à toutes et tous et bienvenue à Sète.

Nous sommes très heureux de vous accueillir au centre Lazaret du 25 au 28 mai 2015 pour le 4ème congrès du Groupe Thé matique "Enzymes: Structure, Fonction, Catalyse, Ingénierie et Régulation" et pour le 19 ème congrès du "Groupe de Graphism e et de Modélisation Moléculaire".

Cette année, ces congrès se recouperont pour accentuer les échanges entre les deux groupes thématiques qui partagent des objectifs communs. Cet événement permettra de relier le côté expérimental de l' enzymologie aux m éthodes prédictives en passant par le graphisme structural et moléculaire. Deux dem i-journées seront consacrées à des thém atiques communes: "les enzy mes comme cibles th érapeutiques" et "Ingén ierie enzymatique et évolution dirigée". Nous rem ercions les bureaux des deux groupes pou r avoir accepté cette mixité exceptionnelle.

Le programme comprend 6 sessions, incluant 6 conférences plénières de 45 minutes et 3 7 conférences de 15 minutes dont les orateurs ont été sélectionnés à partir des résumé soumis, et croyez-nous le choix n'a pas été facile! Toutes les présentations par affiche, au nombre de 45, donneront lieu à une présentation "Flash" d'une diapositive unique avec une m inute de présentation. Vous pourrez profit er des trois longues sessions de présentations par affiche pour discuter avec leurs auteurs et deux prix pour la m eilleure affiche seront rem is à l' issue des congrès respectifs. Vous participerez au jeu de "l'expert est dans la salle" qui permettra de profiter de savoir-faire originaux. Enfin, nous ser ons heureux d'entendre le lauréat du prix du GGMM, Isidro Cortes, dont l' excellent dossier a été retenu par le bureau du GGMM parmi l’ensemble des très bons dossiers déposés.

Nous tenons à remercier les conférenciers invités, J. Blumberger, J. Cherfils, D. Douguet, D. Hilvert, T. Isenberg et T. St rick, dont certains viennent de loin. Nous espérons que vous profiterez pleinement de leur présence et que les discussions seront nom breuses et intéressantes.

Nous remercions également le comité scientifiq ue pour son aide précieuse dans l' élaboration de ce programme: I. André, M. Baaden, S. Barb e, S. Boschi-Muller, N. Colloc' h, A. Kajava, C. Léger, M. Remaud-Simeon, F. Rodrigues-Lima, O. Sperandio, O. Taboureau et C. Tellier.

Les rencontres se dérouleront dans un endroit c onvivial qui bénéficie d' un accès direct à la plage. Afin de profiter au m aximum de la belle Région du Languedoc Roussillon, un apéritif Sétois et une dégustation de pr oduits régionaux seront offerts a ux participants. Et pour ceux qui veulent poursuivre les discussions, le bar sera ouvert jusqu'à tard dans la soirée!

Nous vous souhaitons un excellent congrès, de s échanges intéressants et des rencontres enrichissantes.

L'équipe organisatrice

Rachel Cerdan Laurent Chaloin Frédéric de Lamotte Gilles Labesse Corinne Lionne

Le GT Enzymes

Le Groupe Thématique « Enzymes : Structure, Fonction, Catalyse, Ingénierie, Régulation » a été créé en 2008 sous l’impulsion du conseil d’administration de la SFBBM, et à l’initiative du Pr Guy Branlant. Ce GT a pour but de fédérer au niveau national la communauté scientifique des enzymologistes au sens large, de participer à la formation des étudiants et de contribuer au maintien et au développement de cette discipline.

Depuis 2009, le GT organise un congrès tous les deux ans. Le premier

congrès était axé sur les aspects modernes de l'enzymologie en insistant sur les problèmes à l'interface de la chimie et de la biologie (Nancy, 2009), le second concernait les différentes disciplines et approches dans un contexte de biologie intégrative (Ax-les-Thermes, 2011), alors que le troisième abordait les enzymes dans leurs dimensions moléculaires et intégratives (Paris, 2013).

Les congrès du GT permettent à des chercheurs de stature internationale

mais aussi à de jeunes scientifiques de présenter leurs travaux, d’échanger sur de nouveaux résultats et projets, mais également d’établir de nouvelles collaborations et d’échanger informations et matériel. Ils contribuent ainsi à maintenir une dynamique de recherche nationale et une compétitivité sur le plan international.

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Le GGMM

Le GGMM est une société savante qui rassemble une grande partie de la communauté française dont l’activité́ est dédiée à, ou implique, l’utilisation de la modélisation moléculaire. Cette association loi 1901 a été créée en 1983 à l’initiative de Joël Janin, Evelyne James-Surcouf et Gérard Pepe dans le but de promouvoir le développement de la modélisation moléculaire. Le GGMM est également un groupe thématique de la Société Française de Biophysique (SFB) et de la Société Française de Biochimie et Biologie Moléculaire (SFBBM).

Le GGMM regroupe aujourd’hui un très large panel de thématiques d'une grande diversité allant de la bio-informatique à la chimie théorique, avec des représentations à plusieurs échelles comme l'électron, les molécules et leurs assemblages et même la cellule avec des approches globales de type protéomique. Le but du GGMM est de promouvoir le développement de la modélisation moléculaire au sens large, par la mise en place d’actions spécifiques telles que le prix du GGMM qui est décerné, tous les deux ans, à un jeune chercheur en récompense de travaux originaux ainsi que la diffusion d’offres d’emplois, académiques et industrielles, en modélisation moléculaire. Il s'agit en priorité de permettre aux futures générations de modélisateurs de s'exprimer et s'échanger.

Tous les deux ans, le GGMM organise un congrès dont l’objectif est de rassembler la communauté́ francophone pour communiquer sur les avancées dans le domaine de la modélisation et la dynamique moléculaire, les nouvelles approches et les nouveaux développements pour l’analyse des structures, les avancées en chimie théorique en drug design, etc...

Le site web du GGMM est accessible à cette adresse : http://ggmmfr.wordpress.com/

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Nos partenaires

Nous tenons à remercier chaleureusement tous nos partenaires et sponsors

pour leur soutien financier et matériel, sans qui l'organisation de ce double congrès

n'aurait pas été possible. Merci pour votre soutien et votre fidélité.

Programme

Lundi 25 mai 2015

12:30 Déjeuner

14:15 Mot de Bienvenue – GT Enzymes

Session 1 – Structures et mécanismes enzymatiques Modérateurs : Gilles LABESSE et Fernando RODRIGUES-LIMA

14:30 Jacqueline CHERFILS – Conférence Plénière

Structure and function of bacterial FIC toxins

15:15 Jérôme SANTOLINI De quoi la NO-Synthase est elle le nom ?

15:30 Nolan CHATRON Piston-driven activation mechanism of VKORC1, the human vitamin K epoxide reductase

15:45 Bertrand CASTAING Inhibition sélective des ADN glycosylases : approches structure-fonction

16:00 Pause café

16:30 Présentations Flash n°1 – Posters 1 à 7 S. BOSCHI-MULLER / N. COLLOC'H / R. DUVAL / A. KRIZNIK / C. MATHIEU / M. MERROUCH / S. RAHUEL-CLERMONT

16:45 Ewelina GUCA Structural characterization of Plasmodium falciparum CCT and fragment-based drug design approach for targeting phospholipid biosynthesis pathway

17:00 Pierre LAFITE Understanding the mechanism of UGT74B1 from Arabidopsis thaliana: S- or O-glycosyltransferase ?

17:15 Claire STINES-CHAUMEIL Monitoring the transition of apo i nto holo forms of soluble glucose dehydrogenase by stopped-flow and c rystallographic studies to decipher the mechanism of reconstitution/activation of this enzyme

17:30 Jean-Christophe LEC Mécanisme de la 3-mercaptopyruvate sulfurtransférase : une enzyme impliquée dans la production de sulfure d'hydrogène

17:45 Présentation Elsevier – Journal Biochimie

18:00 Apéritif de bienvenue SESSION POSTERS

19:30 Dîner

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Mardi 26 mai 2015

Matin

Session 2 – Régulation et efficacité Modérateurs : Sandrine BOSCHI-MULLER et Charles TELLIER

09:30 Terence STRICK – Conférence Plénière Approches corrélatives en analyse molécule-unique

10:15 Hélène MUNIER-LEHMANN Régulation des IMPDHs bactériennes au travers des modules CBS

10:30 Nicolas BOSC Identification of Discriminant Conformation-Dependent Residues of Protein Kinases: a Proteometric Analysis

10:45 Jessica HADJ-SAID La Carbon Monoxide Dehydrogenase (CODH) de Desulfovibrio vulgaris

11:00 Pause café

11:30 Loic CARRIQUE Study of the regulatory behavior of Plasmodium falciparum IMP-specific 5'-nucleotidase (PfISN1)

11:45 Sophie RAHUEL-CLERMONT Cellular redox signaling by thiol peroxidase : Mechanisms responsible for the specificity of the redox relay H2O2/Orp1/Yap1 in S. cerevisiae

12:00 Marc-André DELSUC Amyloid properties of a conserved domain in the N-Term domain of the Androgen Receptor - Implications for new therapeutic routes

12:15 Déjeuner

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Mardi 26 mai 2015

Après-midi

14:00 Mot de Bienvenue – GGMM

Session 3 – Ingénierie enzymatique et évolution dirigée Modérateurs : Christophe LEGER et Magali REMAUD SIMEON

14:15 Donald HILVERT – Conférence Plénière

Design and optimization of artificial enzymes: nearer to nature

15:00 Présentations Flash n°2 – Posters 8 à 17 I. ANDRE / S. BARBE / A. BRAKA / M. BRUT / K. DRUART / N. FLOQUET / M. GUEROULT / S. KELLOU-TAIRI / V. MARTINY / C. RIEUX

15:15 Thomas GAILLARD Design computationnel de protéines avec une fonction d'énergie MMGBSA

15:30 Seydou TRAORE Cost Function Network Optimization: exact algorithms towards Computational Protein Design

15:45 David RINALDO Computational approach to enzyme design

16:00 Présentations Flash n°3 – Posters 18 à 26 F. BARAKAT / S. BAUD / J-B. CHERON / J. CORTES / S. CROUZY / D. DE VECCHIS / S. DOUTRELIGNE / L. VERZEAUX / D. FLATTERS

16:15 Pause café

16:45 SESSION POSTERS

18:30 Apéritif Sétois

19:30 Dîner Soirée festive

Mercredi 27 mai 2015

Matin

Session 4 – Les enzymes comme cibles thérapeutiques Modérateurs : Rachel CERDAN et Laurent CHALOIN

09:00 Dominique DOUGUET – Conférence Plénière

Enzymes as target class for approved drugs The case of secretory phospholipase A2 (sPLA2) family of proteins as novel therapeutic targets

09:45 Présentations Flash n°4 – Posters 27 à 35 N. FLOQUET / C. GAGEAT / S. GRUDININ / M. KATAVA / Y. LAURIN / G. LEROUX / C. MAROT / I. MEREU / D. MIAS-LUCQUIN

10:00 Kamel DJAOUT Développement d'antibactériens dirigés contre la ThyX de Mycobacterium tuberculosis

10:15 Fanny KREBS Molecular dynamics study of ligand recognition by DXR: implications for the design of new antibiotic compounds

10:30 Jana SOPKOVA-DE OLIVEIRA SANTOS Discovery of Oligopyridyl scaffold molecules as potent Mcl-1 inhibitors

10:45 Pause café

11:15 Présentations Flash n°5 – Posters 36 à 45 A-E. MOLZA / D. MONET / M. NG FUK CHONG / M-K. NGUYEN / J. REBEHMED / N. RENAULT / E. SELWA / D. STRATMANN / T. TUBIANA / S. WIENINGER

11:30 Florent BARBAULT Into the intimacy of an irreversible inhibition: SMD(QM/MM) simulations of the fibroblast growth factor receptor-1 (FGFR1) kinase domain

11:45 Inès RASOLOHERY PatchSearch: a fast method for flexible recognition of protein binding sites

12:00 Hiba ABI HUSSEIN PockDrug-Server: a new web server for predicting pocket druggability on hol o and apo proteins

12:15 Prix poster GT Enzymes

12:30 Déjeuner et départ GT Enzymes

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Mercredi 27 mai 2015

Après-midi

Session 5 – Relations dynamique - fonction Modérateurs : Nathalie COLLOC'H et Andrey KAJAVA

14:00 Jochen BLUMBERGER – Conférence Plénière

Combining molecular dynamics and Markov state modelling to understand ligand transport in enzymes

14:45 Jérôme EBERHARDT Structural dynamics of the retinoid X receptor ligand binding domain by accelerated molecular dynamics

15:00 Benoît DAVID Turning glycoside hydrolases into transglycosidases: an e xperimental and theoretical study of the internal water dynamics in the Thermus thermophilus β-glycosidase

15:15 Julien DIHARCE Description théorique d'un phénomène de "substrate channeling" dans la biosynthèse des flavonoïdes

15:30 Maxime LOUET L'allostérie dynamique de la protéine CAP révélée par les forces inter-atomiques

15:45 Présentation du jeu "L'expert est dans la salle"

16:00 Pause café

16:30 Matthieu CHAVENT Assessing the crowding of Membrane Proteins at the Mesoscale

16:45 Kaouther BEN OUIRANE New way to fight against antibiotic resistance: reconstruction of a t hree-component efflux pump

17:00 Jean-Pierre DUNEAU Étude de l'assemblage des protéines membranaires par simulation de dynamique moléculaire à gros grains et méthodes de Forces de Biais Adaptatif

17:15 Christophe NARTH Tinker-HP : Dynamique moléculaire polarisable haute performance

17:30 Assemblée Générale GGMM

18:00 Dégustation de produits régionaux SESSION POSTERS

19:30 Dîner

21:00 "L'expert est dans la salle"

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Jeudi 28 mai 2015

Matin

Session 6 – Visualisation et prédiction structurale Modérateurs : Marc BAADEN et Frédéric DE LAMOTTE

09:00 Tobias ISENBERG – Conférence Plénière

Illustrative Molecular Visualizations

09:45 Fabrice ALLAIN Modélisation de novo de structures protéiques par contacts évolutifs

10:00 Mathias FERBER Integrating the Solvent Accessible Surface Distance with cross-links-based modeling methods improves the conformational sampling of protein assemblies

10:15 Emilie NEVEU A Detailed Data-Driven Protein-Protein Interaction Potential Accelerated By Polar Fourier Correlation

10:30 Isaure CHAUVOT DE BEAUCHENE Fragment-based modeling of ssRNA-protein complexes

10:45 Pause café

11:15 Jean-Christophe GELLY

ORION : improving remote homology detection using a structural alphabet

11:30 Stéphane REDON SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems

11:45 Prix du GGMM - Isidro CORTES Integration of Chemical and B iological (’omics’) Data for the Prediction of Cancer Cell-Line Sensitivity

12:15 Prix poster GGMM

12:30 Déjeuner et départ GGMM

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Table des matières

Edito 4

Le GT Enzymes 5

Le GGMM 6

Nos Partenaires 7

Programme détaillé 8

Structures et mécanismes enzymatiques 21

Structure and function of bacterial FIC toxins, Jacqueline Cherfils . . . . . . . . 22

De quoi la NO-Synthase est elle le nom ?, Jérôme Santolini . . . . . . . . . . . . 23

Piston-driven activation mechanism of VKORC1, the human vitamin K epoxidereductase, Nolan Chatron [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Inhibition sélective des ADN glycosylases : approches structure-fonction, BertrandCastaing [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Structural characterization of Plasmodium falciparum CCT and fragment-baseddrug design approach for targeting phospholipid biosynthesis pathway, EwelinaGuca [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Understanding the mechanism of UGT74B1 from Arabidopsis thaliana: S- or O-glycosyltransferase ?, Pierre Lafite [et al.] . . . . . . . . . . . . . . . . . . . . . . 28

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Monitoring the transition of apo into holo forms of soluble glucose dehydroge-nase by stopped-flow and crystallographic studies to decipher the mechanism ofreconstitution/activation of this enzyme., Claire Stines-Chaumeil [et al.] . . . . . 29

Mécanisme de la 3-mercaptopyruvate sulfurtransférase : une enzyme impliquéedans la production de sulfure d’hydrogène., Jean-Christophe Lec [et al.] . . . . . 30

Régulation et efficacité 31

Approches correlatives en analyse molecule-unique, Terence Strick . . . . . . . . . 32

Régulation des IMPDHs bactériennes au travers des modules CBS, Hélène Munier-Lehmann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Identification of Discriminant Conformation-Dependent Residues of Protein Ki-nases: a Proteometric Analysis, Nicolas Bosc [et al.] . . . . . . . . . . . . . . . . 34

La Carbon Monoxide Dehydrogenase (CODH) de Desulfovibrio vulgaris., JessicaHadj-Said [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Study of the regulatory behavior of Plasmodium falciparum IMP-specific 5’-nucleotidase(PfISN1), Loic Carrique [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Cellular redox signaling by thiol peroxidase : Mechanisms responsible for thespecificity of the redox relay H2O2/Orp1/Yap1 in S. cerevisiae, Antoine Berweiler[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Amyloid properties of a conserved domain in the N-Term domain of the AndrogenReceptor - Implications for new therapeutic routes -, Julia Asencio Hernández [etal.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Ingénierie enzymatique et évolution dirigée 41

Design and optimization of artificial enzymes: nearer to nature, Donald Hilvert . 42

Design computationnel de protéines avec une fonction d’énergie MMGBSA, ThomasGaillard [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Cost Function Network Optimization: exact algorithms towards ComputationalProtein Design, Seydou Traore [et al.] . . . . . . . . . . . . . . . . . . . . . . . . 45

Computational approach to enzyme design, David Rinaldo . . . . . . . . . . . . . 48

Les enzymes comme cibles thérapeutiques 49

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Enzymes as target class for approved drugs The case of secretory phospholipaseA2 (sPLA2) family of proteins as novel therapeutic targets, Dominique Douguet . 50

Développement d’antibactériens dirigés contre la ThyX de Mycobacterium tuber-culosis, Kamel Djaout [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Molecular dynamics study of ligand recognition by DXR: implications for thedesign of new antibiotic compounds, Fanny Krebs [et al.] . . . . . . . . . . . . . . 52

Discovery of Oligopyridyl scaffold molecules as potent Mcl-1 inhibitors, JanaSopkova-De Oliveira Santos [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Into the intimacy of an irreversible inhibition: SMD(QM/MM) simulations of thefibroblast growth factor receptor-1 (FGFR1) kinase domain., Yan Li [et al.] . . . 55

PatchSearch: a fast method for flexible recognition of protein binding sites, InèsRasolohery [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

PockDrug-Server: a new web server for predicting pocket druggability on holo andapo proteins, Hiba Abi Hussein [et al.] . . . . . . . . . . . . . . . . . . . . . . . . 59

Relations dynamique - fonction 60

Combining molecular dynamics and Markov state modelling to understand ligandtransport in enzymes, Jochen Blumberger [et al.] . . . . . . . . . . . . . . . . . . 61

Structural dynamics of the retinoid X receptor ligand binding domain by acceler-ated molecular dynamics, Jérôme Eberhardt [et al.] . . . . . . . . . . . . . . . . . 62

Turning glycoside hydrolases into transglycosidases: an experimental and the-oretical study of the internal water dynamics in the Thermus thermophilus -glycosidase, Benoît David [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Description théorique d’un phénomène de ”substrate channeling” dans la biosyn-thèse des flavonoïdes., Julien Diharce [et al.] . . . . . . . . . . . . . . . . . . . . . 65

L’allostérie dynamique de la protéine CAP révélée par les forces inter-atomiques, MaximeLouet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Assessing the crowding of Membrane Proteins at the Mesoscale, Matthieu Chavent[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

New way to fight against antibiotic resistance: reconstruction of a three-componentefflux pump, Kaouther Ben Ouirane [et al.] . . . . . . . . . . . . . . . . . . . . . 69

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Maxime Louet [et al.]

Étude de l’assemblage des protéines membranaires par simulation de dynamiquemoléculaire à gros grains et méthodes de Forces de Biais Adaptatif., Jean-PierreDuneau [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Tinker-HP : Dynamique moléculaire polarisable haute performance, ChristopheNarth [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Visualisation et prédiction structurale 72

Illustrative Molecular Visualizations, Tobias Isenberg . . . . . . . . . . . . . . . . 73

Modélisation de novo de structures protéiques par contacts évolutifs, Fabrice Al-lain [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Integrating the Solvent Accessible Surface Distance with cross-links-based model-ing methods improves the conformational sampling of protein assemblies, MathiasFerber [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

A Detailed Data-Driven Protein-Protein Interaction Potential Accelerated By Po-lar Fourier Correlation, Emilie Neveu [et al.] . . . . . . . . . . . . . . . . . . . . . 77

Fragment-based modeling of ssRNA-protein complexes, Isaure Chauvot De Beauch-ene [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

ORION : improving remote homology detection using a structural alphabet, Yas-sine Ghouzam [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems, Nad-hir Abdellatif [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Integration of Chemical and Biological (’omics’) Data for the Prediction of CancerCell-Line Sensitivity, Isidro Cortes [et al.] . . . . . . . . . . . . . . . . . . . . . . 85

Session poster GT Enzymes/GGMM 87

Les thioltransférases : production et élimination de sulfure d’hydrogène ?, Jean-Christophe Lec [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Rôle fonctionnel d’une cavité interne hydrophobe dans l’urate oxydase, NathalieColloc’h [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Benzene-induced leukemogenesis: Irreversible inhibition of PTPN2, a tumor sup-pressor phosphatase involved in leukemia by the hematotoxic metabolite benzo-quinone, Romain Duval [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

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Cellular redox signaling by thiol peroxidase : Mechanisms responsible for thespecificity of the redox relay H2O2/Orp1/Yap1 in S. cerevisiae, Antoine Bersweiler[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Molecular basis of the alteration of brain glycogen metabolism by dithiocarbamatepesticides: impairment of brain glycogen phosphorylase, Cécile Mathieu [et al.] . 93

Réactivité de la carbon monoxide dehydrogenase à nickel avec l’oxygène, MeriemMerrouch [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Regulation of thiol peroxidases in peroxide-dependent redox signaling: Thiore-doxin vs. glutathione in reduction of hyperoxidized 2-Cys peroxiredoxin by Sul-firedoxin, Samia Boukhenouna [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . 95

Computer-aided engineering of a transglycosylase for the glucosylation of an un-natural disaccharide of relevance for bacterial antigen synthesis, Alizée Verges [etal.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Computational Enzyme Design through deterministic search and counting meth-ods, Clément Viricel [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Towards the development of an in silico tools for kinetics prediction, AbdennourBraka [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

In silico experiments on biomolecules through the Static Modes: An efficient ap-proach to predict and design function, Marie Brut . . . . . . . . . . . . . . . . . . 102

Proteus: dessin de protéine en backbone flexible, Karen Druart [et al.] . . . . . . 103

Molecular dynamics with excited normal modes reveals the role of the activationloop of Cyclin-Dependent Kinases in their open/closed conformational equilib-rium, Nicolas Floquet [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Design raisonné de procédés enzymatiques pour la dégradation améliorée de biopolymères, MarcGueroult [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Ligand-Based QSAR studies on some Cis-Stilbenes as Cyclooxygenase Inhibitor, SafiaKellou-Taïri [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

FOCUSED LIGAND LIBRARIES FOR COVALENT DOCKING ON SERINEACTIVE PROTEINS, Virginie Martiny [et al.] . . . . . . . . . . . . . . . . . . . 107

Inhibition sélective des ADN glycosylases : simulation par dynamique moléculaireet docking pour la recherche et la conception d’inhibiteurs, Charlotte Rieux [et al.] 109

Spectroscopic Fingerprints of the Structural Fluctuations of a Single Amino Acid, Fa-tima Barakat [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

18

-mères, Marc Guéroult [et al.]

Unravelling the structure/activity relationship between collagen derived peptidesand the v3 integrin, Eléonore Lambert [et al.] . . . . . . . . . . . . . . . . . . . . 112

Mécanisme d’activation du récepteur T1R2-T1R3 impliqué dans la perception dugoût sucré., Jean-Baptiste Cheron [et al.] . . . . . . . . . . . . . . . . . . . . . . . 113

Caractérisation du paysage énergétique de biomolécules flexibles avec un couplaged’algorithmes stochastiques, Juan Cortes . . . . . . . . . . . . . . . . . . . . . . . 114

Modeling methods for protein structure prediction, mechanistic and docking stud-ies, Serge Crouzy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Mitochondrial membrane fusion: computational modelling of mitofusins, Dario DeVecchis [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

UnityMol: Simulation et Visualisation Interactive à des fins d’Enseignement et deRecherche, Sébastien Doutreligne [et al.] . . . . . . . . . . . . . . . . . . . . . . . 118

Mécanismes d’activation des RCPG impliqués dans les sens chimiques, SébastienFiorucci [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

SA-conf : un outil d’analyse et de comparaison de la variabilité de séquences etde structures d’un ensemble de conformères d’une protéine, Leslie Regad [et al.] . 121

Exploration of Large Amplitude Motions of GPCRs and their complexes at themolecular level, Nicolas Floquet [et al.] . . . . . . . . . . . . . . . . . . . . . . . . 123

HiRE-RNA: un modèle gros grain pour la simulation d’ARN et d’ADN, CédricGageat [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

PEPSI-SAXS - A very fast adaptive scheme for computation of small-angle X-rayscattering profiles via spherical harmonics expansions, Sergei Grudinin [et al.] . . 126

Conformational changes in themophilic and mesophilic enymes, Marina Katava[et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Etude de la variabilité structurale et de l’interaction du peptide NFL-TBS.40-63avec la tubuline., Yoann Laurin [et al.] . . . . . . . . . . . . . . . . . . . . . . . . 129

Molecular basis of Olfactory Receptors: in silico modeling and in vitro valida-tion., Gwenaëlle Andre-Leroux . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Molecular Modeling CoMFA and docking of a Novel Series of Potent 7-AzaindoleBased Tri-Heterocyclic Compounds as CDK2/Cyclin E Inhibitors, Christine B.Baltus [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

19

Conformational changes in thermophilic and mesophilic enzymes, Marina Katava

Study of TIMP-1/LRP-1 interaction in neurons: a new approach based on proteindynamic, Laurie Verzeaux [et al.]

Study of large-scale conformational transitions in c-Abl at the free energy levelvia a structure-based model, Ilaria Mereu . . . . . . . . . . . . . . . . . . . . . . 133

Cartographie des interactions atomiques dans le filament de dystrophine, Do-minique Mias-Lucquin [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Développement d’une stratégie multi-échelle combinant des données expérimen-tales et in silico pour la reconstruction de complexes impliquant la dystrophine., Anne-Elisabeth Molza [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Analysis of protein structure and dynamics to better understand their functionalmechanisms, Damien Monet [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Conformational properties and transport of glucose by GLUT1 using in silicoapproaches, Matthieu Ng Fuk Chong [et al.] . . . . . . . . . . . . . . . . . . . . . 138

As-Rigid-As-Possible interpolation for structural biology, Minh Khoa Nguyen [etal.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

What wins in determining protein regular secondary structures? The polar/nonpolar binary pattern or the intrinsic amino acid composition? A statistical analysisusing the HCA approach., Joseph Rebehmed [et al.] . . . . . . . . . . . . . . . . 141

Molecular features switching off the melatonin-induced activity from MT1 toGPR50 receptors, Nicolas Renault [et al.] . . . . . . . . . . . . . . . . . . . . . . 143

Coarse-Graining Parameterization of Specific Cardiac Membrane Lipids, EditheSelwa [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Prediction of peptide conformational stability, Marie Amandine Laurent [et al.] . 145

Dynamique d’assemblage de la capside des norovirus, Thibault Tubiana [et al.] . 146

Identification and automated weighting of ensemble-averaged NOE restraints, SilkeWieninger [et al.] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Liste des participants 149

Liste des auteurs 154

Session poster GT Enzymes 160

Session poster GGMM 161

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Liste des auteurs 149

Liste des participants 153

Structures et mécanismes enzymatiques

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Structure and function of bacterial FIC toxins

Jacqueline Cherfils ∗† 1

1 Ecole Normale Superieure (ENS de Cachan) – CNRS – Laboratoire de Biologie et PharmacologieAppliquée- CNRS - ENS de Cachan, France

Five years ago, the large family of bacterial FIC proteins was still uncharted territory. Ithas now become clear that many FIC domain-containing proteins are toxins that add diversepost-translational modifications to target proteins. I will describe our recent structural andbiochemical studies of the FIC protein AnkX, a secreted effector from Legionella pneumophila(the bacteria that causes the legionnaire’s disease, a severe pneumonia). AnkX takes commandof membrane traffic - the “cellular postal service” that shuttles biomolecules around the cell andorganizes the structure of organelles - by adding a phosphocholine moiety to small GTPases ofthe Rab family, which are chief organizers of membrane traffic in the host cell. These studieshighlight how FIC proteins exploit similar catalytic mechanisms to regulate a variety of processesusing a broad range of substrates.Campanacci V., Mukherjee S., Roy C.R , Cherfils J. , EMBO Journal 2013Roy C.R. and Cherfils J., Nature Reviews Microbiology (to be published)

∗Intervenant†Auteur correspondant: [email protected]

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Structures et mécanismes enzymatiques CONFERENCE PLENIERE

mailto:[email protected]

De quoi la NO-Synthase est elle le nom ?

Jérôme Santolini ∗ 1

1 CEA - DSV - SB2SM - LSOD (LSOD) – Centre national de la recherche scientifique - CNRS (France),CEA, Université Paris Sud - Paris XI – CEA-Saclay 91191 gif sur Yvette cedex, France

Nitric Oxide Synthases (NOS), the exclusive source of NO in mammals, are at the core ofa large number of signaling processes crucial in blood pressure regulation, neural communica-tion, cell cycle... NOSs are also the source of an endogenous oxidative stress and are increasinglyassociated to numerous pathological conditions, including cardiovascular, inflammatory and neu-rodegenerative diseases. The improvement of the gene sequencing techniques has recently led tothe discovery of numerous NOSs throughout the living kingdom. The first investigations haverevealed a strong homology with mammalian NOSs but an extremely broad range of biologicalfunctions: from pathogenicity to detoxification, through metabolism and resilience.The current paradigm in the NOS field, namely NOS-NO-Signalling, has become insufficient toaccount for the multiple pathophysiological roles of mammalian NOSs and for the structural andbiological biodiversity of NOSs throughout the living kingdom. There is currently no under-standing of the discrepancy between the strong structural identity of NOSs and their implicationin many different if not opposite biochemical processes. We have initiated an integrative ap-proach that aims at establishing a relationship between the structural properties of NOSs andtheir biological functioning. This project combines Bioinformatics, Enzymology, Biophysics andMicrobiology in order to address the structural, functional and biological diversity of this newfamily of enzymes.

We will present our first results on

- Mammalian NOSs : we showed the existence of two distinct catalytic cycles, alternativelyleading to NO or to harmful nitrogen oxides. The partition between both cycles is specific toeach mammalian NOS and depends on the physiological environment, which might explain thedifferent contributions of NOSs to physiological and pathological processes.

- Bacterial NOS-LPs. We showed that bacterial NOSs had the ability to catalyse oxygen activa-tion but displayed specific electronic and structural properties. This might explain their unfitnessto produce and release NO, which question their assignment as genuine NO synthases.

- Plant NOSs. In the context of the non-existence of plant NOSs, we started characterizingNOSs from a green algae (otNOS) and cyanobacteria (spNOS). Our first results suggest that ot-NOS is a super NOS but raises questions about the actual biochemistry and function of spNOS.By developing an innovative and interdisciplinary project, our final goal is to generate a reliableStructure-Function-Environment Relationship that could predict the chemical and biological ac-tivity of any new NOS from its genomic sequence

∗Intervenant

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Structures et mécanismes enzymatiques COMMUNICATION ORALE

Piston-driven activation mechanism ofVKORC1, the human vitamin K epoxide

reductase

Nolan Chatron ∗ 1, Florent Langenfeld 1, Etienne Benoit 2, Virginie Lattard2, Luba Tchertanov 1

1 Centre de Mathématiques et de Leurs Applications - ENS Cachan (CMLA) – École normalesupérieure [ENS] - Cachan, École normale supérieure (ENS) - Cachan – ENS Cachan CNRS, PRES

UniverSud, 61 Avenue du President Wilson 94230 Cachan, France2 VetAgro Sup [Lyon] – Campus Vétérinaire – Université de Lyon, 1 avenue Bourgelat, 69280 Marcy

l’Etoile, France

Blood coagulation is a crucial physiological process, vital for many living organisms. VitaminK epoxide reductase complex subunit 1 (VKORC1) plays a key role in this process, throughthe catalysis of vitamin K recycling - from its inactive epoxide form to the biologically activehydroquinone form [1]. VKORC1, an endoplasmic reticulum membrane protein, is the primarytarget for vitamin K antagonists (VKAs) [2]. Appearance of VKORC1 mutations promotespatient’s resistance to VKA drugs and/or alteration of VKORC1 activity. Our aim is to clarifythe VKORC1 catalytic mechanisms and the resistance mechanisms to VKAs on the atomicscale. To the best of our knowledge, no structural data is available for the human VKORC1(h-VKORC1 WT ), therefore in our study we used in silico modeling combined with moleculardynamics (MD) simulations.The first step consists to build a structural model of VKORC1 using the recent X-ray data for itsbacterial homolog [3]. The generated model denotes a four-helix structure of VKORC1 (Fig. 1A-B) that is consistent with the experimentally determined enzymatic mechanism regulating theelectron transfer from cysteine residues 43-51 (in blue) to cysteine residues 132-135 (in green).Each pair of transmembrane helices (TM) is linked by either short loops (TM3-TM4 and TM2-TM3) or a long and highly flexible luminal loop (Loop 1/2, linking TM1-TM2) with a smallhorizontal helix (HH) in the middle, overhanging the membrane surface. Molecular Dynamics(MD) simulations (2 x 100 ns) revealed significant conformational changes, evidenced as a two-state transition of the h-VKORC1 WT . The MD simulations data were explored by principalcomponents analysis and cross-correlation analysis, which evidenced highly concerted motions ofprotein residues from Loop 1/2, HH and the four TMs. We observed that the two coiled sub-fragments of Loop 1/2, separated by HH, show anti-correlated movement, which highly correlateswith the HH displacement. The longer the distance between the two coiled sub-fragments, thelarger the extent between HH and TMs (Fig. 1 C-D). When the two coiled sub-fragments ofLoop 1/2 adjoin, the HH moves towards the catalytic site, perturbing its topology by altering theinter-helices space. We denominate these highly concerted movements which involve nearly allstructural subunits of h-VKORC1 WT and its all catalytic residues – C43 (LL), C51 (HH), C132and C135 (TM4) – the ‘piston-driven mechanism’. This purely mechanistic description depictsplausibly a first step of the h-VKORC1 WT activation which precede the enzymatic reaction(s).∗Intervenant

24


Validation of the proposed model and its mechanisms will be performed through the study ofthe VKORC1 mutants, resistant to VKAs, combining in silico, in vitro and in vivo approaches.

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Inhibition sélective des ADN glycosylases :approches structure-fonction

Bertrand Castaing ∗† 1, Stéphane Goffinont 1, Franck Coste 1, FrançoiseCulard 1, Charlotte Rieux 1, Norbert Garnier 1, Stéphane Bourg 1, Pascal

Bonnet 2

1 Centre de Biophysique Moléculaire, UPR4301 CNRS (CBM) – CNRS – rue Charles Sadron, 45071Orléans cedex 02, France

2 Institut de Chimie Organique et Analytique, UMR7311 (ICOA) – CNRS, Université d’Orléans – Pôlede chimie de l’Université d’Orléans, Rue de Chartres, 45067 Orléans cedex 02, France

Les ADN glycosylases sont des enzymes qui initient le système de réparation par excision debase (BER) en reconnaissant et éliminant les bases oxydées (de type radio-induites) dans l’ADNtels que les résidus formamidopyrimidiques (FapyG), la thymine glycol (Tg) (lésions potentielle-ment létales associées à un blocage de la réplication) et la 8-oxoguanine (8-oxoG, lésion mutagèneassociée à une haute fréquence de transversion G:C vers T:A) [1]. Bien que ces enzymes soientimpliquées dans la stabilité génétique des organismes, des travaux récents indiquent que les sys-tèmes de réparation de l’ADN et en particulier les enzymes du système BER constituent descibles pharmacologiques pertinentes pour développer des stratégies anticancéreuses alternativeset combinatoires [2,3]. Ces systèmes de réparation peuvent être en effet à l’origine de mécan-ismes d’échappements aux traitements anticancéreux dont l’un des objectifs est d’endommagerl’ADN de la cellule cancéreuse (exemple : la radiothérapie). Ces observations récentes justifientla recherche d’inhibiteurs de ces enzymes. Sur ce thème général, nous avons initié cette étude surles ADN glycosylases de la surperfamille structurale “ H2TH ”. Une première approche structure-fonction de l’inhibition de cette classe d’enzyme par des nucléobases sera présentée [4].Dalhus, B., Laerdahl, J. K., Backe, P. H. and Bjoras, M. (2009) DNA base repair–recognitionand initiation of catalysis. FEMS microbiology reviews 33, 1044-1078.

Helleday, T. (2011) DNA repair as treatment target. Eur. J. Cancer 47 (Sup3), S333-S335.

Taricani, L., Shanahan, F., Pierce, R.H., Guzi, T.J. and Parry, D. (2010) Phenotypic enhance-ment of thymidylate synthetase pathway inhibitors following ablation of Neil1 DNA glycosy-lase/lyase. Cell cycle 9, 4876-4883.

Biela, A.; Coste, F.; Culard, F.; Guerin, M.; Goffinont, S.; Gasteiger, K.; Cieśla, J.; Winczura,A.; Kazimierczuk, Z.; Gasparutto, D.; Carell, T.; Tudek, B.; Castaing, B. (2014) Zinc fingeroxidation of Fpg/Nei DNA glycosylases by 2-thioxanthine: biochemical and X-ray structuralcharacterization. Nucleic Acids Res. 42(16), 10748-10761.


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Structural characterization of Plasmodiumfalciparum CCT and fragment-based drugdesign approach for targeting phospholipid

biosynthesis pathway

Ewelina Guca ∗† 1, François Hoh 2, Jean-François Guichou 2, Henri Vial 1,Rachel Cerdan 1

1 Dynamique des interactions membranaires normales et pathologiques (DIMNP) – CNRS : UMR5235,Université de Montpellier – BT 24 CC 107 Place Eugène Bataillon 34095 MONTPELLIER CEDEX 5,

France2 Centre de Biochimie Structurale (CBS) – CNRS : UMR5048, Inserm : UMR1054, Université de

Montpellier – 29 rue de Navacelles 34090 MONTPELLIER Cedex, France

Phospholipid synthesis metabolic pathways in Plasmodium falciparum are validated drugtargets for new type of antimalarials. In the de novo Kennedy pathway of phosphatidylcholinebiosynthesis, the second step catalyzed by CTP:phosphocholine cytidylytransferases [2.7.7.15] israte limiting and appears essential for the parasite survival at its blood stage. We are focused onthe structural characterization of this enzyme, the identification of effectors by fragment-baseddrug design approach (FBDD) and then their optimization to eventually design a lead. We solvedthe first reported crystal structure of the catalytic domain of the enzyme target (PfCCT) at reso-lution 2.2 Å, 3 enzyme-substrates complexes (CMP-, phosphocholine- and choline-bound forms)at resolutions 1.9-2 Å and an enzyme-product (CDP-choline) complex structure at resolution2.4 Å that give detailed images of binding pocket, demonstrate conformational changes betweenapo- and holo-protein forms and provide the information about the mechanism of the catalyticreaction at atomic level. The FBDD method uses a library of small molecules (fragments) withmolecular weight that does not exceed 300 Da to explore target binding sites. Primary screeningof fragment library (300 molecules) has been investigated by fluorescence-based thermal shiftassay and Nuclear Magnetic Resonance Saturation Transfer Difference (NMR STD) method isused as a secondary screen to eliminate false positive ligands. This combination of techniquesidentified so far 4 fragment hits that are currently evaluated for their binding modes and affini-ties. Co-crystallization of the protein-fragments complexes is carrying out to provide accurateinformation on the binding modes of the small molecules and topology of interactions will beused to rationally monitor every iterative round of the optimization process allowing subsequentrational design.


27



Understanding the mechanism of UGT74B1from Arabidopsis thaliana: S- or

O-glycosyltransferase ?

Pierre Lafite ∗ 1, Sabine Montaut 2, Stéphanie Marques 1, Sami Marroun 3,Gaël Coadou 3, Hassan Oulyadi 3, Marie Schuler 1, Arnaud Tatibouet 1,

Patrick Rollin 1, Richard Daniellou 1

1 Institut de Chimie Organique et Analytique (ICOA) – CNRS : UMR7311, Université d’Orléans –UFR Sciences Rue de Chartres - BP 6759 45067 ORLEANS CEDEX 2, France

2 Laurentian University – Dpt of Chemistry and BIOchemistry ON P3E 2C6, Canada3 Chimie Organique et Bioorganique : Reactivité et Analyse (COBRA) – CNRS : UMR6014, InstitutNational des Sciences Appliquées [INSA] - Rouen – Rue Tesniere - 76821 - Mont Saint Aignan, France

Carbohydrates play an important part in a vast array of biological processes and thereforeglycomimetics are currently becoming a powerful class of novel therapeutics. Amongst them,thioglycosides, or S -glycosides in which a sulfur atom has replaced the glycosidic oxygen atom,are tolerated by most biological systems. Their major advantages rely on the fact that theyadopt similar conformations than the corresponding O-glycosides and especially that they proveto be less sensitive to acid/base or enzyme-mediated hydrolysis. So far, few examples of naturalS -glycosides were reported in the literature. Until recently, plant glucosinolates were the onlygroup of S -glycosides that were characterized in Nature. These compounds are found in brassicaeand are involved in the plant protective strategy, called “mustard oil bomb”, that occurs aftertheir hydrolysis and subsequent degradation in toxic sulfur-containing compounds (thiocyanates,isothiocyanates, ...).Arabidopsis thaliana S -glycosyltransferase UGT74B1 catalyzes the formation of S-glucosidicbond in the aromatic glucosinolates bionsynthetic pathway, using UDP-glucose as sugar donorand thioxydromixate as thiol acceptor.

In order to assess the potency of this enzyme as a biocatalyst tool to generate unnatural S -glycosides, its versality towards sugar donor and acceptor was tested. This could be efficientlyillustrated by the first chemo-enzymatic synthesis of glucosinolates analogues bearing other sugarmoiety than glucose, using other UDP-sugar donors. Using a range of acceptors, UGT74B1 couldperform S -glycosylation, as well as O-glycosylation, yet with a much lower efficiency. However,the specificity for thiol vs. alcohol was correlated not only to the nature of the atom, but alsoto the chemical properties of the acceptor (eg. pKa).This was experimentaly demontrasted by ‘tuning’ the pKa of a range of alcohol acceptors so thatO-glycosylation rates increased to a level close to S -glycosylation.

∗Intervenant

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Monitoring the transition of apo into holoforms of soluble glucose dehydrogenase bystopped-flow and crystallographic studies to

decipher the mechanism ofreconstitution/activation of this enzyme.

Claire Stines-Chaumeil ∗† 1, François Mavre 2, Brice Kauffmann ∗

3, Benoit Limoges 2, Nicolas Mano 1

1 Centre de recherches Paul Pascal (CRPP) – CNRS : UPR8641 – 115, Av Albert Schweitzer 33600PESSAC, France

2 Laboratoire d’electrochimie moleculaire (LEM) – Université Paris VII - Paris Diderot – UMR CNRS -P7 7591 Université Paris Diderot - Paris 7 Bât . Lavoisier, 15 rue Jean-Antoine de Baïf 7e Etage - case

7107 75205 Paris Cedex 13, France3 Institut Européen de Chimie et Biologie (IECB) – CNRS : UMS3033, Université de Bordeaux

(Bordeaux, France) – 2 rue Robert Escarpit, F-33607 Pessac, France

Soluble glucose dehydrogenase (s-GDH) from Acinetobacter calcoaceticus belongs to the fam-ily of quinoproteins and catalyzes the oxidation of glucose into gluconolactone. This enzyme iswidely use in biotechnology, e.g. for glucose biosensors or enzymatic biofuel cells(1).Here we focus on the mechanism of the reconstitution and activation process of the s-GDH whichis produced under apo form in Escherichia coli. Understanding this process is of interest to im-prove its use in electrochemical devices such as immunosensors. If heterogeneous reconstitutionstudies have been recently published (2), there are no detailed studies of the homogeneous re-constitution of the s-GDH.

To decipher this reconstitution/activation mechanism, we combined stopped-flow experiments,crystallographic studies and simulations. Our data indicates that this mechanism can be ex-plained with a square schema mechanism with two different pathways.

Flexer V. et al., Anal.Chem, 2011, 83 (14), 5721-7Zhang L. et al., Anal. Chem, 2014, 86 (4), 2257-67.


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Mécanisme de la 3-mercaptopyruvatesulfurtransférase : une enzyme impliquée dans

la production de sulfure d’hydrogène.

Jean-Christophe Lec ∗ 1, Sabrina Colin , Séverine Boutserin , HortenseMazon , François Talfournier† , Sandrine Boschi-Muller‡

1 UMR 7365 CNRS-Université de Lorraine (IMoPA) – CNRS : UMR7365, Université de Lorraine – 9Avenue de la Foret de Haye, 54505 Vandoeuvre les Nancy, France

La 3-mercaptopyruvate sulfurtransférase (3-MST) est une enzyme ubiquitaire qui participeà la formation de sulfure d’hydrogène (H2S), un messager cellulaire pour les cellules eucaryotesayant également un effet cytoprotecteur. En effet, H2S est un gaz toxique, qui entraine une inhi-bition de la respiration mitochondriale via son action sur la cytochrome oxydase, mais égalementune molécule de signalisation qui interviendrait au niveau de la neuromodulation dans le cerveauet de la relaxation du muscle lisse dans le système vasculaire. La 3-MST eucaryote est à la foismitochondriale et cytosolique, tandis que les autres systèmes enzymatiques qui produisent H2Ssont exclusivement cytosoliques. La 3-MST est une enzyme à cystéine essentielle composée dedeux domaines rhodanèses qui catalyse le transfert de l’atome de soufre du 3-mercaptopyruvatesur un accepteur de soufre (cyanure ou thiol in vitro), via la formation d’un intermédiaire persul-fure sur sa cystéine catalytique. Dans le cas d’un accepteur à thiol, la décomposition du produitde la réaction conduit à la formation d’H2S. L’étude détaillée du mécanisme catalytique des 3-MST humaine et d’Escherichia coli a été réalisée par des approches cinétiques (état stationnaireet cycle catalytique unique, systèmes enzymatiques couplés), de spectroscopie de fluorescence etde spectrométrie de masse, incluant l’élaboration de stratégies originales afin de pouvoir étudierles différentes étapes du mécanisme. Les résultats montrent que la première étape de transfertde soufre est très rapide, et que l’étape cinétiquement limitante est associée à la libération dupyruvate pour l’enzyme humaine et à la seconde étape de transfert de soufre pour l’enzymebactérienne.

∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant: [email protected]

30




Régulation et efficacité

31

Approches correlatives en analysemolecule-unique

Terence Strick ∗ 1

1 IJM (Institut Jacques Monod) – CNRS : UMR7592, Université de Paris Diderot – 15 rue HeleneBrion 75013 Paris, France

Les mesures sur molecules individuelles permettent d’obsever en temps reel des interactionsmoleculaires discretes et ainsi d’eviter le moyennage intrinseque a une mesure effectuee sur unepopulation de molecules. Ces approches sont particulierement utiles pour l’analyse de systemesmoleculaires multi-composantes et de leurs reactions typiquement multi-etapes. Cependant sid’un cote la manipulation de molecule unique permet de deceler par sa signature ”mecanique”une interaction moleculaire, et donc eventuellement un etat catalytique, elle permet difficilementde connaitre la composition exacte d’un complexe qui pourrait avoir une telle interaction. D’unautre cote la detection par fluorescence molecule-unique permet d’observer la (co)localisationde molecules individuelles, mais renseigne difficilement (en dehors de cas specfiques tels leTIRF) sur l’etat catalytique du systeme observe. La combinaison des deux approches per-met ainsi de simultanement detecter en temps reel la composition et l’etat catalytique de sys-temes moleculaires complexes. Un tel systeme permet de reconstituer la voie de reparationdite transcriptionellement-couplee d’Escherichia coli comprenant six proteines distinctes, et ainsicommencer a etudier les liens entre composition et catalyse dans les processus de reparationd’ADN.

∗Intervenant

32

Régulation et efficacité CONFERENCE PLENIERE

Régulation des IMPDHs bactériennes autravers des modules CBS

Hélène Munier-Lehmann ∗ 1

1 Institut Pasteur, Unité de Chimie et Biocatalyse, CNRS UMR3523 – Institut Pasteur de Paris, CNRSUMR3523 – France

L’inosine 5’-monophosphate déshydrogénase (IMPDH) est une enzyme essentielle et ubiqui-taire intervenant dans la voie de biosynthèse de novo du métabolisme des purines et responsablede la conversion de l’IMP en XMP (1,2). Les IMPDHs humaines ont été largement documentéeset sont la cible de médicaments utilisées en clinique.Les IMPDHs sont décrites sous forme tétramérique : chaque monomère comprend un domainecentral contenant le site actif, et un domaine Bateman constitué de deux modules cystathionine-ß-synthase (CBS).

Par une approche multidisciplinaire, nous avons démontré que l’IMPDH de Pseudomonas aerug-inosa est une enzyme allostérique et octamérique (3), deux propriétés qui n’avaient jamais étédécrites jusqu’à présent. Ces travaux ont été étendus à sept autres IMPDHs bactériennes perme-ttant de définir deux classes (4). Les IMPDHs de la classe 1 (incluant l’IMPDH de P. aeruginosa)présentent une coopérativité vis-à-vis de l’IMP avec une activation par le MgATP et sont sousforme octamérique quelles que soient les conditions testées. Les IMPDHs de la classe 2 ont unecinétique de type michaelienne, comme ce qui a été rapporté dans la littérature sur les IMPDHseucaryotes. Par contre, et de façon surprenante, leur état d’oligomérisation est régulé par leMgATP en passant d’un état tétramérique à un état octamérique. Ce sont les modules CBS quijouent un rôle clé soit dans la régulation de l’activité catalytique, soit dans la modulation de lastructure quaternaire. Ainsi, les modules CBS sont fonctionnels au sein des IMPDHs, et pour-raient être la cible d’inhibiteurs allostériques pour le développement de nouveaux antibiotiques.

Références :

1. Hedstrom, L. (2009) IMP dehydrogenase: structure, mechanism, and inhibition. Chem.Rev. 109, 2903-2928

2. Pankiewicz, K. W., and Goldstein, B. M. (2003) Inosine Monophosphate Dehydrogenase:A Major Therapeutic Target. ACS Symposium Series

3. Labesse, G., Alexandre, T., Vaupre, L., Salard-Arnaud, I., Him, J. L., Raynal, B., Bron,P., and Munier-Lehmann, H. (2013) MgATP Regulates Allostery and Fiber Formation in IM-PDHs. Structure 21, 975-9854. Alexandre, T., Raynal, B., and Munier-Lehmann, H. (2015) Two classes of bacterial IMPDHsaccording to their quaternary structures and catalytic properties PloS one in press

∗Intervenant

33

Régulation et efficacité COMMUNICATION ORALE

Identification of DiscriminantConformation-Dependent Residues of Protein

Kinases: a Proteometric Analysis

Nicolas Bosc ∗† 1, Berthold Wroblowski 2, Samia Aci-Sèche 1, ChristopheMeyer 3, Pascal Bonnet‡ 1


2 Janssen Research Development, a division of Janssen Pharmaceutica N.V. – Turnhoutseweg 30, 2340Beerse, Belgique

3 Centre de Recherche Janssen-Cilag – Johnson Johnson – Campus de Maigremont - CS 10615, 27106Val de Reuil CEDEX, France

Imatinib, a Bcr-Abl tyrosine-kinase inhibitor, was the first protein kinase inhibitor that pro-gressed to the market in 2002. Since then, pharmaceutical companies strongly pursued researchefforts to discover novel kinase inhibitors. Today, seven kinase inhibitors binding to the inactiveconformation of specific protein kinases have been approved by the FDA. Although imatinib isa Type-II inhibitor and four more Type-II inhibitors were approved in 2012, they still representless than a third of approved protein kinase inhibitor class. Since part of their chemical structurebinds outside of the ATP pocket, Type-II inhibitors could therefore increase kinase selectivityin comparison to the classical Type-I inhibitors binding the ATP site. Hence, the identificationof key residues responsible for the binding of Type-II inhibitors could help understanding theprotein conformational changes (active to inactive state) as well as designing better selectivekinase inhibitors. The novel proteometric approach developed in the SB&C group, combinesresidue descriptors of protein kinase sequences and biological activities of several Type-II kinaseinhibitors. Using Partial Least Squares (PLS) regression, we determined 29 key residues respon-sible for high biological activities of Type-II inhibitors. Among these residues, the gatekeeperresidue was found to be the most relevant suggesting its essential role in protein kinase conforma-tional changes. Using the newly developed proteometric model, we also predicted the propensityof each protein kinase to trap Type-II inhibitors. These results were further validated using twoexternal datasets of protein-ligand activity pairs. Other residues present in the kinase domain,and more specifically in the binding site, have been highlighted by this approach, but their rolein biological mechanisms is still under investigation.


34




La Carbon Monoxide Dehydrogenase (CODH)de Desulfovibrio vulgaris.

Jessica Hadj-Said ∗† 1, Maria-Eirini Pandélia 2, Christophe Léger‡ 1,Vincent Fourmont§ 1, Sébastien Dementin¶ 1

1 Bioénergétique et ingénierie des protéines (BIP) – CNRS : UPR9036 – 31 Chemin Joseph Aiguier13402 MARSEILLE CEDEX 20, France

2 Department of Chemistry, The Pennsylvania State University – 224 Chemistry Building UniversityPark, PA 16802 USA, États-Unis

Les Carbon Monoxide Dehydrogenases (CODH) à nickel catalysent l’oxydation réversible duCO en CO2. Dans certains bactéries anaérobies, elles favorisent l’établissement d’une force pro-ton motrice pour la synthèse d’ATP. Elles sont aussi impliquées dans la fixation du CO2 [1]. Cesont des enzymes homodimériques qui contiennent deux sites actifs [Ni-4Fe-4S] et trois centres[4Fe-4S] [2]. Le mécanisme catalytique communément accepté implique la conversion du site actifde l’enzyme d’un état inactif, appelé Cox, en deux états actifs dans des conditions réductrices:Cred1 (Cox réduit par un électron) et Cred2 (Cred1 réduit par deux électrons) [3].

Le génome de Desulfovibrio vulgaris contient un opéron putatif codant pour une CODH (cooS)et une protéine de maturation (cooC) [4]. Par une combinaison d’approches biochimiques etspectroscopiques, nous avons caractérisé deux formes de la CODH produite en absence ou enprésence de CooC, désignées CooS et CooSC, respectivement.

CooS a un faible contenu en nickel et est presque inactive. Aucune signature du site actifn’est détectée par résonance électromagnétique électronique (RPE), ce qui renforce l’idée queCooC est requis pour promouvoir in vivo la construction d’un site actif fonctionnel.

CooSC contient du nickel en quantité substoechiométrique, catalyse l’oxydation réversible duCO en CO2, mais n’est pas totalement active après purification. En effet, son activité n’estmaximale qu’après incubation en présence de nickel exogène dans des conditions réductrices.Une caractérisation par RPE des échantillons de CooSC a cependant montré que le site actif estdans l’état Cred1 et/ou Cred2 aussi bien avant qu’après activation par le nickel.

Nos résultats ont donc permis de mettre en évidence un processus d’activation inattendu dela CODH qui n’implique apparemment pas de changement structural du site actif. Ce mécan-isme d’activation est indétectable par RPE et requiert le nickel exogène dont le rôle reste àdéterminer.

∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant:§Auteur correspondant:¶Auteur correspondant: [email protected]

35

¶Auteur correspondant: [email protected]



mailto:

mailto:


Can, Mehmet and Armstrong, Fraser A. and Ragsdale, Stephen W., Structure, Function, andMechanism of the Nickel Metalloenzymes, CO Dehydrogenase, and Acetyl-CoA Synthase, Chem.Rev., 114, 4149–4174, 2014

Holger Dobbek, Vitali Svetlitchnyi, Lothar Gremer, Robert Huber, and Ortwin Meyer. Crystalstructure of a carbon monoxide dehydrogenase reveals a [Ni-4Fe-5S] cluster. Science, 293(5533):1281–1285, August 2001.

Heo, J., Staples, C. R., Tesler, J., and Ludden, P. W. Rhodospirillum rubrum CO Dehydroge-nase. Part 2. Spectroscopic Investigation and Assignment of Spin-Spin Coupling Signals. J AmChem Soc 121, 11045-11057, 1999

Rajeev, Lara and Hillesland, Kristina L. and Zane, Grant M. and Zhou, Aifen and Joachimiak,Marcin P. and He, Zhili and Zhou, Jizhong and Arkin, Adam P. and Wall, Judy D. and Stahl,David A. Deletion of the Desulfovibrio vulgaris Carbon Monoxide Sensor Invokes Global Changesin Transcription. Journal of Bacteriology, 21(194):5783-5793, Nov 2012

36


Study of the regulatory behavior ofPlasmodium falciparum IMP-specific

5’-nucleotidase (PfISN1)

Loic Carrique ∗ 1, Lionel Ballut 1, Sébastien Violot 1, Nushin Aghajari† 1

1 Institut de biologie et chimie des protéines (IBCP) – Université Claude Bernard - Lyon I (UCBL),CNRS : UMR5086, Bases Moléculaires et Structurales des Systèmes Infectieux, Equipe

Biocristallographie et Biologie Structurale des Cibles Thérapeutiques – 7 Passage du Vercors 69367LYON CEDEX 07, France

Approximately 300 million people worldwide are affected by malaria. Plasmodium falciparuminduces a lethal form of malaria which causes more than 1-2 million deaths worldwide annually,with a large number of victims being children. It has a massive impact on human health inthat it is the world’s second biggest killer after tuberculosis. Despite intense efforts an effectivevaccine is still not available.Proteins involved in the purine salvage pathway are mandatory for regulating the pool of nu-cleotides during the inner red blood cells development of the parasite. Many of these proteins areactive in transient- or stable oligomeric states which are dependent on time and on the osmolyteconcentration present in the cells. One of these is an IMP-specific 5’-nucleotidase (Pf ISN1),for which enzymatic studies have shown that the protein is active in an oligomeric form withan allosteric regulation (Srinivasan & Balaram, 2007). Using different techniques, DynamicLight Scattering (DLS), Size Exclusion Chromatography (SEC) and Multi-Angle Light Scat-tering (SEC-MALS), we were able to conclude that the protein adopts a tetrameric state whenactive, but also an octameric conformation. Screening of crystallization conditions have so far re-sulted in crystals diffracting X-rays to medium resolution. Due to the regulatory behavior of thisenzyme, Small Angle X-ray Scattering studies combined with SEC-MALS are of high importancein order to contribute to the understanding of the influence of the environment on the quaternarystructure formation and thereby on the nucleotidase activity. Results obtained so far from thecombined use of biochemical- and biophysical approaches for the understanding of the reactionmechanism of this important parasite enzyme from the purine salvage pathway will be presented.

References:Bharath Srinivasan and Hemalatha Balaram, ISN1 nucleotidases and HAD superfamily proteinfold: in silico sequence and structure analysis, In Silico Biology 7, 0019 (2007)


37



Cellular redox signaling by thiol peroxidase :Mechanisms responsible for the specificity of

the redox relay H2O2/Orp1/Yap1 in S.cerevisiae

Antoine Berweiler 1, Alexandre Kriznik 1, Guy Branlant 1, SophieRahuel-Clermont ∗† 1

1 IMoPA UMR 7365 CNRS-Université de Lorraine (IMOPA) – CNRS : UMR7365, Université deLorraine – Biopôle, campus Biologie-Santé, 9 Avenue de la Forêt de Haye, CS 50184 54505 -

Vandœuvre-lès-Nancy, France

Thiol-peroxidases, described as antioxidant enzymes, have also been associated with peroxide-dependent cell signaling as sensor and relay of the H2O2-mediated signal (1). One of the bestdocumented examples of such a mechanism is the activation of the transcription factor Yap1, akey regulator of the transcriptional peroxide stress response in Saccharomyces cerevisiae, whichdepends on the formation of intramolecular disulfide bonds catalyzed by the thiol peroxidaseOrp1 (2 ; 3). In this mechanism, it has been proposed that the relay occurs via the oxidationof Orp1 peroxidatic Cys as a sulfenic acid intermediate which reacts with Yap1 to form a mixeddisulfide species (3). In addition, the Ybp1 protein has been identified as an essential partner forthe activation of Yap1 by Orp1 (4). The intrinsic reactivity of Orp1 sulfenic acid species couldpotentially result in competition between Yap1 and other thiols, such as the regeneration Cysof Orp1 or other cellular thiols. This raises the question of the specificity of Yap1 activation byH2O2/Orp1. To address this question, and to elucidate the role of Ybp1 in this mechanism, wehave used an approach based on the kinetic characterization of the two reactions in competition:the peroxydatic cycle of Orp1 and the reaction withYap1, using rapid kinetic stop flow and quenchflow techniques. From the study of the impact of Ybp1 on these kinetics and the characterizationof the protein-protein interactions between the three partners by fluorescence anisotropy andmicrocalorimetry, we propose that Ybp1 and Yap1 recruit Orp1 within a ternary complex, whichrestrains intramolecular disulfide formation within Orp1 and allows the reaction between Orp1sulfenic intermediate and Yap1.


38



Amyloid properties of a conserved domain inthe N-Term domain of the Androgen Receptor- Implications for new therapeutic routes -

Julia Asencio Hernández 1,2, Marc-André Delsuc ∗ 2

1 NMRTEC (NMRTEC) – NMRTEC – Boulevard Sébastien Brandt, Bioparc, Bat. B 67400Illkirch–Graffenstaden, France

2 Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) – CNRS : UMR7104, Inserm :U964, université de Strasbourg – Parc D’Innovation 1 Rue Laurent Fries - BP 10142 67404 ILLKIRCH

CEDEX, France

The prostate cancer is under a strict hormonal androgen control, and develops initially as anandrogen-dependent disease that relies on the Androgen Receptor (AR) for growth and progres-sion. The AR protein, in contrast with other nuclear receptors, has the peculiarity of presenting avery large N-Terminal Domain (NTD) of about 560 residues, which is flexible with a high degreeof intrinsic disorder. The NTD has gain recent interest because of potential new anti-prostatecancer molecules targeting this domain[1].

We have recently shown [2], that a short domain of the NTD, highly conserved across all verte-brate species (fig. 1), is able to readily form amyloid fibrils upon dimerisation by disulfide bridgeformation around Cys240. Unlike most amyloid systems, the fiber formation is reversible, anddepend only onthe redox state of the peptide. Peptides as short as 11-residues as well as longerpeptides, obtained from the AR primary sequence are able to trigger the fibrillation.

This points to the fact that this highly conserved domain could be involved in the regulationof the activity of AR, through a control of the aggregation state. The possibility that amyloidformation is a general mechanism for the regulation of the activity of a protein has already beenproposed[3]. The AR protein is already known to aggregates into amyloid fibers[4]. This aggre-gation property is usually associated to the presence of a poly-glutamine tract (polyQ), knownto be involved in several pathologies[5].

Biophysical experiments, in-vivo experiments, and sequence analysis are presented, and con-fort the link between protein aggregation / redox state and prostate cancer pathology. If thishypothesis proves true, it could form a possible target for prostate cancer therapy, and in generalfor regulating the AR activity. Pre-screening of potential interactive molecules are currently inprogress.

J.K.Myung, C.A.Bañuelos, J.Garcia Fernandez, N.R.Mawji, J.Wang, A.H.Tien, Y.C.Yang, I.Tavakoli,S.Haile, K.Watt, I.J.McEwan, S.Plymate, R.J.Andersen, and M.D.Sadar. An androgen receptorN-terminal domain antagonist for treating prostate cancer. J. Clin. Invest. 123, 2948–2960(2013).∗Intervenant

39


J.Asencio-Hernández, C.Ruhlmann, A.McEwen, P.Eberling, Y.Nominé, J.Céraline, J-P.Starckand M-A.Delsuc (2014) ChemBioChem 15(16):2370-3. Reversible Amyloid Fiber Formation inthe N Terminus of Androgen Receptor.F.Chiti, and C.M.Dobson (2006) Annu. Rev. Biochem. 75, 333–366. Protein misfolding, func-tional amyloid, and human disease.

T.Jochum, M.E. Ritz, C.Schuster, S.F.Funderburk, K.Jehle, K.Schmitz, F.Brinkmann, M.Hirtz,D.Moss, and A.C.B.Cato Toxic and non-toxic aggregates from the SBMA and normal forms ofandrogen receptor have distinct oligomeric structures. Biochim Biophys Acta 1822, 1070–1078(2012).

R.Kumar, H.Atamna, M.N.Zakharov, S.Bhasin, S.H.Khan, and R.Jasuja, Role of the androgenreceptor CAG repeat polymorphism in prostate cancer, and spinal and bulbar muscular atrophy.Life Sciences 88, 565–571 (2011).

40


Ingénierie enzymatique et évolutiondirigée

41

Design and optimization of artificial enzymes:nearer to nature

Donald Hilvert ∗† 1

1 Laboratory of Organic Chemistry – ETH Zürich, Zurich, Suisse

Protein design is a challenging problem. We do not fully understand the rules of proteinfolding, and our knowledge of structure-function relationships in these macromolecules is at bestincomplete. Nature has solved the problem of protein design through the mechanism of Darwinianevolution. From primitive precursors, recursive cycles of mutation, selection and amplification ofmolecules with favorable traits have given rise to all of the many thousands of gene products inevery one of our cells. An analogous process of natural selection can be profitably exploited insilico and in the laboratory on a human time scale to create, characterize and optimize artificialcatalysts for tasks unimagined by Nature. Recent progress in combining computational andevolutionary approaches for enzyme design will be discussed, together with insights into enzymefunction gained from studies of the engineered catalysts.


42

Ingénierie enzymatique et évolution dirigée CONFERENCE PLENIERE


Design computationnel de protéines avec unefonction d’énergie MMGBSA

Thomas Gaillard ∗† 1, Nicolas Panel 1, David Mignon 1, Thomas Simonson 1

1 Laboratoire de Biochimie (BIOC) – CNRS : UMR7654, Polytechnique - X – Laboratoire de BiochimieEcole Polytechnique 91128 Palaiseau, France

Le design de protéines a pour but la conception de nouvelles protéines ou la modification deprotéines existantes pour atteindre une fonction donnée. Les approches computationnelles sontune aide précieuse pour le design de protéines, en aidant à rationaliser les prédictions et guiderles tests expérimentaux. Le design computationnel de protéines (CPD) a stimulé d’importantsefforts méthodologiques et a donné lieu à des succès spectaculaires comme la création d’uneprotéine présentant un nouveau repliement ou encore l’ingénierie de sites actifs d’enzymes. Laprincipale difficulté du CPD réside dans le nombre astronomique de séquences et conformationspossibles, de l’ordre de (20x10)^100 pour une protéine de 100 acides aminés. Des algorithmesd’exploration efficaces sont donc nécessaires. Un autre élément clé du succès du CPD est lafonction d’énergie utilisée pour évaluer et trier les séquences et conformations.

Notre approche du CPD [1-10] se fonde sur un modèle atomique de la structure de la protéine etsur une fonction d’énergie de mécanique moléculaire. Un élément important est le traitement dusolvant, représenté comme un continuum diélectrique à l’aide d’un terme de Generalized-Born,complété par un terme proportionnel à la surface accessible au solvant. Les éléments clés de notreimplémentation sont : 1) le squelette de la protéine est maintenu fixe, 2) l’espace des conforma-tions des chaînes latérales est réduit à une librairie discrète de rotamères, 3) la fonction d’énergieest décomposée en paires d’interactions précalculées. La première étape consiste à calculer unematrice des interactions entre chaque paire de rotamères. Dans l’étape suivante, l’espace desséquences et conformations est exploré à l’aide d’un algorithme d’optimisation. L’évaluationde l’énergie dans cette deuxième étape est rapide grâce au précalcul de la matrice d’énergie.L’implémentation de notre procédure de CPD sera d’abord présentée, puis les applications à laprédiction de conformations de chaînes latérales et au design de séquences entières seront abor-dées.

A. Lopes, A. Alexandrov, C. Bathelt, G. Archontis, T. Simonson, Proteins (2007), 67, 853-867.M. Schmidt am Busch, A. Lopes, N. Amara, C. Bathelt, T. Simonson, BMC Bioinformatics(2008), 9, 148.

M. Schmidt am Busch, A. Lopes, D. Mignon, T. Simonson, J. Comput. Chem. (2008), 29,1092-1102.


43

Ingénierie enzymatique et évolution dirigée COMMUNICATION ORALE


M. Schmidt am Busch, D. Mignon, T. Simonson, Proteins (2009), 77, 139-158.

A. Lopes, M. Schmidt am Busch, T. Simonson J. Comput. Chem. (2010), 31, 1273-1286.

M. Schmidt am Busch, A. Sedano, T. Simonson, PLOS One (2010), 5, e10410.

N. Amara, C. Aubard, P. Plateau, T. Simonson, G. Archontis, Proteins (2011), 79, 3448-3468.

M. Schmidt am Busch, A. Lopes, D. Mignon, T. Gaillard, T. Simonson, in J. Zeng, R. Zhang,H. Treutlein (Ed.), Quantum Simulations of Materials and Biological Systems. Springer Verlag,New York, 2012.

T. Simonson, T. Gaillard, D. Mignon, M. Schmidt am Busch, A. Lopes, N. Amara, S. Poly-dorides, A. Sedano, K. Druart, G. Archontis, J. Comput. Chem. (2013), 34, 2472-2484.

T. Gaillard, T. Simonson, J. Comput. Chem. (2014), 35, 1371-1387.

44


Cost Function Network Optimization: exactalgorithms towards Computational Protein

Design

Seydou Traore ∗ 1, Kyle Roberts 2, David Allouche 3, Bruce Donald 2,Isabelle André 1, Thomas Schiex 3, Sophie Barbe† 1

1 Université de Toulouse; INSA,UPS,INP; LISBP (LISBP) – Institut National des Sciences Appliquées(INSA) - Toulouse, Institut national de la recherche agronomique (INRA) : UMR792, CNRS :

UMR5504 – 135 Avenue de rangueil 31077 Toulouse cedex 04, France2 Department of Biochemistry, Department of Computer Science, Department of Chemistry – Duke

University, Durham, NC, États-Unis3 Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA) – Institut national dela recherche agronomique (INRA) : UR875 – Chemin de Borde Rouge, 31320 Castanet Tolosan, France

Computational protein design (CPD) aims at finding a set of sequences that can accommo-date a given protein scaffold and accomplish some wanted biochemical properties. Other the lastdecade, it has demonstrated is potential to adequately capture fundamental aspects of molecularrecognition and holds great interest for applications ranging from medicine, biotechnology andsynthetic biology and nanotechnologies (see for example [1]).

CPD is a challenging optimization problem. In the simplest form, the CPD problem assumesa fixed protein backbone and, for each type of amino acid considered at a given position, al-lows the side-chains to move only among a set of discrete and low-energy conformations, calledrotamers. Hence, the optimization problem consists in identifying combinations of rotamers atdesignable specified positions such that an objective function is minimized (global minimumenergy conformation or GMEC). This problem has been proven to be NP-hard [2]. The tradi-tional exact approach is the combination of the Dead-End Elimination preprocessing and the A*search algorithm (DEE/A*). However, the DEE/A* framework becomes impractical for com-plex CPD instances, one of the main challenges being the exponential size of the conformationaland sequence protein space that has to be handled. Because of these difficulties, meta-heuristicapproaches including sampling methods (mainly based on the Monte Carlo algorithm) and pop-ulation based approaches (such as the Genetic Algorithm) were privileged. However, only exactapproaches can ensure that discrepancies between CPD predictions and experimental resultscome exclusively from the inadequacies of the biophysical model and not from the optimizationalgorithm. This is an important property in design cycles involving several runs of CPD mod-elling, computational optimization, and experimental testing. In addition, exact approaches maystop before meta-heuristic approaches because they can determine that the optimum is reached.Finally, empirical studies showed that the accuracy of meta-heuristics tends to degrade as theproblem size increases [3].


45



Thence, in our previous works, we showed that a recent combinatorial optimization technique, de-fined in the field of “Cost Function Networks” (or Weighted Constraint satisfaction) can push CPDbeyond the limit of usual tools [4-6]. The performance of the Cost Function Network (CFN) op-timization was compared to several other well-known optimization strategies toward the GMECidentification problem. These methods included 0/1 Linear and Quadratic Programming, 0/1Quadratic Optimization, Weighted Partial MaxSAT, Graphical Model optimization problemsand the commonly used CPD dedicated framework Dead-End Elimination/A* (DEE/A*). TheCFN framework outperformed all these methods both in terms of number of instances solvedbut also in terms of runtime performance.

Following this methodology, we have developed and implemented new CFN-based search strate-gies and heuristics within the OSPREY CPD-dedicated software [7]. The performance of thealgorithms was examined using a benchmark set of 30 CPD instances. These new methods bringimportant speedups compared to the well-known DEE /A* framework for solving numerous CPDproblems. Among the new developments, we have introduced a new Best-First search strategyusing the CFN lower bound as heuristics. This method led to an important speedup comparedto the traditional A* algorithm. A new Side Chain Positioning (SCP)–based upper boundingheuristics have need developed for preprocessing pruning efficiency. This heuristic was extendedto define a new branching scheme which incrementally performs SCP-based upper boundingduring search. The new branching scheme allowed to more efficiently find the GMEC but moreimportantly, it enables to enumerate sub-optimal solutions over a larger energy range.

These results open the door to the exact solving of CPD optimization problems with runtimeperformances that compete against those of heuristics while guaranteeing accuracy. Beyondproviding a design framework and computational tools to facilitate the optimization of highlycombinatorial design cases, our approach is also applicable to design strategies that integratemore flexibility with respect to backbone, and continuous sidechain modelling [8].

Acknowledgments

This work has been funded by the “Agence Nationale de la Recherche”, references ANR 10-BLA-0214 and ANR-12-MONU-0015-03. We thank the Computing Center of Region Midi-Pyrénées(CALMIP, Toulouse, France) and the GenoToul Bioinformatics Platform of INRA-Toulouse forproviding computing resources and support. S. Traoré was supported by a grant from the INRAand the Region Midi-Pyrénées. K.E. Roberts and B.R. Donald supported by NIH Grant 2-R01-GM-78031-5.

References

Khare,S.D. et al. Computational redesign of a mononuclear zinc metalloenzyme for organophos-phate hydrolysis. Nat. Chem. Biol., 8, 294–300. 2012.

Pierce,N.A. and Winfree,E. (2002) Protein Design is NP-hard. Protein Engineering, 15, 779–782.

Voigt,Christopher A. et al. Trading accuracy for speed: A quantitative comparison of searchalgorithms in protein sequence design. Journal of Molecular Biology, 299, 789–803, 2000.

D. Allouche, J. Davies, S. de Givry, G. Katsirelos, T. Schiex, S. Traoré, I. André, S. Barbe, S.Prestwich, B. O’Sullivan. Computational Protein Design as an Optimization Problem. ArtificialIntelligence Journal, March 2014.

46


Traoré S, Allouche D, André I, de Givry S, Katsirelos G, Schiex T and Barbe S. A new frameworkfor computational protein design through Cost function optimization. Bioinformatics, 2013.

Traoré S, Allouche D, André I, de Givry S, Katsirelos G, Barbe S and Schiex T. Computa-tional Protein Design as a Cost Function Network Optimization Problem. CP2012 Proceedings.18th International Conference on Principles and Practice of Constraint Programming, Québec,Canada, Oct, 8-12, 2012– Proceeding.

P. Gainza et al. OSPREY: Protein design with ensembles, flexibility, and provable algorithms.Methods Enzymol. 2013;523:87-107.

Hallen,M.A. et al. Dead-end elimination with perturbations (DEEPer): a provable protein designalgorithm with continuous sidechain and backbone flexibility. Proteins, 81, 18–39. 2013.

47


Computational approach to enzyme design

David Rinaldo ∗ 1

1 Schrödinger GmbH (SCH) – Dynamostraße 13 68165 Mannheim, Allemagne

Enzyme design is an important area of ongoing research with a broad range of applicationsin protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, signif-icant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, wedevelop a computational protocol using an approach that combines molecular dynamics, docking,and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focus isto understand the molecular basis of substrate recognition and catalysis. We apply the methodto a variety of enzymes and substrates and in most cases correctly identify the mutations thatyield the greatest improvements in enzyme activity. Also, we confirm that the computationallypredicted structures reproduce experimental crystal structures, when available. Overall, the pro-tocol developed here yields encouraging results and suggests that computational approaches canaid in the redesign of enzymes with improved catalytic efficiency.

∗Intervenant

48


Les enzymes comme ciblesthérapeutiques

49

Enzymes as target class for approved drugsThe case of secretory phospholipase A2(sPLA2) family of proteins as novel

therapeutic targets

Dominique Douguet ∗† 1

1 Institut de pharmacologie moléculaire et cellulaire – CNRS : UMR7275, Université Nice SophiaAntipolis (UNS) – CNRS-IPMC 660 Route des lucioles 06560 VALBONNE, France

To facilitate drug discovery, there is a need to analyze the current landscape of approveddrugs in terms of 3D chemical structures concomitantly with pharmacodynamics and pharma-cokinetics data. We have addressed this question by constructing the public e-Drug3D databasewhich contains FDA-approved drugs. e-Drug3D provides ready-to-screen electronic collectionsto assist in drug repurposing and fragment-based drug discovery. A short global review will bepresented with a particular focus on enzyme inhibitors. Over the years, secretory phospholipaseA2 (sPLA2) family of proteins has been shown to participate in several pathologies like neu-rodegenerative, inflammatory, cardiovascular, metabolic, infectious, and neoplastic diseases inhumans. However, while the extracellular localization of sPLA2 isoforms makes them feasibletargets for new therapeutics, such drug developments have failed. Here, we will make an overviewof the structure-based design of inhibitors of human sPLA2s.


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Les enzymes comme cibles thérapeutiques CONFERENCE PLENIERE


Développement d’antibactériens dirigés contrela ThyX de Mycobacterium tuberculosis

Kamel Djaout ∗† 1, Marten Vos 1, Hubert F. Becker 1, Hannu Myllykallio 1

1 Laboratoire d’optique et biosciences (LOB) – Polytechnique - X, Inserm : U1182, CNRS : UMR7645 –Route de Saclay 91128 PALAISEAU CEDEX, France

La tuberculose est un est problème de santé publique mondial, avec plus de 1.5 million demorts en 2013 (OMS). L’apparition et la propagation de souches multi et extensivement résis-tantes de Mycobacterium tuberculosis, bactérie causant la tuberculose, met en exergue le besoinurgent de développer nouvelle molécules thérapeutique. ThyX est une cible intéressante dansM. tuberculosis : elle est essentielle à la survie de la bactérie et ne présente pas d’homologiede structure ou de mecanisme avec son équivalent humain, ThyA. De plus, ThyX se retrouvesurexprimée dans les souches de Mycobacterium tuberculosis résistantes aux molécules déjà surle marché. ThyX est une thymidylate synthase flavine-dépendante. Elle catalyse la synthèse denovo de désoxythymidine monophosphate, un précurseur essentiel de l’ADN. Nous avons étudiéle mécanisme réactionnel détaillé de la ThyX de Mycobacterium tuberculsosis et montrons desdifférences majeures de réactivité comparé aux autres ThyX décrites. Nous avons aussi criblépour des inhibiteurs spécifiques de ThyX Mtb. Les molécules issues de ce crible présentent d’unepart un nouveau mode d’inhibition des ThyX, ciblant les sous-sites de fixation d’au moins deuxsubstrats; et d’autre part un pouvoir antibactérien sur Mycobacterium smegmatis et Mycobac-terium tuberculosis, avec des CMI variant de 5 à 20 µg/ml. Nous mettons aujourd’hui en placedes outils computationnels (modèles QSAR et pharmacophore) afin de cribler in silico pour demeilleurs candidats d’inhibition de la ThyX de Mycobacterium tuberculosis.


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Les enzymes comme cibles thérapeutiques COMMUNICATION ORALE


Molecular dynamics study of ligandrecognition by DXR: implications for the

design of new antibiotic compounds

Fanny Krebs ∗ 1, Roland Stote 1, Annick Dejaegere 1

1 IGBMC – CNRS : UMR7104, Inserm : UMRU964, université de Strasbourg – Biocomputing group -Structural Biology Genomics Dept. - IGBMC 1 rue Laurent Fries 67404 Illkirch-Graffenstaden Cedex,

France

Most eubacteria, including pathogenic ones, use the 2-C -methyl-D-erythritol 4-phosphate(MEP) pathway to synthesize the basic units that lead to the production of isoprenoids, whichare important secondary metabolites that have diverse roles in different biological processes.Since the MEP pathway is not present in human, enzymes of this pathway are promising targetsfor the development of new antibiotic drugs. The second enzyme of the MEP pathway is 1-desoxy-D-xylulose 5-phosphate reductoisomerase (DXR), which catalyzes the conversion of the 1-desoxy-D-xylulose 5-phosphate (DXP) via an intra-molecular rearrangement in presence of a metalliccation (Mg2+, Mn2+), followed by a reduction involving the cofactor, nicotinamide adeninedinucleotide phosphate (NADPH). X-Ray structures studies identified large-scale conformationalchanges of active site loops that likely regulate ligand recognition. To investigate the mechanismof ligand binding to the enzyme active site, we used molecular dynamics simulations. Analysisof the simulations focused on the structural and functional impact of important active siteresidues. Understanding the interactions between ligands and the protein target as a function ofconformation can contribute to the development of new therapeutic compounds.

∗Intervenant

52


Discovery of Oligopyridyl scaffold molecules aspotent Mcl-1 inhibitors

Jana Sopkova-De Oliveira Santos ∗ 1, Jade Fogha 1, Anne SophieVoisin-Chiret 1, Ronan Bureau 1, Laurent Poulain 2, Céline Gloaguen 2

1 CERMN FR CNRS INC3M – Université de Caen Basse-Normandie : EA4258 – bd Becquerel 14032Caen, France

2 BioTICLA Unit EA4656, Comprehensive Cancer Center François Baclesse – Université de CaenBasse-Normandie : EA4656 – 14032 Caen, France

B-cell lymphoma 2 (Bcl-2) family proteins are crucial regulators of the intrinsic mitochon-drial pathway of apoptosis and comprise both pro-apoptotic and anti-apoptotic proteins.(a) Forinstance, anti-apoptotic proteins Bcl-xL and Mcl-1 induce cell survival until they are inhibitedby pro-apoptotic proteins (Puma, Noxa, Bim). In ovarian cancer, as in many others, these pro-teins expression is deregulated and play a causative role in chemoresistance. Bcl-xL and Mcl-1anti-apoptotic proteins cooperate to protect tumor cells against apoptosis, and their concomitantinhibition leads to massive apoptosis.(b) Whereas a potent Bcl-xL inhibitor have been discovered(ABT-737 by Abbot Laboratories), Mcl-1 protein inhibition is still not completely resolved. Asinteraction among Bcl-2 family proteins occurs through -helices, our laboratory has developeda new family of compounds able to mimic -helix side chain distribution (abiotic foldamers) us-ing as structural chemical units pyridine(c) and/or phenyl(d). The designed and synthesizedoligopyridines potentially targeting the Mcl-1 hydrophobic pocket were evaluated by their ca-pacity to inhibit Mcl-1 in live cells and to sensitize ovarian carcinoma cells to Bcl-xL-targetingstrategies.(e) The structureactivity relationships were established and we focused our attentionon MR29072, named pyridoclax as a very promising compound inhibiting specifically Mcl-1. ,(f)Its ability to bind directly to Mcl-1 was confirmed by surface plasmon resonance assay.(a) Cory, S.; Adams, J. M. The Bcl2 family: regulators of the cellular life-or-death switch. Nat.Rev. Cancer 2002, 2(9), 647-656.

(b) Brotin, E.; Meryet-Figuière, M.; Simonin, K.; Duval1, R. E.; Villedieu, M.; Leroy-Dudal1, J.;Saison-Behmoaras, E.; Gauduchon, P.; Denoyelle, C.; Poulain, L. Bcl-xL and MCL-1 constitutepertinent targets in ovarian carcinoma and their concomitant inhibition is sufficient to induceapoptosis. Int. J. Cancer2010, 126, 885-895.

(c) Sopková-de Oliveira Santos, J.; Voisin-Chiret, A.-S.; Burzicki, G.; Sebaoun, L.; Sebban,M.; Lohier, J.-F.; Legay, R.; Oulyadi, H.; Bureau, R.; Rault, S. Structural Characterizations ofOligopyridyl Foldamers, -Helix Mimetics. J. Chem. Inf. Model. 2012, 52, 429-439

(d) Perato, S.; Fogha, J.; Sebban, M.; Voisin-Chiret, A.-S.; Sopkova-de Oliveira Santos, J.;Oulyadi, H.; Rault, S. Conformation Control of Abiotic -Helical Foldamers. J. Chem. Inf.Model.2013, 53(10), 2671-2680.∗Intervenant

53


(e) Gloaguen, C. ; Voisin-Chiret, A. S. ; Sopkova-de Oliveira Santos, J. ; Fogha, J. ; Gau-tier, F. ; De Giorgi, M. ; Burzicki, G. ; Perato, S. ; Pétigny-Lechartier, C. ; Simonin-Le Jeune,K. ; Brotin, E. ; Goux, D. ; N’Diaye, M. ; Lambert, B. ; Louis, M. H. ; Ligat,L. ; Lopez, F.; Juin, P. ; Bureau, R. ; Rault, S. ; Poulain, P. J. Med. Chem., 2015, Just Accepted Manuscript.

(f) Poulain L., Voisin-Chiret A. S., Sopkova- de Oliveira Santos J., Bureau R., Burzicki G., DeGiorgi M., Perato S., Fogha J., Rault S., Juin Ph., Gautier F. Mcl-1 modulating compounds forcancer treatment. EP14305309.8. 2014.

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Into the intimacy of an irreversible inhibition:SMD(QM/MM) simulations of the fibroblastgrowth factor receptor-1 (FGFR1) kinase

domain.

Yan Li , François Maurel , Patricia Busca , Guillaume Prestat , LaurenceLegeai-Mallet , Rhuizheng Zhang , Rongjing Hu , Michel Delamar , Florent

Barbault ∗ 1

1 ITODYS (ITODYS) – CNRS : UMR7086, Université Paris Diderot - Paris 7, Université SorbonneParis Cité (USPC) – 15 rue Jean de Baïf 75205, France

The Fibroblast Growth Factor Receptor 1 (FGFR1) binds to Fibroblast Growth Factor (FGF)peptide ligands [1]. This association induces signaling pathways which appears to play criticalroles in the cell development [1]. FGFR1, like the others FGFRs, has been studied in the contextof tumor formation and progression [2]. FGFR1 has been found being amplified in squamous celllung cancer specimens [3], especially of patients who smoke. Therefore, the inhibition of FGFR1mainly aims to provide a therapeutic solution for smoker cancers.Inhibition of the tyrosine kinase domain of FGFR1 is an appealing approach to obstruct the bi-ological role of this receptor. However, inhibitors which compete with ATP generally suffer fromlow selectivity. One way to overcome this drawback is to transform a reversible inhibitor to anirreversible one by adding a reactive moiety. This ”haute couture” strategy requires a meticulousdesign that cannot be performed by serendipity; Computational studies play thus here a pivotalrole.

The compound FIIN (fig. 1) is an irreversible inhibitor of FGFR1 [4] which makes a covalent bondto the Cys486 of its kinase domain through a Michael addition’s. We explored its reactivity withmolecular simulations. Firstly, we performed structural analyses through molecular dynamicssimulations of FGFR1 covalently linked with FIIN (FGFR1-FIIN) and FGFR1 associated withFIIN (FGFR1/FIIN). From this last trajectory we extracted 100 conformations where the sulfurof Cys486 is close to the reactive alkene. To study the reactivity of the FIIN compound, we re-alized QM/MM simulations where the Self-Consistent-Charge Density-Functional Tight-Bindingmethod (SCC-DFTB) was chosen for the QM part [5]. We used the SMD technique [6] witha LCOD reaction pathway for 1ns (fig. 1) and obtained three different mechanisms. Only onemechanism is conceivable and works in two steps. The definitive free energy profile (fig. 1) ex-plains the selectivity of FIIN and provides meaningful information for future drug-developmentof irreversible kinase inhibitors.

Reference: [1] Koziczak M, et al. 2004 Oncogene 23, 3501–8 [2] Powers CJ, et al. 2000 En-docr. Relat. Cancer 7, 165–97 [3] Weiss J, et al. 2010 Sci. Transl. Med. 15:62ra93 [4] Zhou W,∗Intervenant

55


et al. 2010 Chem. Biol. 26, 285-95 [5] Frauenheim T, et al. 1998 Mat. Res. Soc. Symp. Proc.491, 91 [6] Isralewitz B, et al. 1997 Biophys. J. 73, 2972-9

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PatchSearch: a fast method for flexiblerecognition of protein binding sites

Inès Rasolohery ∗† 1, Gautier Moroy 1, Frédéric Guyon 1

1 Molécules Thérapeutiques in silico (MTi) – Université Paris Diderot - Paris 7 – France

Inès Rasolohery, Gautier Moroy and Frédéric Guyon.Specific recognition of a therapeutic molecule by its target protein has raised a lot of issues inthe pharmaceutical field. Indeed, non-specific interactions may lead to significant side effects. Inorder to determine whether or not a ligand is able to interact specifically with its target protein,we have developed a new method called PatchSearch. A patch is a set of atoms belonging to theprotein surface and it can also be a binding site. The aim of our program is to identify similarpatches in different protein surfaces.

PatchSearch is based on a classic strategy of clique detection in a correspondence or productgraph [1]. A clique of the product graph is a group of both geometrical and physicochemicalconsistent links between binding site atoms and protein surface atoms, and consequently pro-viding a possible structural alignment. PatchSearch uses this kind of clique approach adaptingit to protein structures. First, PatchSearch computes all maximum cliques of a reduced sizegraph constructed with a small distance tolerance criterion (in practice, less than 1Å). Then,the best clique is selected depending on its structural similarity score; this clique gives the rigidcore of the query patch. Finally, this clique is enriched with all compatible links from the cliqueneighborhood. The resulting quasi-clique includes therefore both rigid and flexible parts of thequery binding site.

We first assessed PatchSearch ability to recognize protein binding sites specific to a same ligand.We used a dataset proposed by Kahraman and coworkers [2], consisting of 100 protein struc-tures complexed with one of nine ligand types (AMP, ATP, FAD, FMN, Glucose, Heme, NAD,Phosphate or Steroid). We performed a ROC analysis and obtained an AUC value of 0.89 withpatches extracted from this dataset, suggesting that PatchSearch is efficient to identify specificpatches in proteins interacting with a same ligand. We also note that this AUC value is equiv-alent to or greater than the best results obtained by other programs [3] on the same dataset,namely 0.77 to 0.89.

We have evaluated PatchSearch efficiency to find a patch from different conformations of asame protein. For that purpose, we used a benchmark, set up by Gunasekaran and coworkers [4],formed by 98 protein structures in both holo (in complex with ligand) and apo (unbound) forms.When binding sites have undergone some large conformational changes, quasi-cliques approachallows recognition of the whole patches, including flexible parts of the patches.


57



We have also applied PatchSearch on patches interacting with indometacin to mine for similarpatches into the complete set of human protein structures. This non-steroidal anti-inflammatorydrug commonly used to reduce fever and pain is well-known to lead to many side effects.

1. S.B. Seidman: Network structure and minimum degree. Social Networks 5,269-287 (1983).

2. A. Kahraman, R.J. Morris, R.A. Laskowski and J.M. Thornton: Shape Variation in Pro-tein Binding Pockets and their Ligands. J. Mol. Biol. 368, 283-301 (2007).

3. B. Hoffmann, M. Zaslavskiy, J.P. Vert and V. Stoven: A new protein binding pocket similaritymeasure based on comparison of clouds of atoms in 3D: application to ligand prediction. BMCBioinformatics. 11:99 (2010).

4. K. Gunasekaran and R. Nussinov: How Different are Structurally Flexible and Rigid BindingSites Sequence and Structural Features Discriminating Proteins that Do and Do not UndergoConformational Change upon Ligand Binding. J. Mol. Biol. 365, 257-273 (2007).

58


PockDrug-Server: a new web server forpredicting pocket druggability on holo and apo

proteins

Hiba Abi Hussein ∗† 1, Alexandre Borrel , Colette Geneix , Michel Petitjean, Leslie Regad , Anne-Claude Camproux‡

1 Recherche de molécules à visée thérapeutique par approches In Silico (MTI) – Inserm : U973,Université Paris VII - Paris Diderot – Batiment Lamarck 35 rue Hélène Brion 75205 PARIS CEDEX 13,

France

Therapeutical molecules bind to preferred sites of action, which are in the majority of casespockets located within proteins or at their surface. Therefore, estimation and characterizationof pockets is a major issue in drug target discovery. Among the molecules, “drug-like molecules”are small molecules with particular properties as of small size, able to cross the digestive tract.Predicting protein pocket’s ability to bind drug-like molecules with high affinity, i.e., druggability,is a key step of compound clinical progression projects. Currently computational druggabilityprediction models are attached to one unique pocket estimation method despite pocket estimationuncertainties. Here, we present “PockDrug-Server” that predicts pocket druggability, efficient onboth; estimated pockets guided by the ligand proximity (extracted by proximity to a ligandfrom a holo protein structure using several thresholds) and estimated pockets not guided by theligand proximity (based on amino atoms that form the surface of potential binding cavities).PockDrug-Server is based on a statistical model corresponding to a combination of 7 lineardiscriminant analysis model using 9 pocket descriptors to provide a mean druggability. It providesconsistent druggability results using different pocket estimation methods. It is robust withrespect to pocket boundary and estimation uncertainties, thus efficient using apo pockets thatare challenging to estimate. It clearly distinguishes druggable from less druggable pockets usingdifferent estimation methods and outperformed recent druggability models for apo pockets. Itcan be carried out from one or a set of apo/holo proteins. PockDrug-Server is publicly availableat: http://pockdrug.rpbs.univ-paris-diderot.fr.

∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant:

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mailto:

Relations dynamique - fonction

60

Combining molecular dynamics and Markovstate modelling to understand ligand transport

in enzymes

Jochen Blumberger ∗† 1, David De Sancho 2, Po-Hung Wang 3, AdamKubas 1, Robert Best 4

1 University College London - London’s Global University – Gower Street - London, WC1E 6BT,Royaume-Uni

2 University of Cambridge (UK) – The Old Schools, Trinity Lane, Cambridge CB2 1TN, Royaume-Uni3 RIKEN – 2-1 Hirosawa, Wako, Saitama 351-0198, Japon

4 National Institutes of Health (NIH) – 9000 Rockville Pike Bethesda, Maryland 20892 USA, États-Unis

The diffusion of small ligands within enzymes and their binding to enzyme active sites areubiquitous molecular processes in biology. The oxygen we breath diffuses to the catalytic siteof cytochrome c oxidase where it combines with electrons to produce water and cellular energy.Atmospheric CO2 and N2 diffuse to the buried catalytic sites of proteins before being convertedinto precursors of biomass or renewable energy sources. In all these examples a small moleculemust travel from the solvent to the small catalytic site, that is embedded in a large and denselypacked heterogeneous protein matrix. Here we would like to understand how the ligand findsits way to the target. To this end we have recently developed a multiscale molecular simulationapproach, where MD simulation and Markov state modelling are combined to shed light onthe kinetics of ligand diffusion and binding [1]. First applications to wild-type and mutanthydrogenase enzymes[1,2] have given diffusion rates that are in good agreement with experimentalmeasurements [3] validating the Markov state model used. For the proteins studied so far wehave found that the ligands move within multiple hydrophobic cavities or ‘tunnels’ towards acentral cavity from where the ligand makes the final transit to the catalytic site [1,2,4]. Thefunctional role of the protein can be compared to the one of a funnel guiding the small moleculestowards the target. Very recently we have extended our method to calculate sensitivity mapsof the protein that identify residues (‘hot spots’) that are expected to show the greatest affecton ligand diffusion when mutated [5]. Such in-silico predictions could help protein engineers tooptimize or control the kinetics of ligand, substrate or inhibitor diffusion in proteins. [1] P. Wang,R. B. Best, J. Blumberger, J. Am. Chem. Soc. 133, 3548 (2011). [2] P. Wang, J. Blumberger,Proc. Natl. Acad. Sci. USA , 109, 6399 (2012). [3] Leger, C. and coworkers Nat. Chem. Biol,6, 63 (2010). [4] P. Wang, M Bruschi, L. De Gioia, J. Blumberger, J. Am. Chem. Soc. 135,9493 (2013). [5] D. De Sancho, A. Kubas, P. Wang, J. Blumberger, R. B. Best, J. Chem. TheoryComput. Article ASAP, DOI: 10.1021/ct5011455 (2015).


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Relations dynamique - fonction CONFERENCE PLENIERE


Structural dynamics of the retinoid X receptorligand binding domain by accelerated

molecular dynamics

Jérôme Eberhardt ∗ 1, Roland Stote 1, Annick Dejaegere 1

1 Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) – CNRS : UMR7104, Inserm :U964, université de Strasbourg – Parc D’Innovation 1 Rue Laurent Fries - BP 10142 67404 ILLKIRCH

CEDEX, France

Nuclear Receptor proteins are ligand-dependent transcription activators that form one ofthe major signal transduction pathways in metazoans, by contributing to cellular communica-tion networks involved in apoptosis, cell growth and differentiation, homeostasis (Altucci et al.2001). Retinoic acid 9-cis, a metabolite of vitamin A, is a ligand of the retinoid X receptor(RXR) subclass of the nuclear receptor superfamily. This subclass is comprised of three RXR(, and ). RXRs are multi-domains proteins with a central DNA-binding domain (DBD) linkedto C-terminal Ligand-binding domain (LBD). The protein is known to undergo large conforma-tional changes in the course of activation.RXR is also a phosphoprotein and the phosphorylation of Ser260 in the LBD affects the transcrip-tional regulation of RXR dimer complexes. Aberrant phosphorylation of Ser260 is strongly asso-ciated with the development of various cancers, in particular hepatocellular carcinoma (HCC), aprimitive liver cancer (Matsushima-Nishiwaki et al. 2001, Macoritto et al. 2008). Structurally,Ser260 is located near the ligand-binding pocket, in a loop between Helix 1 and 3 of the LBD. Inearlier work, we showed that phosphorylation of the RAR LBD affects structural dynamics in away that can subsequently modulate functionality (Samarut et al. 2011, Chebaro et al. 2013).The specific aim of this project is to understand how phosphorylation of the RXR LBD influencesprotein structure and dynamics by molecular dynamics simulations. However, conformationalsampling by molecular dynamics simulations is, in itself, a challenging task and can often call forthe application of new methods of enhanced sampling. In this work, we explored the ensembleof conformations of ligand bound and apo RXR by applying accelerated molecular dynamics(aMD) (Hamelberg et al. 2004). aMD is a new computational technology that permits moreextensive conformational sampling. In this first application to nuclear receptor proteins, e.g.RXR, extensive validation was required. We developed and applied an aMD protocol to theunphosphorylated RXR receptor, taking advantage of the large amount of experimental infor-mation available for this receptor. Besides providing a means to validate our method, this studyprovided additional interpretation of published experimental data. Our application of this tech-nique yielded very promising results for enhanced conformational sampling of nuclear receptorproteins. We will present results from this work as well as provide future perspectives, whichinvolve the study of phosphorylated forms of RXR as well as implications for the developmentof new therapeutic compounds for the treatment of HCC.

∗Intervenant

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Relations dynamique - fonction COMMUNICATION ORALE

Turning glycoside hydrolases intotransglycosidases: an experimental andtheoretical study of the internal waterdynamics in the Thermus thermophilus

-glycosidase

Benoît David ∗ 1, Charles Tellier 1, Yves-Henri Sanejouand 1

1 université de nantes (ufip) – CNRS : UMR6286 – 2 rue de la houssinière 44322 Nantes cedex 03, France

Mostly known for their ability to hydrolyze glycosidic linkages, numerous glycoside hydro-lases are also able to catalyze reverse hydrolysis (transglycosylation) which can be harnessed forthe synthesis of complex oligosaccharides. Although hydrolysis usually prevails over the reversereaction, transglycosylation propensity has already been increased through mutagenesis and di-rected evolution experiments [1, 2]. However, little is known about the regulation of the balancebetween both activities.A previous experimental study on the Thermus thermophilus (Ttgly) -glycosidase using deu-terium exchange mass spectrometry has shown that amide hydrogens from buried parts of theprotein were able to exchange with the solvent [3]. The discovery via molecular dynamic (MD)simulation of a potential water channel connecting the bulk to the active site along this peptidehas supported a possible role of internal water dynamics on the hydrolytic acivity of Ttgly [3].Using an improved simulation protocol, new MD simulations up to 500 ns have been conductedin order to extensively probe the internal water dynamics in Ttgly. Analysis of the internal watermolecules trajectories allowed to characterized a total of six new potential water channels.

Guided by sequence alignements, mutagenesis experiments conducted on vicinal residues alongthe largest channel allowed the identification of mutations influencing the balance between hy-drolysis and transglycosylation. Within this context, it is tempting to speculate that this waterchannel could be involved in supplying water molecules to the active site and potentially regu-lating the hydrolytic activity of Ttgly.

1. Feng, H.-Y., Drone, J., Hoffmann, L., Tran, V., Tellier, C., Rabiller, C., and Dion, M.(2005). Converting a Glycosidase into a Transglycosidase by Directed Evolution. Journal ofBiological Chemistry 280, 37088–37097.

2. Teze, D., Hendrickx, J., Czjzek, M., Ropartz, D., Sanejouand, Y.-H., Tran, V., Tellier,C., and Dion, M. (2014).Semi-rational approach for converting a GH1 glycosidase into a trans-glycosidase. Protein Engineering Design and Selection 27, 13–19.

∗Intervenant

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3. Teze, D., Hendrickx, J., Dion, M., Tellier, C., Woods, V.L., Tran, V., and Sanejouand,Y.-H. (2013).Conserved Water Molecules in Family 1 Glycosidases: A DXMS and MolecularDynamics Study. Biochemistry 52, 5900–5910.

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Description théorique d’un phénomène de”substrate channeling” dans la biosynthèse des

flavonoïdes.

Julien Diharce ∗ 1, Jérôme Golebiowski 1, Sébastien Fiorucci 1, SergeAntonczak† 1

1 Institut de Chimie de Nice (ICN) – CNRS : UMR7272, Université Nice Sophia Antipolis [UNS],Université Nice Sophia Antipolis (UNS) – Faculté des Sciences Parc Valrose 28 Avenue Valrose 06108

Nice cedex 2, France

Dans tous les systèmes vivants, la concentration d’entités biologiques fonctionnelles (pro-téines, enzymes, peptides...) est très élevée. Dès lors, des structures multienzymatiques transi-toires peuvent se créer et exister suffisamment longtemps pour permettre l’apparition de nou-veaux systèmes fonctionnels appelés métabolons. Dans de telles structures impliquant un en-semble d’enzymes engagées séquentiellement dans un chemin de biosynthèse, le produit d’uneréaction enzymatique est transféré directement dans le site actif de l’enzyme suivante où il devientsubstrat. Ce transfert direct diminue grandement les temps de diffusion lors du processus globalainsi que les transferts énergétiques impliqués lors des phénomènes de désolvatation/solvatation.L’effet catalytique général est ainsi optimisé. Ce phénomène est désigné sous le nom de “ Sub-strate Channeling ”.Il est désormais admis que la création de tels complexes est un élément essentiel des mécanismesde biosynthèse de molécules naturelles[1]. De récents travaux[2] mettent en avant la formationd’un tel complexe supramoléculaire lors de la production de flavonoïdes, molécules naturellesantioxydantes dont font partie les anthocyanes et les proanthocyanidines. Nous avons mis enplace de nombreuses méthodes théoriques pour décrire les interactions à différents niveaux decomplexité. Des méthodes de docking protéine-protéine en résolution gros grain ont permis deproposer des structures multi-enzymatiques alors que des procédures de dynamique moléculaireen résolution tout-atome ont mené à une description fine des interactions entre substrat, cofac-teur et enzyme.

Lors de ce travail, a été décrit le comportement dynamique de structures multienzymatiquesimpliquant trois enzymes en interaction avec une membrane cellulaire. Nous détaillerons plusspécifiquement le phénomène de diffusion du métabolite d’un site actif au suivant et mettrons enavant l’importance de la nature des interactions protéine/protéine sur la stabilité du complexe.L’analyse de la diffusion du métabolite montre clairement qu’il reste constamment en interac-tion avec les structures enzymatiques et que, dans cette configuration, le nombre de moléculesd’eau dans son environnement est fortement réduit comparativement à une solvatation complète.

1. Møller, B.L., Dynamic Metabolons. Science, 2010. 330(6009): p. 1328-1329.2. Crosby, K.C., et al., FEBS Letters, 2011. 585(14): p. 2193-2198.∗Intervenant†Auteur correspondant: [email protected]

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L’allostérie dynamique de la protéine CAPrévélée par les forces inter-atomiques

Maxime Louet ∗ 1

1 Molécules Thérapeutiques In silico (MTi) – Université Paris Diderot - Inserm UMR-S 973 – BâtLamarck A, 35 Rue Hélène Brion 75205 PARIS CEDEX 13, France

La protéine Activatrice des Catabolites (Catabolite Activator Protein – CAP) est un casd’école pour l’allostérie des protéines. L’activation et la liaison à l’ADN de cet homodimèrerequiert la liaison de deux Adénosine Mono-Phosphate cycliques (AMPc) qui se lient de manièreanti-cooperative. Ce facteur de transcription qui joue un grand rôle dans la régulation de nom-breux gènes au sein des procaryotes, tel que l’opéron lactose, a fait l’objet de plusieurs étudesexpérimentales ayant montré une perte notable de son entropie conformationnelle après la liaisondu second ligand. Il a donc été proposé que la cause de cette anti-cooperativité est essentielle-ment entropique et non due à un changement conformationnel. Cependant, les acides-aminésclés initiateurs de la modification de la dynamique globale de CAP sont encore inconnus. Nousprésenterons nos résultats, à l’échelle atomique, concernant la régulation allostérique de la pro-téine CAP, par des méthodes de Dynamique Moléculaire et d’Analyse de Distribution de Force(Force Distribution Analysis – FDA) [1]. Il sera montré que nos simulations reproduisent la perteentropique observée expérimentalement. Cette entropie a été calculée de manière plus précisegrâce à un outil développé au laboratoire se basant sur la covariance des forces et non des coor-données [2]. A l’aide de la FDA nous avons également pu observer un chemin de communicationallostérique au sein de la protéine CAP, d’une part entre les deux sites de fixation des AMPc etd’autre part avec des sites plus distants tels que les domaines de liaison à l’ADN. Nos résultatssuggèrent que l’anti-coopérativité n’est pas uniquement entropique mais également enthalpique,notamment par la rotation de chaînes latérales bloquant le site de fixation du deuxième AMPc.1. Costescu and Gräter. Time-resolved force distribution analysis. BMC Biophysics 2013, 6:52. Hensen, Gräter and Henchman. Macromolecular Entropy Can Be Accurately Computed fromForce. J.Chem.Theory Comput. 2014, 10: 4777-4781

∗Intervenant

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Assessing the crowding of Membrane Proteinsat the Mesoscale

Matthieu Chavent ∗ 1, Anna Duncan 1, Jean Hélie 1, Tyler Reddy 1, MarkSansom 1

1 Department of Biochemistry (Oxford, UK) (SBCB) – South Parks Road, Oxford, OX1 3QU,Royaume-Uni

With recent improvements in both experimental and computational techniques it is nowpossible to gain insight into increasingly complex biomembrane systems. Nevertheless, at thefrontier between these two fields lies a twilight zone which we can refer to as the mesoscale. Thiszone ranges from a few dozen of nanometres to micrometres in size and from microseconds tomillisecond in time. In this zone, proteins may adopt specific spatial organisations within a cellmembrane impacting on a number of biological processes. For example, with our collaborators,we have combined molecular dynamics and microscopy to reveal the presence of “islands” of ~0.5µm dimensions formed by the aggregation of porins in bacterial outer membranes. This phe-nomenon has an essential role in the turnover of proteins at the surface of gram negative bacteria[1]. Nevertheless, despite increasing efforts to cover this mesocale zone, it remains difficult tocompare models and experimental observations. Furthermore, the complex dynamic interactionsbetween lipids and proteins need to be better understood on a larger scale to take into accountpossible emergent phenomena which are not visible for smaller (nanoscale) system sizes. This isparticularly true now with the rise of very large and complex models such as plasma membranes[2,3] or flu virion [4]. Here, we would like to present our different attempts to model and char-acterize large systems to assess the dynamical crowding of membrane proteins and how it hasdriven the development of new visualization programs to improved the understanding of thesedynamical systems [5]. We will also discuss how a better characterization of these systems maybe used to pave the way of simpler mathematical models to create systems of the micrometrescale.1. Rassam, P. et al. Supramolecular assemblies underpin turnover of outer membrane proteinsin bacteria. Nature, accepted.

2. Ingólfsson, H. I. et al. Lipid organization of the plasma membrane. J. Am. Chem. Soc.136, 14554–14559 (2014).

3. Koldsø, H., Shorthouse, D., Hélie, J. & Sansom, M. S. P. Lipid Clustering Correlates withMembrane Curvature as Revealed by Molecular Simulations of Complex Lipid Bilayers. PLoSComput Biol 10, e1003911 (2014).

4. Reddy, T. et al. Nothing to Sneeze At: A Dynamic and Integrative Computational Model of anInfluenza A Virion. Structure (London, England : 1993) (2015). doi:10.1016/j.str.2014.12.0195. Chavent, M. et al. Methodologies for the analysis of instantaneous lipid diffusion in md∗Intervenant

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simulations of large membrane systems. Faraday discussions 169, 455–475 (2014).

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New way to fight against antibiotic resistance:reconstruction of a three-component efflux

pump

Kaouther Ben Ouirane ∗† 1, Isabelle Broutin 2, Catherine Etchebest 1

1 Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB) – UniversitéParis Diderot, Sorbonne Paris Cité, INTS, Inserm : U1134, Université Paris Diderot - Paris 7,

Laboratoire d’excellence GrEx – 6 rue Alexandre Cabanel, 75739 Paris cedex 15, France2 Laboratoire de cristallographie et RMN biologiques (LCRB) – CNRS : UMR8015, Université Paris V -Paris Descartes – Faculté de Pharmacie 4 Avenue de l’Observatoire 75270 PARIS CEDEX 06, France

Efflux pumps are present in Gram-negative bacteria and are a key mechanism to resist toantibiotics through expulsing them to the extracellular medium preventing them to reach theirtarget. Thus, inhibition of this machinery can be an efficient strategy to fight against antibi-otic resistance. Efflux pumps are active as macrocomplexes of three protein partners: MexB, aprotein of the inner membrane, OprM, protein of the outer membrane and MexA, a periplas-mic protein. Although each component has been well characterized, complex assembly remainsmisunderstood. The elucidation of their assembly mechanism, in particular in Pseudomonasaeruginosa, would identify key regions essential for complex function.We propose a new and original strategy to prevent the assembly of the efflux pump: This strat-egy consists in designing new drugs (”complex blockers”) avoiding the protein/protein recognitionand/or rigidifying the different protein partners preventing their required structural adaptabilityfor assembly.

Using normal mode analysis and molecular dynamics simulations (coarse grain and all atoms) wecharacterized the conformational changes as well as the flexibility and dynamics of all partners:MexB and OprM in lipid bilayer environment. The reconstitution of functional complex is anintricate task, due to the particularity of its organization. In fact, efflux pump components adoptasymmetric conformations and function according to complex rotational mechanism. Anotherparticularity is the uncertainty related to the periplasmic protein (MexA) stoechiometry. Dueto the highly flexible nature of those proteins, especially mexA, we showed that rigid dockingstrategy is inadequate for this system. We developed then a new strategy to reconstitute thepump that consisted in using CG simulations to study protein-protein interactions instead ofusing rigid docking.We found that mexA auto-assembles with another molecule forming a stable dimmer for mayhundreds of nanoseconds. Interestingly, our collaborators demonstrated that mexA associatesinto dimers and that it was the functional unit prerequisite for pump assembly. So now we areusing the same original strategy based on successive molecular dynamic simulations for solvingthe problem of assembly between the other efflux pump partners.


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Étude de l’assemblage des protéinesmembranaires par simulation de dynamiquemoléculaire à gros grains et méthodes de

Forces de Biais Adaptatif.

Jean-Pierre Duneau ∗ 1, Marlon Sidore 1, James Sturgis 1

1 Laboratoire d’ingénierie des systèmes macromoléculaires (LISM) – CNRS : UMR7255, Aix-MarseilleUniversité - AMU – 31 Chemin Joseph Aiguier 13402 MARSEILLE CEDEX 20, France

L’assemblage des protéines membranaires est le produit d’interactions complexes impliquantdes contacts hiérarchisés au sein de multiples chaînes polypeptidiques et d’une grande diversitéde lipides. Alors que les méthodes de biologie structurale donnent une vue plus précise des inter-actions mises en jeu au niveau purement protéique, encore très peu de données sont disponiblespour prendre la mesure du rôle de l’environnement lipidique.

Dans le travail présenté, nous avons mis en œuvre des approches de modélisation de dynamiquemoléculaire à gros grains pour étudier la gamme et la portée des perturbations structurales etdynamiques induites par la présence de protéines sur différents lipides. Nos résultats montrent,la structuration d’un annulus lipidique dynamique au sein duquel les plus grands excès de densitécorrespondent à des zones d’ancrage privilégiées mais largement échangeables. Au delà de cettezone, une relaxation plus complexe qu’une simple relaxation élastique se met en œuvre jusqu’àde longues distances (> 40 Å).

Aussi pour rendre compte de l’impact énergétique de ces altérations à longues portées surl’association des protéines, nous avons développé une méthode de simulation utilisant des Forcesde Biais Adaptatif (ABF) suivant des coordonnées qui permettent d’explorer l’espace conforma-tionnel d’association de protéines suivant les orientations relatives de monomères. Nos simula-tions montrent que même à longues distances (80 Å) des biais d’orientation relative existent. Larelation avec les perturbations de densité lipidique doit encore être établie.Ces travaux ont bénéficié d’un accès aux moyens de calcul du CINES au travers de l’allocationde ressources 2014-077044 attribuée par GENCI.

∗Intervenant

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Tinker-HP : Dynamique moléculairepolarisable haute performance

Christophe Narth ∗ 1, Jean-Philip Piquemal 1

1 Laboratoire de chimie théorique (LCT) – CNRS : UMR7616, Université Pierre et Marie Curie(UPMC) - Paris VI – 4 place Jussieu, CC-137, F-75252 Paris, France

La dynamique moléculaire est aujourd’hui un outil puissant au service de la biochimie etpharmacologie, permettant de réaliser bon nombres d’études telle que des analyses conforma-tionnelles, effet de solvatation, énergie libre ou encore simulation d’évènements rares.Cela a été rendu possible par l’accélération des codes de mécanique moléculaire classique, per-mettant ainsi d’atteindre des échelles de temps biologiques.D’autre part, afin de reproduire de manière plus réaliste les interactions intermoléculaires, desméthodes plus raffinées, dites polarisables ou encore de seconde génération, ont été développées,telles que AMOEBA[1] ou SIBFA[2]. Basées sur les multipoles, elles permettent de prendre encompte l’anisotropie des interactions électrostatiques, critique dans le cas de liaisons halogène(“sigma-hole“) ou d’empilement de benzènes (“stacking“). A cela s’ajoute la décomposition destermes d’énergies par la méthode SIBFA, reproduisant ainsi les énergies références (en chimiequantique) dû aux interactions non-liées.Jugés trop souvent comme plus coûteux, ces programmes ont pris du retard par rapport à lalarge utilisation des champs de forces classiques.Nous présentons ici une nouvelle implémentation du potentiel SIBFA au sein de Tinker-HP(open-source) mêlant rigueur méthodologique par la non-additivité de ses termes énergétiquescouplées à une parallèlisation hybride offrant alors la possibilité de réaliser les premières dy-namiques moléculaires SIBFA, ainsi que ses premières applications comme un nouveau modèled’eau.Ponder, Jay W., et al. ”Current status of the AMOEBA polarizable force field.” The journal ofphysical chemistry B 114.8 (2010): 2549-2564Cisneros, G. A., et al. ”Design Of Next Generation Force Fields From AB Initio Computations:Beyond Point Charges Electrostatics.” Multi-scale Quantum Models for Biocatalysis. SpringerNetherlands, 2009. 137-172.

∗Intervenant

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Visualisation et prédiction structurale

72

Illustrative Molecular Visualizations

Tobias Isenberg ∗ 1

1 AVIZ (INRIA Saclay - Ile de France) – INRIA – DIGITEO, Bat. Claude Shannon - Université deParis-Sud, Bâtiment 660, 91190 Gif-sur-Yvette, France

Illustrative visualization is a relatively recent development within the larger research fieldof visualization. Illustrative visualization takes inspiration, in particular, from methods thathave been used in traditional illustration for decades and centuries. Illustrative visualizationtechniques thus make use of concepts such as abstraction and emphasis as well as depiction styleto facilitate the creation of better visualization. In particular in the context of the visualizationof molecular data, several illustrative visualization techniques have been created. I will presenta selection of these, both those created by my own research group and those of colleagues. Forexample, I will discuss an approach for the abstraction of visualizations of molecular structuresin which a continuous abstraction space between structural abstraction, abstraction throughspatial perception, and abstraction by means of ”illustrativeness” is created. Users can navigatethis space to adjust visualization to their specific needs. Other techniques, for example, usedifferent molecular surface representations at different scale levels based on current view of thedata and support a seamless transition between them. With the talk I thus hope to introduce theaudience to new visualization approaches and hope to begin a discussion on potential applicationfields of this work for specific scientific tasks and questions.

∗Intervenant

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Visualisation et prédiction structurale CONFERENCE PLENIERE

Modélisation de novo de structures protéiquespar contacts évolutifs

Fabrice Allain ∗† 1, Benjamin Bardiaux 1, Mickael Nilges 1

1 Institut Pasteur - CNRS UMR 3528 – Institut Pasteur de Paris, Centre National de la RechercheScientifique - CNRS – Département de Biologie Structurale et Chimie 25,28 rue du Docteur Roux,

75015 PARIS, France

Malgré les progrès constants des techniques de biologie structurale, la différence entre le nom-bre de structures et de séquences protéiques disponibles ne cesse de croître depuis l’arrivée destechnologies de séquençage à haut débit. Les méthodes de prédiction structurale ont prouvéesces dernières années leur efficacité pour combler ce fossé(1,2). La croissance des informationsgénomiques a également rouvert la voie des techniques modélisant les protéines à partir desvariations des séquences homologues. La conservation d’une fonction au cours de l’évolutioncontraint en effet les résidus en contact dans la structure à suivre la même trajectoire évolutive.Le but de ces méthodes est donc de détecter des paires de résidus covariants dans les alignementsdes séquences homologues d’une protéine cible et déterminer à terme l’architecture tridimension-nelle via des méthodes de modélisation. De récentes approches(3,4) ont ainsi démontrées qu’ilétait possible de prédire les contacts natifs mais aussi de déduire des conformations relativementproche de celle de référence à partir de ces informations.Malheureusement, ces méthodes présentent encore plusieurs limitations dont la détection descontacts faux positifs. Ces difficultés présentent des similitudes avec la résolution de structureen RMN (Résonnance Magnétique Nucléaire). La spectroscopie par RMN permet de détecter descouples de noyaux proches dans l’espace pour déterminer les conformations. Le logiciel ARIA(Ambiguous Restraints for Iterative Assignment)(5,6). intègre la détection des faux positifs util-isant en particulier le concept de contraintes de distances ambiguës et un processus itératif. Dansce contexte, nous utilisons la méthodologie d’ARIA à partir des contacts évolutifs pour prédiredes structures de protéine.

Les premiers tests réalisés sur un jeu de données type avec le protocole d’ARIA montrent desrésultats encourageant. Cependant, l’apparition de structures en miroirs topologiques nécessiteraune augmentation du nombre de contacts évolutifs utilisés pour obtenir des structures plus juste.

1. Kryshtafovych, A., Fidelis, K. and Moult, J. (2014) Proteins, 82: 164–174. doi: 10.1002/prot.24448

2.Tai, C.-H., Bai, H., Taylor, T. J. and Lee, B. (2014) Proteins, 82: 57–83. doi: 10.1002/prot.24470

3. Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander,C. (2011) PloS one, 6(12), e28766.


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Visualisation et prédiction structurale COMMUNICATION ORALE


4. Michel, M., Hayat, S., Skwark, M. J., Sander, C., Marks, D. S., & Elofsson, A. (2014)Bioinformatics, 30 (17), i482-i488.

5. Rieping, W., Habeck, M., Bardiaux, B., Bernard, A., Malliavin, T. E., & Nilges, M. (2007)Bioinformatics, 23 (3), 381-382.6. Bardiaux, B., Malliavin, T., & Nilges, M. (2012). In Protein NMR Techniques (pp. 453-483)Humana Press.

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Integrating the Solvent Accessible SurfaceDistance with cross-links-based modelingmethods improves the conformational

sampling of protein assemblies

Mathias Ferber ∗ , Guillaume Bouvier , Michael Nilges 1

1 Unite de Bioinformatique Structurale , CNRS UMR 3825 and Institut Pasteur (IP, CNRS UMR 3825)– Institut Pasteur de Paris – 28 rue du docteur Roux 75724 Paris, France

Cross-linking mass spectrometry (XL-MS) is increasingly used for structural modeling ofmulti-subunit protein complexes. Here we use the log-harmonic potential as a formulation ofspatial restraints that realistically reflects the experimentally observed cross-linking distances insolution. In combination with a Bayesian modeling framework that iteratively adjusts weightsduring optimization, conflicting restraints and conformational heterogeneity are appropriatelydealt with. In addition, we developed a new geometric approach to quickly estimate the SolventAccessible Surface Distance (SASD) between two residues in the context of protein assemblies.SASD allows to mimic the behavior of cross-linkers by bridging two atoms without penetratingthe protein. We demonstrated on previously published systems that our computation methodefficiently eliminates false positives while remaining compatible with experimental cross-linkingdata. At each step of the conformational sampling, SASD is computed to estimate data violationand enhance the Bayesian restraints re-weighting scheme. We obtain faster convergence towardsmeaningful conformations.

∗Intervenant

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A Detailed Data-Driven Protein-ProteinInteraction Potential Accelerated By Polar

Fourier Correlation

Emilie Neveu ∗† 1, Sergei Grudinin 1, Dave Ritchie 2, Petr Popov 1

1 NANO-D (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann) – Institutpolytechnique de Grenoble (Grenoble INP), Université Joseph Fourier - Grenoble I, CNRS : UMR5224,Laboratoire Jean Kuntzmann, INRIA – Inria GIANT DRT/LETI/DACLE Batiment 51C - Minatec

Campus 17 rue des Martyrs 38054 Grenoble Cedex, France2 CAPSID (INRIA Nancy - Grand Est / LORIA) – INRIA, CNRS : UMR7503, Université de Lorraine

– France

In structural biology, medicine, and drug design, to find proteins that can act as inhibitorsfor viruses or bacteria, one has to know the structure of the complex to be formed. The Pro-tein Data Bank [1] can help but only a small fraction of its proteins are complexes. Withoutgood prior knowledge of the binding sites, computational prediction methods become necessary.Generally, existing methods first explore the six degrees of freedom search space of rigid bodymotions to locate position of one protein with respect to another one, typically using a verysimple energy function. Then, they locally enhance the search space (adding flexibility) whileusing a more precise energy function. However, important solutions may be missed right at thefirst step. Our research focuses on improving the first stage predictions. We believe it will alsoimprove the subsequent refinement searches.We present an algorithm that uses a more precise energy while exploring the whole search spaceof rigid-body motions. It combines a very fast FFT-accelerated exhaustive search with a detaileddata-driven approximation to the binding free energy. More precisely, we are using protein geom-etry represented through 3-D Gauss-Laguerre expansion coefficients, the Spherical Polar Fouriertransform to rapidly compute energy overlap integrals [2], and a convex optimization techniqueto learn the interaction potential. We describe the chemistry and the geometry of a proteinwith 20 3D grids for each protein in a complex and 210 pairwise distance-dependent interactionpotentials that have been learned from the structures of known protein-protein interfaces usinga convex optimization technique. The method runs in 5-10 minutes on a modern laptop for amid-size protein complex.

When tested on a set of 195 bound non-homologues protein hetero-dimer complexes, the methodachieves correct rank-1 solutions in over 50% of cases, and it produces a correct solution withinthe top 10 solutions in 66% of cases. Here, a “correct solution” means one with a ligand root-mean-squared deviation (RMSD) of less than 5 Å from the native solution. We are currentlyadapting the method to predict the structures of protein complexes starting from their unboundstructures.1. H.M. Berman et al. The protein data bank. Acta Cryst. (2002), D58, 899-907.∗Intervenant†Auteur correspondant: [email protected]

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2. D.W. Ritchie, D. Kozakov, and S. Vajda, Accelerating and Focusing Protein-Protein Dock-ing Correlations Using Multi-Dimensional Rotational FFT Generating Functions, Bioinformatics(2008), 24 (17): 1865-1873.

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Fragment-based modeling of ssRNA-proteincomplexes

Isaure Chauvot De Beauchene ∗† 1, Sjoerd De Vries 1, Martin Zacharias‡ 1

1 Technische Universität München (TUM) – James-Franck-Strasse 1, 85748 Garching, Germany,Allemagne

Rationale. Protein-RNA recognition supports a large variety of fundamental cellular func-tions. It is also a crucial therapeutic target : Abnormal or impaired protein-RNA interactionsare responsible for neurodegenerative diseases [1] and cancers [2] ; many viral infections use therecognition of the viral RNA by proteins of the host [3]. Moreover, RNA’s non-toxicity and thehigh specificity of protein-RNA interactions confer an immense potential to the synthesis of RNAaptamers as modulators of proteins [4]. An atomistic prediction and description of protein-RNAcomplexes is essential to the rational conception of modulators of protein-RNA binding and theirfunction [5]. However the experimental resolution of structures of these complexes is extremelydifficult . Many interfaces are still uncharacterized at atomic scale, and require computationalmodeling.State of the art. However, protein-RNA docking is hampered by the high flexibility of RNA.Therefore, compared to protein-protein and protein-ligand docking, protein-RNA docking meth-ods are lagging behind. Particularly challenging are single-stranded RNAs (ssRNAs) and single-stranded loops in RNAs, which encounter nonlinear conformational rearrangement over binding,such as base flipping. Yet these parts carry the specificity of recognition in most cases. The lackof methodology for modeling ssRNAs limits the accuracy of all current protein-RNA dockingmethods [6] [7].

Method. We present an original and effective fragment-based approach to tackle this prob-lem, capable of accurate prediction of the structure of a ssRNA-protein complex, starting fromthe structure of the protein and the sequence of the RNA. The protocol is as follows: (i) TheRNA sequence is cut in overlapping trinucleotides ; (ii) each trinucleotide is represented by asequence-specific ensemble of conformers in an exhaustive fragment library built from all exper-imental protein-RNA complex structures; (iii) each conformer ensemble for the RNA sequenceis docked on the protein; (iv) the compatible poses are selected and assembled in a realisticconformation, on a RMSD criteria of the overlapping nucleotides .

Results. We tested our method on four sequences of ssRNA (6 to 11 nucleotides) bound toproteins containing two RNA-recognition motifs (RRMs). Without any information on specificcontacts nor on the RNA structure, our method can accurately identify the RNA binding siteon the protein within 10 Å precision. In addition, we tested two different regimes with limitedinformation. First, by selecting the best conformers of our library for each fragment prior to∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant: [email protected]

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docking, we could approximate the bound conformation of the RNA with ~1.5 Å RMSD onheavy atoms. Second, by assuming that at least one nucleotide interacts with each RRM in acanonical manner [8], without any knowledge on the RNA structure, the bound conformationwas approximated with a precision of 2 to 3 Å RMSD. For both regimes, alternative incorrectstructures were also generated, and future research will focus on identifying the correct struc-ture (scoring). Still, this precision has never been reached so far by any protein-RNA dockingmethod. If our method proves generalizable further, this will constitute a major methodologicalbreakthrough in RNA-protein docking.

1. T. Vanderweyde, K. Youmans, L. Liu-Yesucevitz, and B. Wolozin, Gerontology, vol. 59,no. 6, pp. 524–533, 2013.

2. M. Derrigo, A. Cestelli, G. Savettieri, and I. Di Liegro, Int. J. Mol. Med., vol. 5, no.2, pp. 111–134, Feb. 2000.

3. A. Ranji and K. Boris-Lawrie, RNA Biol., vol. 7, no. 6, pp. 775–787, Dec. 2010.

4. D. Peer and J. Lieberman, Gene Ther., vol. 18, no. 12, pp. 1127–1133, Dec. 2011.

5. S. Kandil, S. Biondaro, D. Vlachakis, A.-C. Cummins, A. Coluccia, C. Berry, P. Leyssen,J. Neyts, and A. Brancale, Bioorg. Med. Chem. Lett., vol. 19, no. 11, pp. 2935–2937, Jun.2009.

6. S. Fulle and H. Gohlke, J. Mol. Recognit. JMR, vol. 23, no. 2, pp. 220–231, Apr. 2010.

7. N. Morozova, J. Allers, J. Myers, and Y. Shamoo, Bioinforma. Oxf. Engl., vol. 22, no.22, pp. 2746–2752, Nov. 2006.8. C. Maris, C. Dominguez, and F. H.-T. Allain, FEBS J., vol. 272, no. 9, pp. 2118–2131, May2005.

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ORION : improving remote homologydetection using a structural alphabet

Yassine Ghouzam 1, Guillaume Postic , Alexandre De Brevern 1,Jean-Christophe Gelly ∗† 1

1 Biologie Intégrée du Globule Rouge - Equipe Dynamique des Systèmes et des Interactions desMacormolécules Biologiques (DSIMB) – Université Paris VII - Paris Diderot, Institut National de laTransfusion Sanguine (INTS), Inserm : U1134, Laboratoire d’excellence GrEx – 6 rue Alexandre

Cabanel 75739 Paris cedex 15, France

Protein structure prediction is crucial to apprehend protein functions. In this importantfield, the most successful strategies are comparative modeling methods. Given a protein targetsequence with unknown structure, comparative modeling consists to build its structure from anevolutionary related protein with a known structure used as a template. Comparative modelinghave mainly two steps (i) identify a suitable protein template candidate and (ii) building targetstructure from the template.The first stage is the main limiting step. Indeed many potential interesting templates are stillunrecognizable using classical sequence alignment methods due to far evolutionary relationshipbetween template and target proteins. Peculiar approaches are then specifically then developedfor identify such far relationship. Such approaches are based on profile-profile sequence align-ment, obtained from homologs sequences on both target and template. Nonetheless even withsuch strategies many potential interesting templates remain still undetected. Besides, struc-tural features (e.g secondary structures) are three to ten times more conserved than sequence(Illergård et al. 2009). Therefore integration of structural data, mainly as secondary structure,can effectively boost the finding of distant relationship between proteins (Koretke et al. 1999).However, secondary structure states characterize local conformation only in three main states(helix, strand and coil) and are limited for describing the loops, which constitutes 45% of thelocal structure of the proteins.

We have developed ORION, a new generation of profile-profile method combining both amino-acids profile and a new local conformation structural information. The key element of our methodrelies on Protein Blocks (PBs), a structural alphabet of 16 states of five residues long, able tofinely describe local conformations and depict protein structure in term of a sequence constitutedof 16 letters alphabet (de Brevern et al. 2000). Contrary to secondary structure, limited to only3 states, it can finely approximate local conformation and can catch all transitions in proteinstructures. PBs are already been used successfully in many applications such as structural align-ment and local structure prediction (for review: Joseph et al. 2010).

The major steps of ORION are as follows: Starting from a query sequence, a sequence pro-file is computed using PSI-BLAST. This profile is also used for PB prediction with LOCUSTRA∗Intervenant†Auteur correspondant: [email protected]

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software (Zimmermann and Hansmann, 2008). Using dynamic programming, sequence profileand predicted structure of the query are then combined to search in a fold library of pre-computedsequence profiles and assigned PB built from structural alignments of families in HOMSTRADdatabank (Mizuguchi et al, 1998).

We showed that including PBs information increase both specificity and sensitivity in searchinga remote homology template on a large benchmark based on HOMSTRAD databases (Figure1). Moreover ORION systematically outperforms PSI-BLAST (Altschul SF et al, 1997) andHHsearch (Söding, 2005) one of the top leading method and winner of ninth Critical Assessmentof Structure Prediction (CASP) in template-based modeling category, and detects around 10%more templates at fold and superfamily level.

We also confronted our method to HHsearch and PSI-BLAST on previous CASP competitions(CASP 8, 9 and 10) experiments and demonstrated that at 12 % of False Positive Rate (FPR)ORION achieved a True Positive Rate (TPR) of 45 %, 2 times more than HHsearch and retrieved8.6% more homologs than HHsearch in the top 10.

Finally, we also successfully assessed a pre-version of ORION in CASP 11 experiment, the lastedition of the international competition dedicated to assess the state of the art method in proteinprediction in which we participated in 2014.

References:

Illergård K, Ardell DH, Elofsson A. Structure is three to ten times more conserved than sequence–a study of structural response in protein cores. Proteins. 2009 Nov 15;77(3):499-508

Koretke KK, Russell RB, Copley RR, Lupas AN. Fold recognition using sequence and secondarystructure information. Proteins. 1999;Suppl 3:141-8.§ de Brevern AG, Etchebest C, HazoutS. Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.Proteins. 2000 Nov 15;41(3):271-87.

Joseph AP, Agarwal G, Mahajan S, Gelly JC, Swapna LS, Offmann B, Cadet F, Bornot A,Tyagi M, Valadié H, Schneider B, Etchebest C, Srinivasan N, De Brevern AG. A short surveyon protein blocks. Biophys Rev. 2010 Aug;2(3):137-147.

Zimmermann O, Hansmann UH. LOCUSTRA: accurate prediction of local protein structure us-ing a two-layer support vector machine approach. J Chem Inf Model. 2008 Sep;48(9):1903-8.

Mizuguchi K, Deane CM, Blundell TL, Overington JP. HOMSTRAD: a database of proteinstructure alignments for homologous families. Protein Sci. 1998 Nov;7(11):2469-71.

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLASTand PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997Sep 1;25(17):3389-402.

Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005 Apr1;21(7):951-60.

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SAMSON: Software for Adaptive Modelingand Simulation Of Nanosystems

Nadhir Abdellatif , Svetlana Artemova , Marc Aubert , Jocelyn Gate ,Sergei Grudinin , Leonard Jaillet , Khoa Nguyen , Mohamed Yengui ,

Stephane Redon ∗ 1

1 INRIA Rhône-Alpes (INRIA Grenoble Rhône-Alpes) – INRIA – ZIRST 655 Avenue de l’EuropeMontbonnot 38334 Saint Ismier cedex, France

SAMSON: Software for Adaptive Modeling and Simulation Of NanosystemsNadhir Abdellatif, Svetlana Artemova, Marc Aubert, Jocelyn Gate, Sergei Grudinin, LeonardJaillet, Khoa Nguyen, Mohamed Yengui, and Stephane Redon*

NANO-D, INRIA Grenoble - Rhone-Alpes, 38334 Saint Ismier Cedex, Montbonnot, France; Lab-oratoire Jean Kuntzmann, B.P. 53 38041 Grenoble, Cedex 9, France.

SAMSON (Software for Adaptive Modeling and Simulation Of Nanosystems), the software plat-form for computational nanoscience that we have been developing in the NANO-D group, is nowavailable on SAMSON Connect at http://www.samson-connect.net.

SAMSON integrates modeling and simulation to aid in the analysis and design of molecularsystems. For instance, an interactive quantum chemistry module (ASED-MO level of theory)makes it possible for users to build and edit structures while interactively visualizing how theelectronic density is updated (Figure – a); interactive flexing and twisting tools allow users toeasily perform large-scale flexible deformations of e.g. proteins with a few mouse clicks (Figure– b); interactive virtual prototyping of hydrocarbon systems may be used to edit and constraingraphene sheets, nanotubes (Figure – c), etc.

This integration is made possible via SAMSON’s data graph, which contains all informationon models and simulators. Nanosystems are described through five types of models: structuralmodels (geometry and topology), dynamical models (degrees of freedom), interaction models (po-tential energy, forces, electronic structure), visual models (graphical representations, e.g. Figure– d) and property models. SAMSON’s data graph relies on a signalling system that may beused to develop adaptive algorithms. For instance, an adaptively restrained state updater maycontrol, at each time step of a simulation, which degrees of freedom should be updated in adynamical model [1]. In turn, an interaction model may request from a dynamical model the listof positions that have been updated since the last simulation step, to incrementally update thepotential energy, forces and electronic structure [2, 3, 4].

Most important, SAMSON has an open architecture: a Software Development Kit allows devel-opers to extend SAMSON’s functionality by developing SAMSON Elements, i.e. new modules∗Intervenant

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for SAMSON such as new interaction models, editors (e.g. procedural generators), apps, wrap-pers or interfaces to existing software, connectors to web services, etc. The SAMSON Connectwebsite (http://www.samson-connect.net) is open for developers and users to easily distributeand install SAMSON Elements.

We will present the architecture of SAMSON and its general design principles, as well as theSAMSON SDK and SAMSON Connect.

S. Artemova and S. Redon, Physical Review Letters, 109:19, 2012

M. Bosson et al, Journal of Computational Physics, 231:6, 2012

M. Bosson et al, Journal of Computational Chemistry, 34:6, 2013

R. Rossi et al, Bioinformatics, 23:13, 2007

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Integration of Chemical and Biological(’omics’) Data for the Prediction of Cancer

Cell-Line Sensitivity

Isidro Cortes ∗† 1, Gerard J.p. Van Westen 2, Guillaume Bouvier 3, MickaelNilges 1, John P. Overington 2, Andreas Bender 3, Therese Malliavin 1

1 Unite de Bioinformatique Structurale – Institut Pasteur de Paris, CNRS : UMR3528 – 28 rue dudocteur Roux 75724 Paris, France

2 European Bioinformatics Institute [Cambridge] (EMBL-EBI) – EMBL-EBI, Wellcome Trust GenomeCampus, Hinxton, Cambridgeshire, CB10 1SD, UK, Royaume-Uni

3 Centre for Molecular Science Informatics – Department of Chemistry, University of Cambridge,Lensfield Road, CB2 1EW, Cambridge, Royaume-Uni

Cultured cell-lines have proved versatile disease models for cancer drug discovery [1]. Inthe last decades, large-scale multi-omics initiatives have catalogued the somatic alterations ofcancer cell-line panels coupled with their pharmacological response to thousands of compounds[1,2,3]. Although these cell-line collections have proved valuable to identify genomic markers ofdrug sensitivity [2,3], the question now arises how these pharmacogenomic data can be mean-ingfully mined to discover cancer-specific drugs. Here, we propose the simultaneous modellingof chemical and cell-line information in single machine learning models to predict with errorbars the growth inhibition 50% bioassay end-point (GI50) of 17,142 compounds screened against59 cancer cell-lines from the NCI60 panel. The integration of these different, yet complemen-tary, streams of information is often termed Proteochemometrics (PCM) or pharmacogenomicmodelling (PGM) [4]. PCM is a computational method to simultaneously model the bioactiv-ity of multiple ligands against multiple biomolecular targets, and therefore permits to explorethe selectivity and promiscuity of ligands [4]. In typical PGM models, each compound-cell-lineinteraction is numerically encoded by the concatenation of compound and cell-line descriptors,which are related in single machine-learning models to a specific biological readout of interest.To describe the compounds we used Morgan fingerprints, whereas to describe the cell-lines, wedownloaded and curated the following cell-line profiling data: gene expression, DNA copy-numbervariation, protein abundance, exome sequencing, miRNA abundance, and cell-line fingerprints.We benchmarked their predictive signal by training one model on each cell-line profiling data andon compound fingerprints, finding that protein, gene transcript, and miRNA abundance providethe highest predictive signal, significantly outperforming DNA copy-number variation or exomesequencing data (Tukey’s HSD, P < 0.05). The performance of the best model on the testset led to mean R20 and RMSE values of 0.83 and 0.40 pGI50 units, respectively. Comparablemean RMSE values were obtained when interpolating and extrapolating compound bioactivitiesto novel cell-lines (RMSE=0.43), and when interpolating compound bioactivities to structurallysimilar compounds (RMSE=48-0.65). Nevertheless, the mean RMSE value increased till 0.83when extrapolating to∗Intervenant†Auteur correspondant: [email protected]

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chemically dissimilar compounds, indicating that careful attention should be paid to not overstepthe boundaries of themodels applicability domain. Finally, we demonstrate that the predicted bioactivities can beused to predict growth inhibition patterns across the NCI60 panel for compounds excluded fromthe training set (e.g. methotrexate), and that significant drug-pathway associations are consis-tent with the experimental data published in the literature. Conceptually, this approach couldalso be extended to select personalized treatments on the basis of patients genomic makeup.JN Weinstein. Drug discovery: Cell lines battle cancer. Nature 2012, 483, 544–545.J Barretina, G Caponigro, N Stransky et al. The Cancer Cell Line Encyclopedia enables predic-tive modelling of anticancer drug sensitivity. Nature 2012, 483, 603–607.Garnett, MJ, EE Edelman, SJS Heidorn et al. Systematic identification of genomic markers ofdrug sensitivity in cancer cells. Nature 2012, 483, 570–575.

I Cortes-Ciriano, QU Ain, V Subramanian et al. Polypharmacology Modelling Using Pro-teochemometrics (PCM): Recent Methodological Developments, Applications to Target Families,and Future Prospects. Med. Chem. Commun., 2015, 6, 24-50.

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COMMUNICATION ORALE

Session poster GT Enzymes/GGMM

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Les thioltransférases : production etélimination de sulfure d’hydrogène ?

Jean-Christophe Lec ∗ 1, Séverine Boutserin 1, Hortense Mazon 1, FrançoisTalfournier ∗ † 1, Sandrine Boschi-Muller ∗ ‡ 1

1 UMR 7365 CNRS-Université de Lorraine (IMoPA) – CNRS : UMR7365, Université de Lorraine – 9Avenue de la Foret de Haye, 54505 Vandoeuvre les Nancy, France

Les thioltransférases sont des enzymes ubiquitaires de la famille structurale des rhodanèsesqui catalysent le transfert de soufre d’un substrat donneur vers un substrat accepteur, via laformation d’un intermédiaire persulfure sur la cystéine catalytique. Elles se répartissent endeux familles selon la nature du substrat donneur et leurs rôles physiologiques proposés : les3-mercaptopyruvate sulfurtransférases (3-MST) utilisent le 3-mercaptopyruvate (3-MP) et sontimpliquées dans la production de sulfure d’hydrogène, alors que les thiosulfate sulfurtransférases(TST) utilisent le thiosulfate (TS) et assurent des fonctions variées comme la détoxication ducyanure, la biosynthèse des centres Fe-S ou le catabolisme d’H2S au niveau mitochondrial. Afinde caractériser les mécanismes moléculaires associés aux rôles physiologiques proposés pour lesthioltransférases, l’identification des bases moléculaires responsables de la spécificité structurale aété abordée en évaluant l’efficacité catalytique des deux étapes de transfert de soufre en présencede différents substrats donneurs et accepteurs. Les résultats obtenus sur des enzymes d’originehumaine et bactérienne montrent que, contrairement à ce qui est décrit dans la littérature, le3-MP est un meilleur substrat donneur que le TS pour les deux familles d’enzymes. De plus,elles présentent des spécificités différentes pour le substrat accepteur : la thiorédoxine pour les3-MST et le glutathion et le sulfite pour les TST.


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Communication par affiche Poster P1



Rôle fonctionnel d’une cavité internehydrophobe dans l’urate oxydase

Nathalie Colloc’h ∗ 1, Thierry Prangé 2

1 Imagerie et stratégies thérapeutiques des pathologies cérébrales et tumorales (ISTCT) – Université deCaen Basse-Normandie, CNRS : UMR6301, CEA – GIP CYCERON UMR 6301- CNRS Boulevard

Henri Becquerel BP 5229 14074 CAEN CEDEX, France2 Laboratoire de cristallographie et RMN biologiques (LCRB) – CNRS : UMR8015, Université Paris V -Paris Descartes – Faculté de Pharmacie 4 Avenue de l’Observatoire 75270 PARIS CEDEX 06, France

L’urate oxydase est un homo-tetramère de 135 kDa qui possède une cavité hydrophobe enfouiedans chaque monomère et proche du site actif. Les structures cristallographiques sous pressionmodérée de gaz inerte (10-30 bar) ou sous haute pression hydrostatique (1500 bar) ont montréque la présence d’un gaz ou l’application de la pression entrainent tous deux une rigidification dela cavité et induisent une inhibition du mécanisme catalytique. La flexibilité de la cavité sembledonc essentielle pour l’activité catalytique de cette enzyme. Elle peut donc être considérée commeun site allostérique qui donnerait de la flexibilité au site actif voisin, et aurait également un rôlede réservoir transitoire pour l’oxygène sur son chemin d’accès vers le site actif.

∗Intervenant

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Benzene-induced leukemogenesis: Irreversibleinhibition of PTPN2, a tumor suppressorphosphatase involved in leukemia by thehematotoxic metabolite benzoquinone

Romain Duval ∗ , Linh Chi Bui 1, Laeticia Durant , Cécile Mathieu 1, EmilePetit , Jean-Marie Dupret , Jan Cools , Christine Chomienne , Fabien

Guidez , Fernando Rodrigues-Lima† 1

1 Unité de Biologie Fonctionnelle et Adaptative (BFA- RMCX) – CNRS : UMR8251, Université ParisVII - Paris Diderot – UFR Sciences du Vivant - 4 rue Marie-Andrée Lagroua Weill-Hallé 75205 Paris

Cedex 13, France

Benzene (BZ) is a chemical compound of industrial and toxicological interest classified as aclass I human carcinogen. Environmental and occupational exposure to BZ lead to bone marrowmalignancies such as leukemia. The leukemogenic effects of BZ relies on its metabolization inbone marrow cells into reactive metabolites, in particular benzoquinone (BQ) that can reactwith macromolecules (arylation) and/or induce oxidative stress. Although BZ is well recognizedas a leukemogenic chemical, most of the key molecular and cellular mechanisms underlying itshematotoxicity are not fully understood.PTPN2 is a protein tyrosine phosphatase (PTP) mainly expressed in hematopoietic cells andplaying a key role in the homeostasis of the hematopoietic system. In particular, this PTP isan important modulator of growth factors and JAK/STAT signaling pathways. Loss of functionanalyses in patients with mutation/deletion of the PTPN2 gene and knock-out mouse modelsindicate that PTPN2 acts as a tumor suppressor in haematologic disorders such as leukemia.We found that BQ, the prime hematotoxic metabolite of BZ, is an irreversible inhibitor ofhuman PTPN2. Kinetic and biochemical analyses using purified PTPN2 indicated that theirreversible inhibition of the enzyme by BQ is mainly due to arylation of its active site cysteine.Exposure of immortalized human hematopoietic cells (Jurkat T and THP-1 lines) to BQ leadsto the irreversible inhibition of endogenous PTPN2 activity with a concomitant over activationof JAK/STAT signaling pathway. Irreversible BQ-dependent inhibition of PTPN2 in cells wasfound to be mainly due to overoxydation of its catalytic cysteine into sulfinic and/or sulfonicforms.In Vivo experiments conducted in mice confirmed that exposure to BZ leads to irreversibleinhibition of PTPN2 in bone marrow and spleen cells.Our data provide the first mecanistic evidence that irreversible inhibition of PTPN2, a tumorsuppressor tyrosine phosphatase, may contribute to benzene-dependent leukemogenesis.

∗Intervenant†Auteur correspondant:

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mailto:

Cellular redox signaling by thiol peroxidase :Mechanisms responsible for the specificity of

the redox relay H2O2/Orp1/Yap1 in S.cerevisiae

Antoine Bersweiler 1, Hortense Mazon 1, Alexandre Kriznik ∗ 1, GuyBranlant 1, Sophie Rahuel-Clermont† 1

1 IMoPA UMR 7365 CNRS-Université de Lorraine (IMOPA) – CNRS : UMR7365, Université deLorraine – Biopôle, campus Biologie-Santé 9 Avenue de la Forêt de Haye, CS 50184 54505

Vandœuvre-lès-Nancy Cédex, France

Thiol-peroxidases, described as antioxidant enzymes, have also been associated with peroxide-dependent cell signaling as sensor and relay of the H2O2-mediated signal (1). One of the bestdocumented examples of such a mechanism is the activation of the transcription factor Yap1, akey regulator of the transcriptional peroxide stress response in Saccharomyces cerevisiae, whichdepends on the formation of intramolecular disulfide bonds catalyzed by the thiol peroxidaseOrp1 (2 ; 3). In this mechanism, it has been proposed that the relay occurs via the oxidationof Orp1 peroxidatic Cys as a sulfenic acid intermediate which reacts with Yap1 to form a mixeddisulfide species (3). In addition, the Ybp1 protein has been identified as an essential partnerfor the activation of Yap1 by Orp1 (4).The intrinsic reactivity of Orp1 sulfenic acid species could potentially result in competition be-tween Yap1 and other thiols, such as the regeneration Cys of Orp1 or other cellular thiols. Thisraises the question of the specificity of Yap1 activation by H2O2/Orp1. To address this question,and to elucidate the role of Ybp1 in this mechanism, we have used an approach based on thekinetic characterization of the two reactions in competition: the peroxydatic cycle of Orp1 andthe reaction withYap1, using rapid kinetic stop flow and quench flow techniques. From the studyof the impact of Ybp1 on these kinetics and the characterization of the protein-protein interac-tions between the three partners by fluorescence anisotropy and microcalorimetry, we proposethat Ybp1 and Yap1 recruit Orp1 within a ternary complex, which restrains intramolecular disul-fide formation within Orp1 and allows the reaction between Orp1 sulfenic intermediate and Yap1.

(1) Fourquet S., Huang, M., D’Autreaux, B., Toledano, M.B. (2008), Antiox. Redox Sign.10, 15565-76.

(2) Delaunay, A., Isnard, A.D., Toledano, M.B. (2000) EMBO J. 19, 5157-66.

(3) Delaunay, A., Pflieger, D., Barrault, M.B., Vinh, J., Toledano, M.B. (2002) Cell 111,471-81.


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(4) Veal, E.A., Ross, S.J., Malakasi, P., Peacock, E., Morgan, B.A. (2003) J. Biol. Chem.278, 30896-904.

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Molecular basis of the alteration of brainglycogen metabolism by dithiocarbamatepesticides: impairment of brain glycogen

phosphorylase

Cécile Mathieu ∗ 1, Linh Chi Bui 1, Solène Boitard 2, Onnik Agbulut 2,Jean-Marie Dupret 1, Fernando Rodrigues-Lima 1

1 Unité de Biologie Fonctionnelle et Adaptative (BFA- RMCX) – CNRS : UMR8251, Université ParisVII - Paris Diderot – UFR Sciences du Vivant - 4 rue Marie-Andrée Lagroua Weill-Hallé 75205 Paris

Cedex 13, France2 Adaptation Biologique et Vieillissement – CNRS : UMR8256, Université Pierre et Marie Curie

(UPMC) - Paris VI – Campus de Jussieu - 4 place Jussieu 75005 Paris, France

Dithiocarbamates (DTC) are organosulfur compounds widely used as fungicide. Acute ex-posure is mainly a concern for agricultural and industrial workers but large population can beexposed through residues in food and water. The high reactivity of these chemical lead to nu-merous toxic effects and have been linked to Parkinson’s disease. Different biological processeshave been suggested to be altered by DTC including glycogen (gln) metabolism.Gln is a glucose polymer found primarily in liver, muscles and brain where it represents the mainstorage-form of glucose. In brain, Gln is mainly found in astrocytes and appears to be criticalfor certain neurological processes such as learning and long term memory consolidation. Brainglycogen phosphorylase (bGP) is the key enzyme involved in mobilization of Gln. This enzymeis thighly regulated by both phosphorylation and binding of allosteric effectors.

In order to better understand the molecular mechanisms of neurotoxicity linked to DTC, westudied the impact of Thiram (TH), a well known DTC, on bGP. Incubation of mouse brainextracts with TH impaired glycogen mobilization which is concomitant with the inhibition ofbGP. Studies on recombinant human bGP showed that TH and its metabolites were potent in-hibitors of bGP. Chemical-labelling, electrophoretic and mass spectrometry analyses suggestedthat inhibition of purified bGP by TH occured through modification of critical cysteines residueand formation of intramolecular/intermolecular disulfide bonds.Altogether our data indicate that certain DTC inhibit the glycogenolytic activity of bGP throughthe modification of key cysteine residues with subsequent impairment of Gln catabolism. Thismay lead to altered energy metabolism or toxic accumulation of Gln in astrocytes. More broadly,owing to the important role of Gln in neurological processes, impairment of bGP functions inbrain could contribute to the neurotoxic effects of DTC.

∗Intervenant

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Réactivité de la carbon monoxidedehydrogenase à nickel avec l’oxygène

Meriem Merrouch ∗† 1, Jessica Hadj-Said 1, Lilith Domnik 2, Holger Dobbek2, Sébastien Dementin 1, Claude Léger 1, Vincent Fourmond 1

1 Bioénergétique et Ingénierie des Protéines (BIP) – CNRS : UMR7281, Université de la Méditerranée -Aix-Marseille II – 31 Chemin J. Aiguier, 13402 Marseille Cedex 20, France

2 Institut für Biologie, Strukturbiologie/Biochemie – Humboldt-Universität zu Berlin, 10099 Berlin,Allemagne

Les Carbon Monoxide Dehydrogenases (CODH) catalysent l’oxydation réversible du CO enCO2 selon la réaction:CO + H2O CO2 + 2e- + 2H+

Le site actif des CODH isolées d’organismes anaérobies est constitué d’un centre [Ni-4Fe-4S][1, 2]. Ces enzymes sont réputées être extrêmement sensibles à l’O2, cependant le mécanismed’inactivation par l’oxygène n’est pas connu [3]. Nous avons étudié par électrochimie directe[4] l’effet de l’oxygène sur deux CODH : la CODH II de Carboxydothermus hydrogenoformans(Ch) et la CODH de Desulfovibrio vulgaris Hildenborough (Dv). Nos résultats ont montré quel’oxygène interagit rapidement avec les sites actifs des deux CODH, cependant l’inactivation estréversible, principalement après une étape de réduction de l’enzyme. Nous montrons égalementque la réactivité des deux enzymes vis-à-vis de l’O2 est différente: la fraction de Dv CODHréactivée après exposition à l’O2 est beaucoup plus importante que celle de la Ch CODH. Enparticulier, dans les conditions de concentration d’O2 utilisées pendant notre étude, l’enzyme deDv est réactivée presque complètement, ce qui n’est pas le cas pour l’enzyme de Ch.

Nos travaux apportent pour la première fois des données mécanistiques concernant l’inhibitiondes CODH par l’O2 et montrent que la réactivité des CODH vis-à-vis de l’O2 n’est pas la mêmed’une espèce à l’autre, comme c’est le cas pour d’autres enzymes à Nickel, comme les hydrogé-nases.

1. Jeoung, J.-H., Dobbek, H. Science 2007, 318, 1461-1464.

2. Drennan, C., Doukov, T., Ragsdale, S. Journal of Biological Inorganic Chemistry 2004,9, 511-515.

3. Can, M., Armstrong, F. A., Ragsdale, S. W. Chem. Rev. 2014, 114, 4149-4174.4. Léger, C., Bertrand, P. Chem. Rev. 2008, 108, 2379-2438.


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Regulation of thiol peroxidases inperoxide-dependent redox signaling:

Thioredoxin vs. glutathione in reduction ofhyperoxidized 2-Cys peroxiredoxin by

Sulfiredoxin

Samia Boukhenouna 1, Hortense Mazon 1, Guy Branlant 1, ChristopheJacob 1, Michel Toledano 2, Sophie Rahuel-Clermont ∗† 1

1 IMoPA UMR 7365 CNRS-Université de Lorraine (IMOPA) – CNRS : UMR7365, Université deLorraine – Biopôle, campus Biologie-Santé 9 Avenue de la Forêt de Haye, CS 50184 54505

Vandœuvre-lès-Nancy Cédex, France2 Laboratoire Stress Oxydants et Cancer (LSOC) – CEA – CEA, iBiTecS, LSOC, Gif-sur-Yvette Cedex,

France., France

Due to its oxidant properties, hydrogen peroxide (H2O2) can act both as a toxic and as acellular messenger. Typical 2-Cys-peroxiredoxins (Prx) are thiol peroxidases sensitive to H2O2-mediated hyperoxidation of the catalytic Cys to the sulfinic acid state. This unique property isinvolved in regulation of Prx functions in peroxide-dependent redox signaling and is reversed byATP-dependent reduction by Sulfiredoxin (Srx).

Srx catalytic mechanism involves a unique enzymatic chemistry, which leads to the formation of athiolsulfinate intermediate PrxSO-SSrx that must be reduced to complete the cycle. To evaluatethe mechanism and efficiency of the cellular redox systems thioredoxin (Trx) and glutathione(GSH) in Srx recycling, we combined in vitro steady state and single turnover kinetic analysesof the reaction monitored by enzymatic coupled assay, Prx intrinsic fluorescence, SDS-PAGEor reverse phase chromatography coupled to mass spectrometry for the characterization of in-termediate species, and in vivo approaches using S. cerevisiae strains depleted in Trx or GSH.In the case of S. cerevisiae, owing to the presence of an additional Cys residue, Srx recyclinginvolves the formation of an oxidized Srx intermediate that is efficiently reduced by Trx. On thecontrary, in the case of Srx lacking this resolving Cys48 as in mammals or plants (1-Cys Srxs),using S. cerevisiae Srx mutants lacking Cys48 as a model, we show that GSH reacts directly withthe thiolsulfinate intermediate on the Srx moiety, releasing glutathionylated Srx as a catalyticintermediate. Unexpectedly, the results suggest that GSH binds to the thiolsulfinate complex,thus allowing efficient and non rate-limiting reduction. Total cellular depletion of GSH impactedthe recycling of Srx, confirming in vivo that GSH is the physiologic reducer of 1-Cys Srx.


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Computer-aided engineering of atransglycosylase for the glucosylation of anunnatural disaccharide of relevance for

bacterial antigen synthesis

Alizée Verges 1, Emmanuelle Cambon 1, Sophie Barbe 1, StéphaneSalamone 2, Yann Le Guen 2, Claire Moulis 1, Laurence Mulard 2, Magali

Remaud-Siméon 1, Isabelle André ∗ 1

1 Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés (LISBP) – Institut National desSciences Appliquées [INSA], Centre national de la recherche scientifique - CNRS (France), Institut

national de la recherche agronomique (INRA) – 135 Avenue de rangueil 31077 Toulouse cedex 04, France2 Institut Pasteur, Unité de chimie des biomolécules – CNRS : UMR3523, Institut Pasteur de Paris – 28

rue du Docteur Roux 75724 Paris Cedex 15, France

The exploration of chemo-enzymatic routes to complex carbohydrates has been hamperedby the lack of appropriate enzymatic tools having the substrate specificity for new reactions.Here, we used a computer-aided design framework to guide the construction of a small, diver-sity controlled library of amino acid sequences of an a-transglucosylase, the sugar binding sub-sites of which were re-engineered to enable the challenging 1,2-cis -glucosylation of a partiallyprotected -linked disaccharide – allyl (2-deoxy-2-trichloroacetamido--D-glucopyranosyl)-(12)--L-rhamnopyranoside, a potential intermediate in the synthesis of Shigella flexneri cell-surfaceoligosaccharides. The target disaccharide is not recognized by the parental wild-type enzymeand exhibits a molecular structure very distinct from that of the natural -(1,4)-linked acceptor.A profound reshaping of the binding pocket had thus to be performed. Following the selection of23 amino acid positions from the first shell, mutations were sampled using RosettaDesign leadingto a subset of 1,515 designed sequences, which were further analyzed by determining the aminoacid variability among the designed sequences and their conservation in evolutionary relatedenzymes. A combinatorial library of 2.7 10^4 variants was finally designed, constructed andscreened. One mutant showing the desired and totally new specificity was successfully identifiedfrom this first round of screening. Impressively, this mutant contained 7 substitutions in the firstshell of the active site leading to a drastic reshaping of the catalytic pocket without significantlyperturbing the original specificity for sucrose donor substrate.This work illustrates how computer-aided approaches can undoubtedly offer novel opportunitiesto design tailored carbohydrate-active enzymes of interest for glycochemistry or synthetic glyco-biology.

This work was supported by the ANR Project GLUCODESIGN (ANR-08-PCVI-002-02).

A.V., E.C., S.B. contributed equally to the work.

∗Intervenant

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References :Verges A., Cambon E., Barbe S., Salamone S., Le Guen Y., Moulis C., Mulard L.A., Remaud-Siméon M., André I. 2015. Computer-aided engineering of a transglucosylase for the glucosylationof an unnatural disaccharide of relevance for bacterial antigen synthesis.ACS Catalysis. 5:1186-1198

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Computational Enzyme Design throughdeterministic search and counting methods

Clément Viricel 1, Seydou Traore 1, David Simoncini 2, David Allouche 2,Simon De Givry 2, Isabelle André 1, Thomas Schiex 2, Sophie Barbe ∗† 1

1 Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés (LISBP) – Institut National desSciences Appliquées [INSA], Centre National de la Recherche Scientifique - CNRS, Institut national de

la recherche agronomique (INRA) – 135 Avenue de rangueil 31077 Toulouse cedex 04, France2 Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA) – Institut national dela recherche agronomique (INRA) : UR875 – Chemin de Borde Rouge, 31320 Castanet Tolosan, France

Enzymes are powerful catalysts, which can be used for a wide range of innovative, efficientand eco-compatible processes that can open the way to many biotechnological applications. How-ever, enzymes available in nature have evolved to operate under physiological conditions on anarrow range of substrates and they often do not display the physico-chemical properties requiredfor their practical use in industrial-scale biocatalytic processes. Enzyme engineering has becomea key technology to modulate both the structure and the function of polypeptides and generateenzymes displaying desired properties/activities. The integration of computational methods inenzyme engineering strategies has grown interest to increase the chances of success to tailor thewanted enzymes, while reducing the human and financial costs.Structure-based Computational Protein Design (CPD) aims at guiding evolution of proteins onrelevant sequence space regions to achieve the desired function and thus reduce the size of mutantlibraries to build and screen experimentally. Starting from an exponentially large space of possi-ble sequences, CPD seeks to identify those that fold into stable three-dimensional structures andpossess the desired functional properties. Despite notable achievements in the field, numerouslimitations still need to be addressed in these computational methods to improve their efficiency,predictability and reliability.

In this work, we have adapted and extended several cutting-edge deterministic combinatorial op-timization techniques to solve various CPD problems. In particular, we have developed novel en-zyme design approaches based on Cost Function Networks (CFN) (combining Depth First Branchand Bound search with Local Consistency properties) to search the sequence-conformation spaceto identify provably the optimal solution in regard to the design objective function (namelyenzyme or enzyme-substrate complex stability in this work) [1-4]. These approaches are ableto handle highly complex enzyme design problems that previously could not be solved exactly.Compared to deterministic methods commonly used in CPD, the CFN-based approaches speedup the search process by several orders of magnitude. Hence, the optimal solution is provablyfound for enzyme design problems with large sequence-conformation spaces. The CFN-basedapproach has also been extended to enable the generation of ensembles of near-optimal solutionsthat could be used for the rational design of sequence libraries.∗Intervenant†Auteur correspondant: [email protected]

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Moreover, the efficiency of the CFN optimization techniques has also been exploited to de-velop a scoring and search method aiming to optimize the enzyme-substrate binding affinity [5].It combines a statistical mechanics-derived ensemble-based approach with the speed and thecompleteness of CFN algorithms to compute approximate enzyme-substrate binding constantswith deterministic guarantees.

Most recent developments and their application on various CPD problems will be presented.

1- Allouche D, Davies J, de Givry S, Katsirelos G, Schiex T, Traoré S, André I, Barbe S,Prestwitch S, O’Sullivan B. 2014. Computational Protein Design as an optimization Problem.Artificial Intelligence 212:59-79.

2- Traoré S, Allouche D, André I, de Givry S, Katsirelos G, Schiex T, Barbe S. 2013. A NewFramework for Computational Protein Design through Cost Function Network Optimization.Bioinformatics 29:2129-36

3- Allouche D, Traore S, André I, de Givry S, Katsirelos G, Barbe S, Schiex T. 2012. Computa-tional Protein Design as a Cost Function Network Optimization Problem. In, Proceedings of the18th international conference on Principles and Practice of Constraint Programming, Québec,Canada.

4- Traoré S, Roberts K-E, Allouche D, Donald B-R, André I, Schiex T, Barbe S. Fast searchalgorithms for Computational Protein Design. Submitted5- Viricel C, Simoncini D, Allouche D, de Givry S, Barbe S, Schiex T. 2015. Approximate Count-ing with Deterministic guarantees for Affinity Computation. In, proceedings of the internationalconference on Modelling, Computation and Optimization in Information Systems and Manage-ment Sciences, MCO 2015, Metz, France: Advances in Intelligent Systems and Computing -Springer.

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Towards the development of an in silico toolsfor kinetics prediction

Abdennour Braka ∗ 1,2, Stéphane Bourg 1,2,3, Norbert Garnier 1, SamiaAci-Sèche 2, Pascal Bonnet 2

1 Centre de biophysique moléculaire (CBM) – CNRS : UPR4301 – Rue Charles Sadron 45071ORLEANS CEDEX 2, France


3 Fédération de Recherche Physique et Chimie du Vivant – CNRS : FR2708 – Rue Charles Sadron,45071 Orléans Cedex 2, France

Molecular docking plays an increasing important role in drug discovery programs. Whiledocking tools can accurately predict the binding mode of a ligand into the active site of a pro-tein, the evaluation of binding affinity is still a major issue. One of the most important limitationsis the treatment of protein flexibility during docking. The simplistic rigid model of ligand/proteincomplex is inadequate to represent the full motion of a protein structure during ligand binding.Moreover, most scoring functions are often correlated with activity-based data without takinginto account the kinetics of ligand binding (koff and kon).

This project aims to develop a new predictive method to improve the docking performanceby considering protein flexibility. Protein kinases are promising therapeutic targets, which arehighly flexible. In this study, we focus on the development of a novel in silico method using thep38 Map kinase as an example.

To represent the motion of the protein, thousands of conformations were generated using molec-ular dynamics simulations (MD). We compared the performance of classical MD (cMD) with twobiased methods: an in house restrained MD method (rMD) (1) and the Replica-Exchange MD(REMD) (2). As expected, the biased methods (rMD, REMD) widely outperform the cMD forthe protein conformational sampling. We then used a clustering method based on k-means toselect a representative subset of binding site conformations. A multiconformational docking isactually ongoing using this representative subset for the protein; and the ligands were extractedfrom the Directory of Useful Decoys (DUD-E) (3) database.

Later, we envisage taking into account the association pathway of the ligand in order to predictkon association rate constant. The prediction of such biochemical data could be very useful forpreclinical compound selection and optimization.

References

(1) Aci-Sèche, S.; Garnier, N.; Genest, D.; Bourg, S.; Marot, C.; Morin-Allory, L.; Genest,∗Intervenant

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M. A Restrained Molecular Dynamics Empirical Approach for Generating a Small Set ofStructures Representative of the Internal Flexibility of a Receptor. QSAR Comb. Sci.2009, 28, 959–968.

(2) Sugita, Y.; Okamoto, Y. Replica-Exchange Molecular Dynamics Method for ProteinFolding. Chemical Physics Letters 1999, 314, 141–151.

(3) Mysinger, M. M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of Useful Decoys,Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. J. Med. Chem.2012, 55 (14), 6582–6594.

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In silico experiments on biomolecules throughthe Static Modes: An efficient approach to

predict and design function

Marie Brut ∗ 1

1 Laboratoire d’analyse et d’architecture des systèmes [Toulouse] (LAAS) – Institut NationalPolytechnique de Toulouse - INPT, Université Paul Sabatier (UPS) - Toulouse III, CNRS : UPR8001,

Institut National des Sciences Appliquées [INSA] - Toulouse – 7 Av du colonel Roche 31077TOULOUSE CEDEX 4, France

A highly accurate understanding of the molecular mechanisms that govern biological pro-cesses is required to accelerate medical and pharmaceutical innovation. Molecular modelingand simulation have become indispensable tools to address this question and provide new in-sight where information is not available through experimentation alone. However, predictingthe structure-activity relationship that determines biomolecular function remains an outstand-ing challenge for computational investigations. In this competitive background, the Static Modemethod developed at LAAS-CNRS, is an innovative approach to account for biomolecular flex-ibility in the prediction of structure/activity relationship. Thanks to the pre-calculation of theintrinsic deformations of biomolecules submitted to custom designed stimuli, this method allowsthe user to design specific actions applied on single or multi-atom sites, and to anticipate theinduced structural/mechanical changes. We will show how the Static Modes can be used toefficiently localize allosteric sites and to decipher allosteric mechanisms with atomic precision.We will also present selected results on various enzymes to propose a guideline for efficient,predictive and custom in silicoexperiments: ligand-induced accommodation, mutation effects,domain communication... In particular, we will focus on the case of Ras oncoproteins and willdiscuss how this low-cost computational approach offers unprecedented possibilities to exploreRas biomechanical properties and to mechanically simulate the activation/inactivation process.Our goal is to acquire a unique understanding of the biomolecular processes that govern Rasbiological function and mutation effects, and consequently, how to reverse them by performingthe screening of mutations impact in view of guiding mutation experiments.

∗Intervenant

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Proteus: dessin de protéine en backboneflexible

Karen Druart ∗† 1, David Mignon 1, Edouard Audit 2, Georgios Archontis‡3, Thomas Simonson§ 1

1 Laboratoire de Biochimie (BIOC) – CNRS : UMR7654, Polytechnique - X – Laboratoire de BiochimieEcole Polytechnique 91128 Palaiseau, France

2 Maison de la Simulation (MDLS) – CEA – USR 3441 bât. 565 CEA Saclay 91191 Gif-sur-Yvettecedex, France

3 Theoretical and Computational Biophysics Group – Department of Physics University of Cyprus P.OBOX 20537 1678 Nicosia Cyprus, Chypre

Le dessin computationnel de protéine (CPD) est une méthode qui permet de prédire lesmutations pertinentes à réaliser pour conférer aux enzymes de nouvelles propriétés. Un logicielde CPD assez complet, Proteus, a été developpé dans l’équipe de Thomas Simonson à l’EcolePolytechnique (Simonson et al, 2013); il s’appuie sur un modèle de mécanique moléculaire fournipar le programme XPLOR. Brièvement, XPLOR précalcule les énergies d’interaction entre toutesles paires de résidus de la protéine. Ces calculs sont réalisés en maintenant le backbone fixe, surlequel les rotamères d’une librairie discrète sont positionnés. Les énergies sont ensuite reluespar un autre programme (“proteus”), qui permet une exploration Monte Carlo de l’espace desséquences et conformations. Cependant, la rigidité du backbone limite l’espace conformationnelde la protéine et diminue en principe la qualité du modèle. Très récemment, nous avons ajouté lapossibilité de prendre en compte un ensemble de conformations du backbone, au lieu d’une seule,avec la possibilité pendant le Monte Carlo de passer d’une conformation à l’autre. Pour obtenirun taux d’acceptation raisonnable, le saut de backbone est suivi par une courte relaxation dansl’espace des rotamères. Le changement global, saut de backbone puis relaxation rotamérique, estaccepté ou rejeté selon un test de Metropolis généralisé, qui garantit en principe une explorationselon une distribution de Boltzmann des séquences et des structures. Les premiers résultatsobtenus sur 3 systèmes tests (tyrosyl-ARNt synthétase, domaines SH3 et SH2) seront présentés.

∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant: [email protected]§Auteur correspondant: [email protected]

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Molecular dynamics with excited normalmodes reveals the role of the activation loop ofCyclin-Dependent Kinases in their open/closed

conformational equilibrium

Nicolas Floquet ∗ 1, Mauricio G.s. Costa 2, Paulo R. Batista , PedroRenault 3, Paulo M. Bisch , Florent Raussin , Jean Martinez , May C.

Morris , David Perahia 4

1 Institut des Biomolécules Max Mousseron (IBMM) – CNRS : UMR5247, Université de Montpellier –Faculté de Pharmacie, 15 Av. Charles Flahault, BP 14491, 34093 Montpellier, Cedex 05, France

2 Programa de Computação Científica, Fundação Oswaldo Cruz – 21949900, Rio de Janeiro, Brésil3 Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro – 21949-901, Rio

de Janeiro, Brésil4 Laboratoire de Biotechnologie et Pharmacologie génétique Appliquée (LBPA) – CNRS : UMR8113,École normale supérieure (ENS) - Cachan – 61 AVENUE DU PRESIDENT WILSON 94235 CACHAN

CEDEX, France

Cyclin-dependent kinases are central for timely regulation of cell cycle progression and consti-tute attractive pharmacological targets. Characterization of the structural and dynamic featuresof these kinases is essential to gain in-depth insight into their structure-activity relationships.Structural studies of CDK2/Cyclin A have yielded a wealth of information concerning the con-formations of the monomeric and heterodimeric forms of this kinase. There is however muchless structural information available for other CDK/Cyclin complexes, including CDK4/CyclinD complexes. In this study, we performed Normal Modes Analyses on CDK2/Cyclin A andCDK4/Cyclin D1 X-ray structures, which display a different position of the Cyclin partner rel-ative to the CDK, CDK2/Cyclin A being in a “closed” conformation in contrast to the “open”conformation of CDK4/Cyclin D1. Interestingly, we observed that the lowest frequency NMcomputed for each of these complexes described the transition between the “ open ” and the “closed ” conformations. Exploration of this motion with an explicit representation of the solventusing the Molecular Dynamics with Excited Normal Modes (MDeNM) method confirmed thatthe closed conformation was the most stable for the CDK2/Cyclin A complex, in agreementwith all available structures in the PDB. On the contrary, we clearly show that an open closedequilibrium may exist in CDK4/Cyclin D1, with closed conformations resembling that capturedfor CDK2/Cyclin A. Using the same approach, the putative roles of both the phosphoryl groupon Thr160 and of the conformation of the T-loop on this conformational equilibrium were inves-tigated. These results provide, for the first time, a dynamic view of Cyclin-dependent kinasesand reveal the existence of intermediate conformations which have not yet been characterized forCDK members other than CDK2, and which will be useful for the design of inhibitors, targetingcritical conformational transitions.

∗Intervenant

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Design raisonné de procédés enzymatiquespour la dégradation améliorée de biopolymères

Marc Gueroult ∗ 1, Pablo Alvarez 1,2, Emilie Amillastré 1,2, SophieDuquesne 1, Sophie Barbe ∗

1, Alain Marty 1,3, Isabelle André ∗ † 1

1 Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés (LISBP) – Institut National desSciences Appliquées [INSA], Institut national de la recherche agronomique (INRA) : UMR792, CNRS :

UMR5504 – 135 Avenue de rangueil 31077 Toulouse cedex 04, France2 Toulouse White Biotechnology (TWB) – Institut national de la recherche agronomique (INRA) :

UMR1337, CNRS : UMS3582 – 3 rue des satellites F-31400 Toulouse, France3 Carbios – Carbios – biopole Clermont Limagne rue Emile Duclaux, F-63360 St Beauzire, France

Le recyclage des plastiques d’origine biosourcée est un enjeu économique d’importance de nosjours. Au sein du laboratoire, des approches génomiques ont permis d’isoler une serine protéasequi montre une activité hydrolytique importante vis-à-vis de certains biopolymères courammentutilisés dans l’industrie, tel que l’acide polylactique (PLA). Cependant, l’activité de dégradationde cette enzyme n’est pas suffisante pour envisager son utilisation dans des procédés industriels.Ainsi, pour améliorer son activité catalytique, une approche d’ingénierie enzymatique a étédéveloppée au sein de notre équipe. Elle s’appuie sur des techniques de modélisation moléculairepour d’une part mieux comprendre les déterminants moléculaires impliqués dans la reconnais-sance du polymère par l’enzyme et d’autre part, pour proposer des modifications à apporternotamment par des mutations d’acides aminés, qui permettraient d’améliorer les performancescatalytiques de l’enzyme et contrôler la nature des produits obtenus.

Dans le cadre de ces travaux, nous avons mis en place une stratégie de modélisation molécu-laire multi-échelles pour étudier différentes facettes du procédé enzymatique de dégradation despolymères et guider de façon raisonnée l’ingénierie de ces enzymes. Ces approches combinentl’utilisation de modèles atomiques et de modèles “ gros grain ” à des outils dédiés à l’étude desinteractions moléculaires pour modéliser l’ensemble du processus enzymatique de dégradationdes polymères. L’étude réalisée pour l’amélioration du procédé enzymatique de dégradation duPLA sera rapportée.Financement : Cette étude a été financée dans le cadre du Projet FUI ThanaplastTM en collab-oration avec la société CARBIOS


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Ligand-Based QSAR studies on someCis-Stilbenes as Cyclooxygenase Inhibitor

Safia Kellou-Taïri ∗ 1, Zohra Bouakouk-Chitti

1 Université des Sciences et de la Technologie Houari Boumediene [Alger] (USTHB) – BP 32 EL ALIA16111 BAB EZZOUAR ALGER, Algérie

Cyclooxygenase (COX), which is the key enzyme in the synthesis of prostaglandins (PGs)from arachidonic acid, exists in two isoforms: COX-1 and COX-2. COX-1 is constitutively ex-pressed in a variety of cell types and is mainly involved in the synthesis of cytoprotective PGs ingastrointestinal (GI) tract, whereas COX-2 is expressed during inflammatory conditions. Non-steroidal anti-inflammatory drugs (NSAIDs) act through the inhibition of COX. All classicalNSAIDs are capable of inhibiting both isoforms; however, they are found to be bound moretightly to COX-1. As a result, those drugs are associated with a high risk of adverse effects onthe GI tract as well as on the renal function. Owing to these problems, a novel NSAIDs gen-eration has been developed by increasing the selectivity of COX-2. However, the use of specificCOX-2 selective drugs was a failure owing to their cardiovascular side effects. Alternative tothose drugs are natural products, which offer a great hope for the identification of bioactive leadcompounds and their development into drugs.Stilbenes are a group of phytochemicals that can be commonly found in higher plants with awide global distribution. Experimental studies have indicated their multiple chemopreventiveproperties and anti-inflammatory activity [1, 2]. Moreover, various laboratories are interested inthe design, synthesis, and biological evaluation of cis-Stilbene derivatives. All the world’s majorpharmaceutical and biotechnology companies use computational design tools. The ligand-basedcomputer-aided drug discovery (LB-CADD) approach involves the analysis of ligands known tointeract with a target of interest.

The studies presented in this work describe a series of cis-stilbene analogs selected in litera-ture for their anti-inflammatory effect. We used the Canvas module of Schrödinger software[3], which allows for novel QSAR methodologies to provide predictive and interpretable models.The results obtained will be used in understanding the possible structural modification of thesemolecules to improve their inhibitory potency.

References:

A. Cassidy, B. Hanley, R. M. Lamuela Raventos. J. Sci. Food Agric. 2000; 80:1044-1062.

P. Fresco, F. Borges, C. Diniz, M. P. Marques. Med. Res. Rev. 2006; 26:747–766.Canvas, Version 2.2, Schrödinger, LLC, NY2014.

∗Intervenant

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FOCUSED LIGAND LIBRARIES FORCOVALENT DOCKING ON SERINE

ACTIVE PROTEINS

Virginie Martiny ∗ 1,2, Edithe Selwa 1, Hanna Debiec 2, Pierre Ronco 2,Bogdan Iorga 1

1 Institut de Chimie des Substances Naturelles, CNRS UPR 2301, LabEx LERMIT – CNRS : UPR2301– Avenue de la Terrasse, Bat. 27, 91198 Gif-sur-Yvette, France

2 Laboratoire « Des maladies rénales rares aux maladies fréquentes, remodelage et réparation »,INSERM

UMRS1155−−Inserm : UMRS1155−−HpitalTenon, 4ruedelaChine, 75970ParisCedex20, F rance

The use of covalent inhibitors has known a very important development in recent years [1-4].Currently, 30 % of the molecules approved for clinical use present a covalent mechanism [3]. Thecovalent docking technique also became more and more popular, being now implemented in mostmajor software packages (GOLD, Glide, AutoDock, ICM, MOE, etc.) and on several dedicatedweb servers (DOCKovalent, Covalent Dock Cloud, etc.). A major limitation that hampers theuse of covalent docking on large scale is the availability of chemical libraries with ligands preparedin a particular format, compatible with this technique. Indeed, the existing chemical librariescannot be used directly in covalent docking and require a special preparation for this specificpurpose.In this work, we have generated focused ligand libraries, compatible with the GOLD dockingprogram, suitable for covalent docking on serine active proteins. For this purpose, we collectedfrom the literature and available databases (PDB, DrugBank, etc.) all known structural patternsallowing a covalent binding with a serine active residue. In order to avoid the side effects due tonon-specific interactions, an additional filter was applied and we kept only the functional groupswith a low to moderate electrophilic reactivity. A number of 18 structural patterns were thusselected, then translated into SMARTS strings that were further used for substructure search inthe ZINC databank ( 22 millions compounds). In this way, we identified commercially availablemolecules possessing the pattern of interest and constituted one focused library per pattern. Allidentified compounds were converted into a form suitable for covalent docking using SMIRKSreactions and in-house developed scripts based on the CACTVS Chemoinformatics Toolkit [5].These focused libraries of covalent ligands are currently being used for virtual screening cam-paigns on serine active proteins of therapeutic interest.

Singh J., Petter R.C., Baillie T.A., Whitty A. The resurgence of covalent drugs. Nat. Rev. DrugDiscov. 2011;10(4):307-17

Bachovchin D.A., Cravatt B.F. The pharmacological landscape and therapeutic potential of ser-ine hydrolases. Nat. Rev. Drug. Discov. 2012;11(1):52-68∗Intervenant

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Johnson D.S., Weerapana E., Cravatt B.F. Strategies for discovering and derisking covalent, ir-reversible enzyme inhibitors. Future Med. Chem. 2012;2(6):949-964

Maryanoff B.E., Costanzo M.J. Inhibitors of proteases and amide hydrolases that employ analpha-ketoheterocycle as a key enabling functionality. Bioorg. Med. Chem. 2008;16(4):1562-95

Ihlenfeldt W.D., Takahashi Y., Abe H., Sasaki S. I. Computation and management of chemicalproperties in CACTVS: An extensible networked approach toward modularity and compatibility.J. Chem. Inf. Comp. Sci. 1994;34:109-116 (www.xemistry.com).

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Inhibition sélective des ADN glycosylases :simulation par dynamique moléculaire etdocking pour la recherche et la conception

d’inhibiteurs

Charlotte Rieux ∗ 1, Franck Coste 1, Françoise Culard 1, Stéphane Goffinont1, Stéphane Bourg 1,2, Bertrand Castaing 1, Norbert Garnier 1

1 Centre de biophysique moléculaire (CBM) – CNRS : UPR4301 – Rue Charles Sadron 45071ORLEANS CEDEX 2, France


Les organismes vivants sont constamment soumis aux effets de facteurs endogènes ou ex-ogènes tels que des agents chimiques (oxydants et alkylants) ou physiques (radiations UV etgamma) qui peuvent endommager leur ADN. Pour survivre sans contrepartie aux effets néfastesde ces lésions de l’ADN, des mécanismes moléculaires de réparation ont été mis en place, no-tamment le système de réparation par excision de base (BER). Les ADN-glycosylases sont lesenzymes qui participent au BER en détectant les bases altérées de l’ADN (oxydées, alkyléeset/ou dégradées) telle que la 8-oxoguanine (une des lésions extrêmement mutagène) [1].Bien qu’impliqué dans la stabilité génétique des organismes, le système BER peut cependant in-duire une résistance aux traitements par radiothérapie. En effet, ce type de traitement entrainedes lésions oxydatives sur l’ADN qui peuvent être prises en charge par le BER. La recherched’inhibiteurs des enzymes du BER présente donc un enjeu thérapeutique dans la lutte contre lecancer.

Dans ce cadre, nous avons choisi de cibler les ADN glycosylases. Ces enzymes initient le systèmeBER. Notre étude se focalise en particulier sur les ADN glycosylases de la superfamille H2TH(Hélice 2 Tours Hélice) telles que les protéines bactériennes Fpg, Nei et les protéines humaineshNeil1, hNeil2 et hNeil3 qui réparent les pyrimidines et purines oxydées [1]. Dans un premiertemps, nous nous sommes intéressés aux mécanismes d’interaction de ces enzymes libres ou com-plexées à l’ADN endommagé, avec des ligands de types nucléobases récemment identifiés commedes inhibiteurs. Pour cela nous utilisons principalement des simulations de dynamique molécu-laire et des méthodes d’amarrage moléculaire. Les résultats préliminaires seront alors confrontésaux nombreuses données structurales et fonctionnelles de l’inhibition de ces enzymes disponiblesdans la littérature [2, 3]. L’objectif à plus long terme de cette étude est de mettre en placeune méthode de tri moléculaire virtuel pour cribler des banques de molécules synthétiques ounaturelles afin de faciliter la recherche d’inhibiteurs.

Prakash, A.; Doublié, S.; Wallace, S.S. (2012)

∗Intervenant

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The Fpg/Nei Family of DNA Glycosylases : Substrates, Structures and Search of Damage.Prog Mol Biol Transl Sci. 110, 71-91.

Biela, A.; Coste, F.; Culard, F.; Guerin, M.; Goffinont, S.; Gasteiger, K.; Cieśla, J.; Winczura,A.; Kazimierczuk, Z.; Gasparutto, D.; Carell, T.; Tudek, B.; Castaing, B. (2014)

Zinc finger oxidation of Fpg/Nei DNA glycosylases by 2-thioxanthine: biochemical and X-raystructural characterization. Nucleic Acids Res. 42(16), 10748-10761.

Jacobs, A.C.; Calkins, M.J.; Jadhav, A.; Dorjsuren, D.; Maloney D.; Simeonov, A.; Jaruga, P.;Dizdaroglu, M.; McCullough, A.K.; Lloyd, R.S. (2013)Inhibition of DNA glycosylases via small molecule purine analogs. PLoS One. 8(12), e81667

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Spectroscopic Fingerprints of the StructuralFluctuations of a Single Amino Acid

Fatima Barakat ∗ 1, Patrice Delarue† 1, Patrick Senet‡ 1

1 Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB) – CNRS : UMR6303, Université deBourgogne – 9 avenue Alain Savary, BP 47870, 21078 Dijon Cedex, France

Thanks to the nanoscience revolution, nowadays it is possible to study a single amino-acidexperimentally by using the Surface Enhanced Raman Spectroscopy. In contrast to bulk Ramanmeasurements that average over an ensemble of molecules, single-molecule spectroscopy on asingle amino acid produces a set of high-resolution heterogeneous spectra. Several reasons mightbe the cause of the heterogeneity of the spectra as the experimental noise, the amino-acid inter-actions with the SERS nanodevice and the (intrinsic) conformational fluctuations of the aminoacid. To examine the latter hypothesis from a theoretical point of view, classical infrared spectraas well as Raman spectra were computed for blocked hydrated amino-acids. One thousand con-formations of hydrated amino-acids were selected every 100 ps from classical molecular dynamicstrajectories of 100 ns of duration at 300 K in explicit solvent (water). Normal mode analysiswas performed for every conformation and the infrared and Raman spectra were computed. Thecalculations produce heterogeneous ensembles of infrared and Raman spectra, similar to thosemeasured in SERS. The fluctuations of the spectra relative to the average spectrum were studiedby using principal component analysis. The role of the hydration on the fluctuations was clearlyidentified. A correlation between the amino-acid conformational fluctuations and the fluctua-tions of the spectra was rationalized by calculating an effective local force constant on each of theatom. The present work strongly supports that the origin of the time-dependent fluctuations ofthe Raman spectra observed in SERS are mainly due to the multiple minima of the free-energysurface of a single amino acid in solution.


111




Unravelling the structure/activity relationshipbetween collagen derived peptides and the v3

integrin

Eléonore Lambert 1, Manuel Dauchez 1,2, Sylvie Pasco-Brassart 1,Stéphanie Baud ∗† 1,2

1 UMR CNRS/URCA 7369 (MEDyC), Reims (1) – Université de Reims - Champagne Ardenne, CNRS :UMR7369 – Moulin de la Housse, 51687 Reims Cedex 2, France

2 Plateau de Modélisation Moléculaire Multiéchelle (P3M), Reims (2) – Université de Reims -Champagne Ardenne, CNRS : UMR7369 – Moulin de la Housse, 51687 Reims Cedex 2, France

The Extra Cellular Matrix (ECM) is a connective tissue formed by a complex network ofmacromolecules. Upon degradation by Matrix MetalloProteinases (MMP), elastases or collage-nases, the ECM releases bio-active fragments named matrikines. In particular, the NC1 domainof the a4 chain of collagen IV, named tetrastatin was demonstrated to inhibit in vitro melanomacell proliferation and invasion and in vivo tumor growth through V3 integrinbinding. The inter-action with the integrin V3 was demonstrated by Surface Plasmon Resonance [1].The aim of the present in silico study is to gain insight into the molecular and atomistic aspectsof the interaction between tetrastatin-derived peptides and the V3 integrin. Since it was possibleto identify the tetrastatin minimal active sequence QKISRCQVCVKYS (corresponding to thepeptide named QS-13), we are characterizing V3/QS-13 interactions using multiple moleculardocking experiments and statistical analyses.

We are then able to identify five “preferred areas of interactions” as well as the key residuesimplicated in the interactions. In addition, our results demonstrate the important role playedby the presence of a disulfide bridge in QS-13 peptide.1. Brassart-Pasco, S., Sénéchal, K., Thevenard, J., Ramont, L., Devy, J., Di Stefano, L., Dupont-Deshorgue, A., Brézillon, S., Feru, J., Jazeron, J. F., Diebold, M. D., Ricard-Blum, S., Maquart,F. X., and Monboisse, J. C. (2012). Tetrastatin, the nc1 domain of the 4(iv) collagen chain: anovel potent anti-tumor matrikine. PLoS One, 7(4)


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Mécanisme d’activation du récepteurT1R2-T1R3 impliqué dans la perception du

goût sucré.

Jean-Baptiste Cheron ∗ 1, Jérôme Golebiowski 1, Serge Antonczak 1,Sébastien Fiorucci† 1

1 Institut de Chimie de Nice (ICN) – CNRS : UMR7272, Université Nice Sophia Antipolis [UNS],Université Nice Sophia Antipolis (UNS) – Faculté des Sciences Parc Valrose 28 Avenue Valrose 06108

Nice cedex 2, France

La stimulation chimique du récepteur T1R2-T1R3 est à l’origine de la perception du goûtsucré. Ce Récepteur Couplé aux Protéines G (RCPG) de classe C est un hétérodimère carac-térisé par un grand domaine N-terminal appelé Venus Flytrap Domain (VFD) lié au domaine à7 segments transmembranaire (7TM) par un domaine riche en cystéines (CRD). Il a été montréque les molécules influençant l’activité du récepteur interagissent avec les différents domaines [1]: les agonistes naturels lient le site orthosterique localisé dans le VFD de T1R2, les modulateursallostériques interagissent avec le domaine à 7TM de T1R3 et les protéines à pouvoir sucrantlient le CRD de T1R3. Malgré la connaissance de ces sites de liaison, les mécanismes moléculairesimpliqués dans la transmission du signal d’activation du VFD de T1R2 à la partie cytosoliquedu domaine à 7TM reste à élucider. Dans cette perspective d’étude in silico, l’obtention de lastructure 3D du récepteur est nécessaire pour caractériser la relation structure-activité impliquéedans la perception du goût sucré et in fine définir de nouveaux édulcorants. L’absence de don-nées structurales sur le récepteur T1R2-T1R3 requière une étape de modélisation par homologieà l’aide de contraintes expérimentales : i) les données cristallographiques disponibles du récep-teur métabotropique au glutamate (RCPG de classe C) [2,3] ii) les régions d’interactions desdomaines à 7TM [4] iii) les résidus clés définis par mutagenèses dirigées [5,6]. Les simulations dedynamiques moléculaires et les calculs de modes normaux sont utilisés pour décrire les processusmoléculaires impliqués dans la liaison des ligands et le mécanisme d’activation du récepteur.

Références:Behrens M. et al., Angew. Chem. Int. Ed, 2011, 50, 2220-2242.Wu H. et al., Science, 2014, 344 (6179), 58-64.Doré A.S. et al., Nature, 2014, 511, 557–562.

Xue L. et al., Nat. Chem. Biol., 2015, 11, 134-140.

Zhang F. et al., PNAS, 2010, 107(10), 4752-4757.


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Caractérisation du paysage énergétique debiomolécules flexibles avec un couplage

d’algorithmes stochastiques

Juan Cortes ∗ 1

1 LAAS-CNRS (LAAS-CNRS) – CNRS : UPR8001 – France

La caractérisation du paysage énergétique de biomolécules flexibles est essentielle pour l’analysede ses propriétés physico-chimiques et ses fonctions biologiques. Les peptides sont un cas d’étudeparticulièrement intéressant, car ces molécules exploitent leur flexibilité structurale pour mod-uler leur fonction. Malgré leur petite taille (par rapport aux protéines), la modélisation despeptides reste un sujet difficile à cause de l’existence d’une multitude d’états métastables et detransitions possibles entre eux. Nous proposons une approche basée sur le couplage d’algorithmesstochastiques. Premièrement, une version simple de l’algorithme Basin Hopping est utilisée pouréchantillonner des régions de basse énergie de manière à identifier un ensemble d’états métasta-bles. Ensuite, plusieurs variantes d’un algorithme issu de la robotique, appelé Transition-basedRapidly-exploring Random Tree (T-RRT), sont proposées pour calculer de manière efficace leschemins de transition entre ces états. L’approche est illustrée sur la met-enképhaline.

∗Intervenant

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Modeling methods for protein structureprediction, mechanistic and docking studies

Serge Crouzy ∗† 1

1 Laboratoire de Chimie et Biologie des Métaux (LCBM) – CEA, Université Joseph Fourier - GrenobleI, CNRS : UMR5249 – 17, rue des Martyrs 38054 GRENOBLE CEDEX 9, France

Nous nous efforçons, dans notre petit groupe, de répondre à des problèmes expérimentauxpar des modèles théoriques et des simulations. Il s’agit soit de modèles structuraux basés surla mécanique ou la dynamique moléculaire, soit de mécanismes réactionnels basés sur les calculsd’énergie libre ou la chimie thèorique. Depuis les modèles “ gros-grain ” jusqu’à une descriptionélectronique, nos modèles couvrent une large gamme de temps (ps à ms) et d’espace (nm à µm).Nous présentons brièvement cinq résultats récents couvrant ces domaines à l’exception du niveauélectronique.1- La structure X de NccX, un senseur transmembranaire de métal, a été déterminée à une réso-lution de 3,12 Å en détergent. Cette structure ne correspond pas au modèle basé sur le domainepériplasmique largement caractérisé de son homologue le plus proche CnrX. Des simulations dedynamique moléculaire “ gros-grain ” de NccX immergée dans une bicouche de phospholipidesmodèles ont permis d’isoler le segment transmembranaire de la protéine malencontreusementattaché à une région hydrophobe dans la structure en détergent.2- Les Récepteurs Couplés auxCanaux Ioniques (ICCR) sont des protéines artificielles construites à partir d’un récepteur cou-plé à la protéine G et d’un canal ionique, utilisés comme biocapteurs moléculaires. Le canalpotassique rectifiant entrant Kir6.2 a ainsi été couplé avec succès au récepteur muscarinique M2ou dopaminergique D2. Le mécanisme moléculaire expliquant la différence de comportement(courant ionique à travers le canal) entre les 2 ICCR après liaison de leur activateur respec-tif reste à élucider. En particulier, il était important de déterminer comment l’activation del’acétylcholine M2 par son agoniste déclenche la modulation du canal Kir6.2 via la liaison M2-Kir6.2. Nous avons développé et validé une approche computationnelle permettant de construiredes modèles de M2-Kir6.2 qui a produit deux modèles possibles correspondant à deux orientationsdifférentes de M2. 3- La protéine FUR (régulateur de l’absorption ferrique) est un régulateurtranscriptionnel global spécifique des bactéries. C’est une cible de choix pour le développementde molécules visant à réduire la virulence des bactéries sans développer des résistances. Des pep-tides anti-FUR de 13 acides aminés ont été identifiés, capables d’interagir avec FUR d’Escherichiacoliet ainsi inhiber sa liaison à l’ADN conduisant à sa pathogénicité. Une combinaison originaled’approches théoriques (“ docking ” et DM) et expérimentales a été utilisée pour étudier le mé-canisme d’inhibition de FUR par ces peptides.4- L’ATPase à cuivre humaine ATP7A (Menkès)est essentielle pour l’homéostasie intracellulaire du cuivre. Le domaine N-terminal de la protéinese compose de six séquences répétitives de 60-70 acides aminés (Mnk1-Mnk6) qui se replienten domaines de liaison métallique individuels (MBD) liant un ion Cu(I) grâce à deux résiduscystéine. La structure de chaque MBD est connue par RMN et nous nous sommes intéressés∗Intervenant†Auteur correspondant:

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mailto:

à la stabilité et la dynamique de chaque MBD isolé dans sa forme apo et holo ainsi qu’à leursinteractions avec la métallochaperone soluble Hah1 qui délivre le cuivre à ATP7A. Nous avonsparamétrisé le métal dans ces protéines et réalisé des simulations de DM dans ce but.5- Lesinteractions entre la -cyclodextrine, (BCD) et le chlorure de p-toluènesulfonyl (TsCl) ont étéétudiées en utilisant des simulations de DM, dans le vide, se rapprochant de l’environnement dela cavité hydrophobe de la CD, et en présence d’eau. Dans les deux cas, les chemins d’énergieadiabatique minimale, et les potentiels de force moyenne (profils d’énergie libre) pour l’insertiondes TsCl le long d’une coordonnée de réaction perpendiculaire au plan de la CD, ont été calculéspour les deux orientations possibles de TsCl. Les résultats montrent une entrée préférentielle deTsCl dans la cavité de CD par la face à hydroxyles primaires en excellent accord avec les donnéesde RMN.

1- Ziani W, Maillard AP, Petit-Härtlein I, Garnier N, Crouzy S, Girard E. and Covès J. JBiol Chem. 289 31160-31172 (2014).

2- Sapay N., Estrada A., Moreau C., Vivaudou M. and Crouzy S. Proteins 82 1694-1707 (2014)

3- Cissé C., Mathieu S.V., Abeih M.B., Flanagan L., Vitale S., Catty P., Boturyn D., Michaud-Soret I.. and Crouzy S. ACS Chem Biol . 9 2779-2786 (2014)

4- Arumugam K. and Crouzy S. Biochemistry. 51 8885-8906 (2012)5- Law H., Benito J.M., Garcia Fernandez J.M., Jicsinszky J., Crouzy S. and Defaye J. J PhysChem B. 115, 7524-7532. (2011)

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Mitochondrial membrane fusion:computational modelling of mitofusins

Dario De Vecchis ∗† 1, Antoine Taly 1, Marc Baaden 1, Jérôme Hénin 1

1 Institut de biologie physico-chimique (IBPC) – CNRS : FRC550 – 13 Rue Pierre et Marie Curie 75005PARIS, France

Mitochondria are highly dynamic organelles that continuously alter their shape, rangingfrom a filamentous network to a collection of isolated fragments. This process is controlledby fusion-fission equilibrium which is a largely unexplored topic despite its importance in cellfunction, evolution and ageing. Fusion of mitochondrial outer membranes (MOM) involves mito-fusins, large GTPases of the Dynamin-Related Proteins (DRPs) superfamily, one of which isFzo1. Although several hypotheses have been proposed, many aspects of Fzo1 function are yetdebated, and many important questions remain unanswered. Recent data indicate that theGTPase domain of Fzo1 would induce a conformational switch from a closed to an open stateconcomitant with mitochondrial tethering. Such a conformational switch resembles that of thebacterial dynamin-like protein (BDLP), a close relative of Fzo1, whose structure is known in twoconformational states: binding of GTP analogues by BDLP results in a switch from a compactstructure, seen with bound GDP, to an open conformation that stimulates BDLP oligomerizationand favours its ability to shape the morphology of liposomes. BDLP structural properties andFzo1 behaviour during MOM fusion suggest that mitofusins may promote membrane fusion byswitching from closed to open conformation, although the molecular fusion mechanism remainsunknown. In this project we use homology modelling approach and atomistic simulations inclose link to experiment to study Fzo1 based on the open and closed BDLP crystal structures.The refined models may generate hypotheses for the conformational transition between open andclosed forms and predict the phenotype of interesting mutations to be tested and performed byMickael Cohen lab, as a part of the collaboration. Since sequence and function of mitofusins areconserved throughout evolution, our approach may unravel novel functional domains and pro-vide fundamental insights about molecular mechanisms by which mitofusins and more generallyDRPs, catalyse membrane fusion.


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UnityMol: Simulation et VisualisationInteractive à des fins d’Enseignement et de

Recherche

Sébastien Doutreligne ∗ 1, Cédric Gageat 1, Tristan Cragnolini 1, AntoineTaly 1, Samuela Pasquali 1, Marc Baaden 1, Philippe Derreumaux 1

1 Laboratoire de biochimie théorique (LBT) – CNRS : UPR9080, Université Paris VII - Paris Diderot –13 Rue Pierre et Marie Curie 75005 PARIS, France

Nous présentons UnityMol, une base logicielle pour développer des solutions de visualisation,analyse et exploration de données biologiques. Une des originalités de UnityMol est son implé-mentation à travers un moteur de jeu vidéo. Nous venons de connecter ce logiciel de visualisationà des simulations de dynamique moléculaire pour interagir directement avec la molécule d’intérêt.Cette manipulation apporte une nouvelle dimension très concrète et intuitive à l’exploration dela structure des biomolécules. Ici nous présentons nos essais sur les molécules d’ARN. Nousutilisons un moteur de simulation qui implémente le modèle gros-grain HiRE-RNA développéau laboratoire. Une des applications possibles est l’enseignement. A présent, UnityMol a étéutilisé avec des groupes d’étudiants de Licence en Biologie et en Bioinformatique, soit un totalde près de 100 personnes. Ces expériences créent un contact concret avec les simulations etleur fonctionnement et nous permettent de tester et valider nos choix techniques pour ensuiteproposer un espace d’expérimentation virtuel pour la recherche.

∗Intervenant

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Study of TIMP-1/LRP-1 interaction in neurons: a new

approach based on protein dynamic

Laurie Verzeaux *1, Nicolas Belloy 2, 3, Hervé Emonard 1, Manuel Dauchez 2, 3, Laurent Martiny 1,

Nicolas Etique 1 and Emmanuelle Charpentier 1

1 – Unité MeDyC UMR CNRS URCA 7369, Equipe “Protéolyse et cancer”, Reims 2 – Unité MeDyC UMR CNRS URCA 7369, Equipe “Vieillissement matriciel et Remodelage vasculaire”, Reims 3 – Plateau de Modélisation Moléculaire Multi-échelle (P3M), Maison de la Simulation de Champagne-Ardenne

(MaSCA), Reims

TIMP-1, a member of the Tissue Inhibitor of Metalloproteinases (TIMPs) family, has been first characterized for its ability to inhibit Matrix Metalloproteinases (MMPs) and can also act as a cytokine by binding to cell surface complex receptors. In mouse cortical neurons, we have recently identified the LDL receptor-related protein-1 (LRP-1) as a new receptor for TIMP-1. The binding of TIMP-1 to LRP-1 reduces neurite outgrowth and modulates growth cone morphology. The characterization of TIMP-1/LRP-1 interaction could help to develop new therapeutic strategies in neurodegenerative diseases. Whether the TIMP1 structure is well characterized, the lack of structural data about LRP1 doesn’t allow the use of molecular docking algorithms. Based on the observation that ligand/receptor interaction requires conformational changes of both partners, we have hypothesized that understanding the intrinsic dynamic of TIMP-1 could help us to highlight regions that govern its interaction with LRP-1. Normal Modes Analysis (NMA) and/or Molecular Dynamics (MD) simulations are usefulness in silico methods to study protein dynamics. In this study, we used these theoretical methods in conjunction with experimental data to identify residues or regions that might be involved in TIMP-1/LRP-1 interaction and signaling.

Normal Mode Analysis led us to identify a movement of the N-terminal domain relative to the

C-terminal domain which could be assimilated to a clamp movement. In these modes, study of residues deformation energy has highlighted a cavity which could govern this movement and potentially its interaction with LRP-1. Molecular dynamic studies of TIMP-1 and particularly carbon α atomic fluctuation representation have also designed this cavity as essential for the TIMP-1 intrinsic dynamic. In this area, three residues have been pointed out and mutated into alanine in order to disturb the movement. The three single mutants have been produced and purified by affinity chromatography. Determination of both Ki (inhibitory constant) and Kon (association constant) revealed that the three mutants conserved their inhibitory effects towards MMP-1, -2, -3 and -9. As expected, the three single mutants are not able to reduce neurite length in mouse cortical neurons. Surprisingly, surface plasmon resonance analyses show that the three mutants are able to bind LRP-1. However, the LRP-1-mediated endocytosis, evaluated by confocal microscopy, is altered for the three mutants.

In this study, we have identified a TIMP-1 region essential for LRP-1-mediated endocytosis and

associated biological effects in neurons. Our findings could help to design molecules that specifically block the cytokine-like effects of TIMP-1 through LRP-1 and open new perspectives in


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neurodegenerative disease therapy. This work also suggests the utility of our in silico approach in other topics where there is a lack of structural data for studying ligand-receptor interactions.

____________________________ * Intervenant


SA-conf : un outil d’analyse et de comparaisonde la variabilité de séquences et de structuresd’un ensemble de conformères d’une protéine

Leslie Regad 1, Jean-Baptiste Cheron 2, Caroline Senac 3, Delphine Flatters∗ 1, Anne-Claude Camproux 1

1 Recherche de molécules à visée thérapeutique par approches In Silico (MTI) – Inserm : U973,Université Paris VII - Paris Diderot – Batiment Lamarck A (case 7113), 35 rue Hélène Brion 75205

PARIS CEDEX 13, France2 Institut de Chimie de Nice (UMR 7272 CNRS) – CNRS : UMR7272, Université Nice Sophia Antipolis

– Parc Valrose, 06108 Nice Cedex 2, France3 Laboratoire d’imagerie biomédicale – CNRS : UMR7371, Inserm : UMRS1146, Université Paris VI -Pierre et Marie Curie – Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire

d’imagerie biomédicale, F-75006 Paris, France

Actuellement, plus de 100.000 structures protéiques sont disponibles dans la Protein DataBank (PDB). La PDB inclut souvent plusieurs conformères correspondant à une même pro-téine obtenue dans différentes conditions expérimentales, par exemple par cristallographie ouRMN, complexée ou non avec un partenaire (protéine, un ligand, un ADN/ARN), sous uneforme mutante ou sauvage. Ces différents conformères présentent des variations structuralessubtiles pouvant illustrer la “ plasticité ” de la protéine. A l’heure actuelle, il n’existe pas d’outildisponible d’analyse fine, systématique et rapide d’un ensemble de conformères. Or cette anal-yse permettrait (i) d’identifier les régions structurellement variables pouvant être associées à laplasticité d’une protéine, (ii) d’aider à la détermination des régions importantes pour la fonctionbiologique de la protéine.Dans ce contexte, nous avons développé un outil, nommé SA-conf, permettant d’étudier et decomparer un ensemble de conformères d’une protéine et analyser leur diversité de séquences et destructure. Tout d’abord, SA-conf offre une description de chaque conformère en termes de méth-ode expérimentale, de résolution, de nombre et taille de(s) chaîne(s) le composant. Puis, SA-confconstruit un alignement multiple des séquences (AMS) extraites des différents conformères. Afinde proposer une comparaison entre la variabilité des séquences et de structures locales des struc-tures, SA-conf construit un alignement des structures locales extraites des différents conformères.L’extraction des structures locales est basée sur un l’alphabet structural HMM-SA (Camprouxet al., 2004) qui permet de simplifier chaque conformère en une séquence de lettres structurales(LS) où chaque LS correspond à la géométrie d’un fragment de 4 résidus, i.e. structure locale durésidu en prenant en compte son environnement. Chaque acide aminé du AMS est ensuite rem-placé par la LS correspondante produisant ainsi l’alignement des conformations locales (ACL).

L’analyse du AMS permet de caractériser les séquences du jeu de conformères en identifiant lesséquences mutantes, celles qui contiennent des insertions/délétions ou des régions non résolueset de différencier les séquences provenant de différents organismes. L’analyse des ACL permet∗Intervenant

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d’identifier les conformères ayant des particularités structurales, les régions et positions struc-turellement variables, i.e positions pour lesquelles plusieurs conformations locales sont observéessur ACL. Cette variabilité structurale des conformères peut alors être analyser/interpréter enfonction des différentes conditions expérimentales, comme par exemple la présence d’un parte-naire (ligands, protéines, ...), la présence de mutations, .... De plus, SA-conf produit des représen-tations graphiques qui permettent de localiser ces positions variables et conservées sur la structuretri-dimensionnelle de la protéine.En conclusion SA-conf est un outil rapide d’analyse et de comparaison d’un ensemble des con-formères d’une protéine, en termes de séquence et structure. SA-conf permet à l’utilisateurd’identifier les séquences mutées, et de différencier les séquences provenant de différents organ-ismes. SA-conf est aussi un outil efficace pour étudier la diversité structurale locale des différentsconformères d’une protéine ce qui permet une première analyse rapide de la plasticité de cetteprotéine. L’analyse conjointe des conformères obtenus dans différentes conditions expérimentaleset des régions variables de la protéine permet d’apporter des premiers éléments de réponse sur lesraisons expliquant cette plasticité : flexibilité intrinsèque, mutations, fixation d’un partenaire,et ainsi d’identifier des régions importantes pour la fonction biologique de la protéine.

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Exploration of Large Amplitude Motions ofGPCRs and their complexes at the molecular

level

Nicolas Floquet ∗ 1, Mauricio Costa 2, Marjorie Damian 1, Sophie Mary 1,Jean-Alain Fehrentz 1, Jacky Marie 1, Jean Martinez 1, Jean-Louis Banarès

1, David Perahia 3

1 Institut des Biomolécules Max Mousseron (IBMM) – CNRS : UMR5247, Université de Montpellier –Faculté de Pharmacie, 15 Av. Charles Flahault, BP 14491, 34093 Montpellier, Cedex 05, France

2 Programa de Computação Científica, Fundação Oswaldo Cruz, 21949900, Rio de Janeiro, Brazil. –Brésil

3 Laboratoire de Biotechnologie et Pharmacologie génétique Appliquée (LBPA) – CNRS : UMR8113,École normale supérieure (ENS) - Cachan – 61 AVENUE DU PRESIDENT WILSON 94235 CACHAN

CEDEX, France

Class-A G-Proteins Coupled Receptors (GPCRs) and G-Proteins together form protein:proteincomplexes that mediate a large panel of physiological responses, and therefore constitute attrac-tive targets for the design of new drugs. The number of available X-ray structures describingeither isolated, or complexed GPCRs has considerably increased since 2007. However, and despiteof this, the crucial question of the activation mechanism of these large protein:protein complexesat the molecular level is still controversial. In the literature, the conformational change observedbetween the isolated and the G-protein complexed receptor is still described as the holy-grail.However, many experimental data have now evidenced the existence of G-Proteins pre-coupledGPCRs, suggesting that the activation mechanism is a key feature of the complex and not of theisolated, uncoupled, partners. In agreement, beyond frozen X-ray data, a better understanding ofthe dynamical behavior of these large molecular assemblies is of crucial importance for the designof more specific drugs, with more controlled effects. In a recent paper, we used Normal ModesAnalyses in vacuo, to show that the conformational diversity of GPCRs is considerably reducedupon complexation to G-proteins [1]. These results even pointed to a unique motion possiblybeing the activation motion of GPCRs in the complex. Here we used the “Molecular Dynamicswith Excited Normal Modes” (MDeNM) method to explore such a large amplitude motion of thecomplex with an explicit representation of the surrounding medium (membrane+water). Amongthe lowest frequencies Normal Modes of the complex, one motion was in particularly good agree-ment with experimental results obtained in our laboratory on the Ghrelin Receptor/Gq modeland showing the increase/decrease of up to 10 Angströms of both intra- and inter-moleculardistances in the protein:protein complex [2]. These data provide direct evidence of a mechanismin which transition of the receptor from an inactive to an active conformation is accompanied bya large conformational rearrangement of a preassembled receptor: G protein complex, ultimatelyleading to G protein activation and signaling.1. Louet M, Karakas E, Perret A, Perahia D, Martinez J, and Floquet N. (2013) Conformational∗Intervenant

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restriction of G-proteins Coupled Receptors (GPCRs) upon complexation to G-proteins: a pu-tative activation mode of GPCRs? FEBS Lett. 587(16):2656-61.2. Damian M, Mary S, Maingot M, M’Kadmi C, Gagne D, Leyris JP, Denoyelle S, Gaibelet G,Gavara L, Garcia de Souza Costa M, Perahia D, Trinquet E, Mouillac B, Galandrin S, Galès C,Fehrentz JA, Floquet N, Martinez J, Marie J, Banères JL. (2015) Ghrelin receptor conforma-tional dynamics regulate the transition from a preassembled to an active receptor:Gq complex.Proc Natl Acad Sci U S A. 112(5):1601-6.

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HiRE-RNA: un modèle gros grain pour lasimulation d’ARN et d’ADN

Cédric Gageat ∗ 1, Samuela Pasquali 1, Philippe Derreumaux 1

1 Laboratoire de biochimie théorique (LBT) – CNRS : UPR9080, Université Paris VII - Paris Diderot –13 Rue Pierre et Marie Curie 75005 PARIS, France

Malgré que l’ARN joue de nombreux rôles au sein des cellules, nous disposons de très peude structures 3D hautes définitions. Du fait de la grande flexibilité des ARN, l’obtention destructures par RMN ou rayon X reste un défi actuel. Des modèles issus de prédictions théoriquessont donc proposés afin de combler le manque de données expérimentales. Malheureusement, lesmodèles tout-atome, sont eux aussi limités par les puissances de calcul actuelles, ce qui permetuniquement l’étude de petits systèmes.Pour permettre l’étude de plus gros systèmes d’intérêts biologiques, un modèle gros grain hauterésolution, HiRE-RNA [1, 2], a été développé dans notre laboratoire. Ce modèle a été testé etvalidé en couplage avec différentes techniques d’échantillonnage de conformations (Dynamiquemoléculaire simple, avec échange de répliques, interactive, ...). Nous présentons ici notre modèleet ses termes énergétiques au travers de ses deux spécialisations : la première pour l’ARN, laseconde pour l’ADN. Nous présentons également l’étude d’une large molécule d’ADN (178 nu-cléotides) [3] ainsi que l’étude de petites molécules à la géométrie complexe : des quadruplexes.

Cragnolini T, Laurin Y, Derreumaux P, Pasquali S, The coarse-grained HiRE-RNA model forde novo calculations of RNA free energy surfaces, fodling pathways and complex structure pre-diction. Submitted

Cragnolini T, Derreumaux P, Pasquali S, Coarse-grained simulations of RNA and DNA duplexes.J Phys Chem B. 2013 ;117(27):8047-60Kanevsky I, Chaminade F, Chen Y, Godet J, René B, Darlix J-L, Mély Y, Mauffret O, FosséP, Structural determinants of TAR RNA-DNA annealing in the absence and presence of HIV-1nucleocapsid protein. Nucl. Acids Res. 2011;39 (18):8148-8162.

∗Intervenant

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PEPSI-SAXS - A very fast adaptive scheme forcomputation of small-angle X-ray scatteringprofiles via spherical harmonics expansions

Sergei Grudinin ∗† 1, Maria Garkavenko 2, Andrei Kazennov 2, TeamNano-D 1

1 NANO-D (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann) – Institutpolytechnique de Grenoble (Grenoble INP), Université Joseph Fourier - Grenoble I, CNRS : UMR5224,Laboratoire Jean Kuntzmann, INRIA – Inria GIANT DRT/LETI/DACLE Batiment 51C - Minatec

Campus 17 rue des Martyrs 38054 Grenoble Cedex, France2 Moscow Institute of Physics and Technology (MIPT) – 141700, 9, Institutskii per., Dolgoprudny,

Moscow Region, Russia, Russie

Small angle scattering is an important tool in structural biology. It allow to investigate thethree-dimensional structures and structural changes of biological objects. Thanks to recent ad-vances in experimental methods, the amount of experimental small-angle X-ray scattering data isincreasing dramatically [1,2]. Therefore, there is an urgent need for very efficient computationaltools to analyze this data.We present PEPSI-SAXS, a new method that uses spherical harmonics expansions, adaptiveresolution, and smooth interpolation of scattering amplitudes in the reciprocal space to rapidlycompute solution scattering of macromolecules starting from their known atomic structure. Themethod can either predict the intensity curve, or to fit the experimental scattering curve usingtwo fitting parameters, the averaged displaced volume and the contrast of the hydration layer.Additionally, our method can automatically subtract the constant background noise from exper-imental curves, which improves the fitting result.

Compared to other state-of-the-art methods such as Crysol [3], FoXS [4], and SAStbx [5], PEPSI-SAXS uses the adaptive resolution of its expansion coefficients and a very accurate model of thesolvation shell to improve the quality of the predicted scattering curve. Additionally, fast sum-mations based on the evaluation of the partial intensities and shared-memory parallelizationallows PEPSI-SAXS to significantly out-perform the other methods in computational efficiency.More precisely, on a modern laptop our method runs in 5-50 times faster compared to the othertested methods.

The PEPSI-SAXS method was exhaustively tested on all available structural models from theBIOISIS [4] and SASDB [5] databases. Table 1 provides comparison of the four tested methodslisting the average timings and quality of fit to experimental curves for 28 models from BIOISIS[1] and 23 models from SASDB [2]. As can be seen from the table, PEPSI-SAXS significantlyoutperforms all other methods both in speed and model quality. We have also developed agraphical user interface for the PEPSI-SAXS method as a module for the SAMSON modular∗Intervenant†Auteur correspondant: [email protected]

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software package developed in our team (see Figure 1), which is available at http://www.samson-connect.net.

http://www.bioisis.net.

Erica Valentini, Alexey G Kikhney, Gianpietro Previtali, Cy M Jeffries, and Dmitri I Svergun.Sasbdb, a repository for biological small-angle scattering data. Nucleic acids research, pagesD357–63, 2015.

D Svergun, C Barberato, and MHJ Koch. Crysol-a program to evaluate x-ray solution scatter-ing of biological macromolecules from atomic coordinates. Journal of applied crystallography,28(6):768–773, 1995.

Dina Schneidman-Duhovny, Michal Hammel, John A Tainer, and Andrej Sali. Accurate saxsprofile computation and its assessment by contrast variation experiments. Biophysical journal,105(4):962–974, 2013.

Haiguang Liu, Richard J Morris, Alexander Hexemer, Scott Grandison, and Peter H Zwart.Computation of small-angle scattering profiles with three-dimensional zernike polynomials. ActaCrystallographica Section A: Foundations of Crystallography, 68(2):278–285, 2012.

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Conformational changes in themophilic andmesophilic enymes

Marina Katava ∗ 1, Fabio Sterpone ∗

1

1 Laboratoire de Biochimie Théorique (LBT) – CNRS : UPR9080, Université Paris Diderot - Paris 7 –13, Rue Pierre et Marie Curie - 75005 PARIS, France

We study the conformational changes that occur during the enzymatic activity of two ho-mologous proteins from the mesophile E. coli (optimum growth at 37◦) and the thermophile S.solfataricus (optimum growth at 87◦). The enzymatic activity is energized by GTP to GDPconversion and is a necessary part of the polypeptide elongation process.

In the mesophile protein, activity is granted by a dramatic secondary structure to conver-sion occurring in a conserved region (switch I), and following the GTP/GDP catalysis [1]. Thisregion is the most thermally unstable region of the protein [2]. As no to conversion has beenobserved in the thermophilic EF-1, we are interested in the conformational changes occurring inthe analogous portion as they will help us identify the differences between the protein molecularmechanisms in the meso- and the thermophile.

We mimicked the conformational changes induced by the enzymatic reactions by performingMolecular Dynamics simulations of the g-domains in different secondary structures bound to thereactant (GTP) and product state (GDP). Results show that the protein matrix is rigid in the/GTP form, and that it becomes more flexible in the /GDP form, which could be the trigger thetransition to the state. The thermophile shows similar behavior.

The conformational changes in the switch I fragment will be better explored by means of en-hanced sampling techniques.

Jurnak et al., Structure, 1996.Sterpone et al., J. Phys. Chem. B, 2013.

∗Intervenant

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Conformational changes in thermophilic and mesophilic enzymes


Etude de la variabilité structurale et del’interaction du peptide NFL-TBS.40-63 avec

la tubuline.

Yoann Laurin ∗ 1, Philippe Savarin 2, Charles Robert 1, Chantal Prevost 1,Sophie Sacquin-Mora 1

1 Laboratoire Biochimie Théorique CNRS UPR9080 (LBT) – CNRS : UPR9080 – IBPC, 13 rue Pierreet Marie Curie, 75005 Paris, France

2 Université Paris 13, Sorbonne Paris Cité, CSPBAT, CNRS UMR7244 – CNRS : UMR7244 – 74 rueMarcel Cachin, 93017 Bobigny, France

NFL-TBS.40-63 is a 24 amino acid peptide corresponding to the tubulin-binding site locatedon the light neurofilament subunit, which selectively enters glioblastoma cells, where it disruptstheir microtubule network and inhibits their proliferation. We investigated its structural vari-ability and binding modes on a tubulin hetero-dimer using a combination of NMR experiments,docking and molecular dynamics (MD) simulations. Our results show that, while lacking a sta-ble structure, the peptide preferentially binds on a specific single site located near the -tubulinC-terminal end, thus giving us precious hints regarding the mechanism of action of the NFL-TBS.40-63 peptide’s antimitotic activity on the molecular level. Le peptide de 24 acides aminésNFL-TBS.40-63 est un Tubulin-Binding Site (TBS, site de liaison à la tubuline) localisé sur leneurofilament léger. Il pénètre spécifiquement dans les cellules des glioblastomes, où il va in-hiber la formation du réseau de microtubules. Nous avons étudié sa variabilité structurale etson interaction avec un hétérodimère de tubuline grâce à des expériences de RMN, de docking etdes simulations de dynamique moléculaire. Nos résultats montrent que, malgré l’absence d’unestructure stable, le peptide interagit préférentiellement sur un site spécifique situé aux abordsde l’extrémité C-terminale de la sous unité de la tubuline. Ces observations sont de précieuxindices afin de comprendre le mécanisme de l’action antimitotique du peptide NFL-TBS.40-63au niveau moléculaire.

∗Intervenant

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Molecular basis of Olfactory Receptors: insilico modeling and in vitro validation.

Gwenaëlle Andre-Leroux ∗† 1

1 André-Leroux (Inra MaIAGE) – Institut National de la Recherche Agronomique - INRA (FRANCE)– Unité Mathématiques et Informatique Appliquées, du Génome à l’Environnement (MaIAGE) INRA -

Bat233 Domaine de Vilvert - 78352 Jouy-en-Josas, France

Adrien Aquistapace1, Véronique Martin2, Jean-François Gibrat2, Guenhaël Sanz1 and Gwe-naëlle André-Leroux21Inra, NBO UR1197, Bât 440 & 2Inra, MaIAGE UR1404, Bât 233, Domaine de Vilvert, 78352Jouy-en-Josas

[email protected]

G protein-coupled receptors (GPCRs) are a highly versatile family of membrane proteins witha broad spectrum of functional behaviours in response to diffusible ligands. Upon activation byexternal stimuli, which can be photons, odors, pheromones, hormones and neurotransmitters,GPCRs trigger specific cellular responses and recruit downstream activators such as G proteins.The human genome accounts for as much as 3% of GPCR genes which associate with numerousbiological sensing functions including olfaction, vision, taste, pain, etc. Noteworthy, most of thehuman GPCRs are olfactory receptors (ORs), responsible for olfaction, expressed in the olfac-tory epithelium where they signal diverse odorants [1-2]. Additionally, ORs is employed to detectsteroid hormones in non-olfactory tissues, regulate serotonin secretion in the gut, cell migrationor adhesion in muscle [3-6].

Deciphering how OR functional behaviour reconciles with structural properties is a challenge,because there is no experimental structure available for ORs and because most of them are or-phans, i.e their physiological agonists are unknown. Nevertheless during the last decade, morethan 25 high-resolution X-ray structures of GPCRs have been solved, tremendously augmentinginsights into their structure/function relationships [7]. GPCRs share a conserved topology of 7transmembranes (TM) helix bundle that shows 36 topologically equivalent positions with con-served residues or motifs, referred as Ballesteros–Weinstein (BW) numbering scheme [8]. Anymutation in 14 out of 36 of these BW positions modifies the receptor activity, be it a loss or anincrease of activity. Although similarity of OR sequences can be very low compared to GPCRs,these conserved motifs could be topological markers that anchor the alignments of the TMs andparticipate in ligand binding. This hypothesis prompted us to investigate in silico the molecularbasis of folding and ligand binding in ORs. Experimental validations were performed on PSGR-Prostate Specific G-Coupled Receptor- a RO for which we have an activity test and we knowsome agonists.∗Intervenant†Auteur correspondant: [email protected]

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From the multiple sequences alignment of non-redundant clustered ORs of the human genome,we found out consensus motifs, with respect to GPCRs in general and within ORs in particular.We were able to propose to mutation six residues referred as BW 3.32, 3.33, 3.36, 6.48, 6.51 and7.39 that could form the ligand site, by analogy with the solved GPCRs [7]. Homology modelsof PSGR were built using modeler [9], then agonist molecules, centered at those BW positions,were docked with CHARMm [10]. Four of the six residues subjected to alanine mutagenesis inPSGR result in functional impairment in vitro, thus validating our computational analysis. Lig-and specificity in ORs is now addressed that discriminates agonists from antagonists. Eventually,this work will allow a more accurate version of our GPCR automodel webservice [11].

Buck L et al. (1991) Cell 65: 175-187

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Molecular Modeling CoMFA and docking of aNovel Series of Potent 7-Azaindole Based

Tri-Heterocyclic Compounds as CDK2/CyclinE Inhibitors

Christine B. Baltus 1, Radek Jorda 2, Christophe Marot ∗† 1, Karel Berka3,4, Václav Bazgier 4,5, Vladimír Kryštof 5, Gildas Prié 1, Marie-Claude

Viaud-Massuard 1

1 Génétique, Immunothérapie, Chimie et Cancer (GICC) – CNRS : UMR7292, Université FrançoisRabelais - Tours – Faculty of Pharmacy, 31 avenue Monge, 37200 Tours, France

2 Centre of the Region Haná for Biotechnological and Agricultural Research, Palacký University andInstitute of Experimental Botany – AS CR, Šlechtitelů 11, CZ-78371 Olomouc, République tchèque3 Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry –

Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 77146 Olomouc, République tchèque4 Department of Physical Chemistry, Faculty of Science, Palacký University – 17. Listopadu 1192/12,

771 46 Olomouc, République tchèque5 Laboratory of Growth Regulators Department of Chemical Biology and Genetics – Šlechtitelů 11,

CZ-78371 Olomouc, République tchèque

From four molecules inspired by the structural features of fascaplysin, with an interestingpotential to inhibit the complex CDK2/cyclin E, we designed a new series of tri-heterocyclicderivatives based on 1H -pyrrolo[2,3-b]pyridine (7-azaindole) and triazole heterocycles. Com-parative molecular field analysis (CoMFA), based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies, was conducted on a series of 30 compounds from theliterature with high to low known inhibitory activity towards the complex CDK2/cyclin E andwas validated by a test set of 5 compounds giving satisfactory predictive r2 value of 0.92. Re-markably, it also gave us a good prediction of pIC50 for our tri-heterocyclic series which reinforcethe validation of this model for the pIC50 prediction of external set compounds.


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Study of large-scale conformational transitionsin c-Abl at the free energy level via a

structure-based model

Ilaria Mereu ∗† 1

1 Karlsruhe Institute of Technology [Eggenstein-Leopoldshafen] (KIT) – Campus NorthHermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen, Allemagne

The c-Abl protein is a non-receptor tyrosine kinase of primary relevance in cancer research.It is regulated by a complex interplay of conformational transitions that are currently difficultto simulate in full atomistic detail for free energy calculation purposes. The computational ap-proach of structure-based models, though, makes an efficient study of conformational transitionsof large amplitude feasible, even with limited computational resources.We integrated a highly accurate, non-standard structure-based model (that we previously con-tributed to develop) with the metadynamics method for enhanced sampling. This protocol wasapplied to a rich set of conformational transitions that take place in the regulation process ofc-Abl 1b (the activation loop opening, the DFG motif flip, the displacement of the SH2 and SH3domains, the hinge motion and others) and gave us access to the free energy profile associatedto each transition.This work compares three constructs of increasing length: the isolated kinase domain (kd),SH2+kd and SH3+SH2+kd. The performance of the model on three constructs at the free en-ergy level was compared with the aim of elucidating the role of each domain and conformationaltransition in c-Abl activation mechanism.


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Cartographie des interactions atomiques dansle filament de dystrophine

Dominique Mias-Lucquin ∗ 1, Olivier Delalande 1, Elisabeth Le Rumeur 1,Anne-Elisabeth Molza 1, Jean-Francois Hubert 1

1 IGDR – Universite de Rennes 1, CNRS : UMR6290 – France

La dystrophine est une protéine de très grande taille (427kDa) qui a pour rôle de maintenirla cohésion du sarcolemme au cours des cycles de contractionélongation du muscle. Le domainecentral de la protéine est constitué de 24 répétitions repliées en faisceaux d’hélices alpha de typespectrine. Dans les hélices, la succession d’acides aminés hydrophobes tous les 3 et 4 résidus enalternance définit les heptades qui permettent le repliement en faisceaux des trois hélices A, Bet C d’une même répétition. La récente résolution de modèles structuraux par flexible fittingsous contraintes expérimentales de diffusion des rayons X aux petits angles (SAXS) nous permetd’atteindre un niveau de compréhension atomique pour les propriétés structurales et dynamiquesdu faisceau d’hélices qui confèrent à la dystrophine sa fonction de préservation du muscle.Nous avons développé un outil d’analyse des interactions atomiques (Dys-CCC) qui nous permetd’établir une cartographie précise des interactions hydrophobes, des liaisons hydrogènes et desponts salins, au sein du domaine central de la dystrophine. Sur la base d’une carte de proxim-ité spatiale et de mesures de potentiels hydrophobes, Dys-CC génère différentes représentationsgraphiques complémentaires pour l’étude des contacts inter-hélices.

Les premiers résultats obtenus mettent en évidence la prédominance des interactions hydrophobesentre hélices d’une même répétition, parfois renforcées par des liaisons hydrogènes ou pontssalins. Même si les interactions hydrophobes s’organisent principalement autour d’une heptadede résidus, des variations existent autour de ce motif et pourraient expliquer la diversité struc-turale obesrvée tout au long du filament que compose le domaine central de la dystrophine.Dys-CCC est basé sur la bibliothèque Pynteract développée dans le laboratoire. Cette API peutégalement être utilisée pour traiter des interactions au sein de complexes moléculaires (contactsintermoléculaires), comme pour l’analyse de résultats de docking.

∗Intervenant

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Développement d’une stratégie multi-échellecombinant des données expérimentales et insilico pour la reconstruction de complexes

impliquant la dystrophine.

Anne-Elisabeth Molza ∗† 1, Khushdeep Manga 2, Olivier Delalande 1,Nicolas Ferey 3, Marc Baaden 4, Chantal Prevost 4, Sébastien Fiorucci 5,Mirjam Czjzek 6, Nick Menhart 2, Denis Chrétien 1, Elisabeth Le Rumeur

1, Jean-François Hubert 1

1 IGDR, Université Rennes 1 – CNRS : UMR6290, Universite de Rennes 1 – Rennes, France2 Illinois Institute of Technology – Chicago IL, États-Unis

3 LIMSI (3) – CNRS : UPR3251 – CNRS UPR 3251- IUT d’Orsay, Université PARIS XI, Paris, France4 Laboratoire de Biochimie Théorique - Institut de Biologie Physico-Chimique – CNRS : UPR9080,Université Paris VII - Paris Diderot – Institut de Biologie Physico-Chimique 13, rue Pierre et Marie

Curie, F-75005 Paris, France5 Institut de Chimie de Nice (5) – CNRS : UMR7272, Université Nice Sophia Antipolis [UNS],

Université Nice Sophia Antipolis (UNS) – Faculté des Sciences Parc Valrose 28 Avenue Valrose 06108Nice cedex 2, France

6 Station Biologique de Roscoff – CNRS : UMR8227 – Roscoff, France

La dystrophine est une très grande protéine sous-sarcolemmique de 427kDa codée le gèneDMD. Cette protéine joue un rôle essentiel dans le maintien de l’intégrité de la cellule musculairelors des cycles de contraction/relaxation.La dystrophine est composée de quatre principaux domaines structuraux dont le domaine centralcomposé de 24 répétitions et quatre charnières. Chaque répétition est organisée en faisceau detrois -hélices homologues à la spectrine et appelé “ coiled-coil ”.

Des mutations du gène DMD sont à l’origine des myopathies de Duchenne et de Becker, lesquelless’accompagnent de ruptures fréquentes de la membrane des cellules musculaires.

Malgré le rôle important que joue la dystrophine, il existe peu de données sur sa structureet ses interactions avec ses principaux partenaires cellulaires.

Afin de pallier à ce manque, nous avons développés une approche multi-échelle combinant desdonnées expérimentales et des données in silico [1].

Cette méthodologie nous a permis d’une part d’obtenir des modèles tout-atome de fragmentsde la dystrophine native et mutée, en se basant sur des données de diffusion des rayons X auxpetits angles (SAXS) et des méthodes de simulations interactives.∗Intervenant†Auteur correspondant:

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mailto:

D’autre part, nous avons utilisés des méthodes d’amarrage moléculaires afin d’obtenir des mod-èles de complexes impliquant l’actine filamenteuse (actine-F) ou le domaine PDZ de la nNOS(nNOS-PDZ) avec les répétitions R11-15 ou R16-17 de la dystrophine native ou de mutants BMDde la protéine.

Cette stratégie a permis de reconstruire un modèle tout-atome du complexe macromoléculaireimpliquant les répétitions R11-17 de la dystrophine, l’actine-F, nNOS-PDZ et les phospholipidesmembranaires.1. Molza, A.-E. et al. (2014) Faraday Discuss. 169, 45–62

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Analysis of protein structure and dynamics tobetter understand their functional mechanisms

Damien Monet ∗ 1, Nathan Desdouits 1, Michael Nilges 1, Arnaud Blondel 1

1 Unité de Bioinformatique structurale – CNRS : UMR3528, Institut Pasteur de Paris – 28, rue duDocteur Roux - 75724 Paris Cedex 15, France

In many cases, proteins exert their function through internal motions. Accurate under-standing of protein functional mechanisms can be useful, for example, to elaborate innovativeinhibition strategies attempting to use small molecules to block protein motions. This approachis illustrated on two targets involved in different pathologies, the Rift Valley Fever Virus enve-lope protein (Gc), and the nicotinic Acetylcholine Receptors (nAChRs), which both act throughlarge functional transitions. Analysis of cavities on molecular dynamics of Gc helped us to findmolecular levers on its action mechanism. nAChRs have been studied for decades, but solvingtheir structures remains a challenge. To tackle this class of targets we first needed to build highquality comparative models for both the open and closed forms of the same protein sequence. Forthat we devised a specific approach gathering and sorting the information from known structuresof many different homologuous proteins in different states to selectively build models represent-ing the active/inactive forms. These models will be used as starting points for transition pathcalculations by the POE approach developed in the laboratory.

∗Intervenant

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Conformational properties and transport ofglucose by GLUT1 using in silico approaches

Matthieu Ng Fuk Chong ∗† 1, Catherine Etchebest‡ 1

1 Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB) – UniversitéParis Diderot, Sorbonne Paris Cité, INTS, Inserm : UMRS1134 – 6 rue Alexandre Cabanel, 75739 Paris

cedex 15, France

Glucose is an essential source of energy for the mammalian cells. Its transport is achieved byfacilitative diffusion by the glucose transporter GLUT1 into erythrocytes and endothelial cells ofthe blood brain barrier. Thus, inactivating mutation or low expression of GLUT1 is medicallyimportant, as it will lead to a lack of glucose supply to the brain. On the other hand, the ex-pression levels of GLUT1 are often elevated in malignant cells due to the accelerated metabolismand high glucose requirements of the cells.GLUT1 belongs to the family of sugar transporter, a subfamily of the major facilitator super-family (MFS). The MFS transporters are known to alternate between different conformationalstates in which access to the central binding site is switched from the extracellular medium (out-ward open conformation) to the intracellular compartment (inward open conformation) in orderto transport a substrate across the plasma membrane. Recently, a mutant form of GLUT1 waspurified and structurally characterized by X-ray crystallography [1]. The structure was trappedin an inward-open state with a molecule of -nonyl glucoside (BNG), a glucose-derivative, locatedwithin a cavity. Even if this atomic 3D structure is valuable, it does not allow to fully elucidatethe transport mechanism of glucose by GLUT1.

To address this question, we first modeled the 3D structure of the wild-type (WT) form using thecrystal structure of the GLUT1 mutant. Then, we performed molecular dynamics simulations ofthe WT protein embedded in a POPC bilayer where the crystallized ligand was either replacedby a -D-glucose or suppressed.

In the holo state (i.e. with a bound glucose in the central cavity), the glucose translocatesout of the protein. In the cytoplasmic half of the channel, glucose interacts predominantly withpolar or charged residues and underwent several rotations before leaving the binding site andexiting the protein. In the apo state (i.e. without glucose in the central cavity), the results of1 µs equilibrium MD simulations of GLUT1 showed the partial closure of the cytoplasmic endof the protein. This state was similar to the intermediate state, namely the occluded state seenin the bacterial homologue XylE, in which the solvated central cavity is inaccessible from eitherside of the membrane. For the first time, these results enlighten the role of the different aminoacids in the conformational changes and in the translocation mechanism.1. Deng D, Xu C, Sun P, Wu J, Yan C, et al. (2014) Crystal structure of the human glucosetransporter GLUT1. Nature 510: 121-125.∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant: [email protected]

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As-Rigid-As-Possible interpolation forstructural biology

Minh Khoa Nguyen ∗† 1, Léonard Jaillet 1, Stephane Redon 1

1 INRIA Rhône-Alpes (INRIA Grenoble Rhône-Alpes) – INRIA – ZIRST 655 Avenue de l’EuropeMontbonnot 38334 Saint Ismier cedex, France

Finding the transition pathway or minimum energy path (MEP) between two conformationsis a complicated problem in structural biology, because the search space typically has high dimen-sion. Methods such as Nudged Elastic Band (NEB) [1] and zero temperature string [2] search fora MEP from an initial guess of the pathway. A common way to choose the initial guess is to usethe linear interpolation between the start and goal conformations. However, such a guess doesnot necessarily give a realistic path, and it has been shown that it can lead to low convergenceor failure of the chosen method [3].In the field of computer graphics, methods for interpolating between a complicated shape and adeformed version of it have been applied to find a smooth and realistic motion between two posesof a figure [4] [5] [6]. Inspired by this research, we apply the As-Rigid-As-Possible (ARAP) inter-polation between two given conformations. This method attempts to preserve the rigidity of thestructure, i.e. changes in bond lengths and angles are minimized as much as possible during theinterpolation process. Consequently, the interpolation creates a series of low-change-of-energytransitions from one conformation to another. This method can find a good initial guess forother algorithms such as NEB.

In our method, a large molecule is divided into smaller connected regions. The amount ofrotation of each region is calculated from two given conformations and then interpolated. Theinterpolated image of the entire molecule is a result of combining all individual interpolations. Asa result, this method produces little local deformations. For evaluation purposes, this method iscompared with linear interpolation. NEB is applied to minimize the energy of the initial guessesobtained by ARAP interpolation and linear interpolation. The tests were performed on the fold-ing of a graphene sheet into a nanotube, as well as a few other benchmarks. Results show thatthe initial guess produced by the ARAP interpolation method gives an appropriate transitionpath in a shorter time than that from linear interpolation.

Our method has several advantages: force field independence, low energy changes along thepathway due to local rigidity preservation, and high parallelization capability. It will be appliedto more complicated molecules such as proteins and RNA in the future.

References:

H. Jónsson, G. Mills, K. W. Jacobsen, “Nudged Elastic Band Method for Finding Minimum En-∗Intervenant†Auteur correspondant: [email protected]

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ergy Paths of Transitions,” Classical and Quantum Dynamics in Condensed Phase Simulations,ed. B. J. Berne, G. Ciccotti and D. F. Coker (World Scientific, 1998), 385.

E. Weinan, W. Ren, E. Vanden-Eijnden, “Simplified and improved string method for computingthe minimum energy paths in barrier-crossing events,” The Journal of Chemical Physics (2007),126 (16), 164103.

B. Peters, A. Heyden, AT. Bell, A. Chakraborty, “A growing string method for determiningtransition states: comparison to the nudged elastic band and string methods,” The Journal ofChemical Physics (2004), 120(17), 7877-7886.

O. Sorkine, M. Alexa, “As-rigid-as-possible surface modeling,” In Proceedings of the fifth Euro-graphics symposium on Geometry processing (SGP ’07). Eurographics Association, Aire-la-Ville,Switzerland, Switzerland, 109-116.

Z. Zhang, G. Li, H. Lu, Y. Ouyang, M. Yin, C. Xian, “Fast as-isometric-as-possible shape inter-polation,” Computers & Graphics (2015), 46, 244-256.

W. Baxter, P. Barla, K. Anjyo. “Rigid shape interpolation using normal equations,” In Proceed-ings of the 6th international symposium on Non-photorealistic animation and rendering (NPAR’08). ACM, New York, NY, USA, 59-64.

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What wins in determining protein regularsecondary structures? The polar/non polarbinary pattern or the intrinsic amino acid

composition? A statistical analysis using theHCA approach.

Joseph Rebehmed ∗ 1, Jean-Paul Mornon 1, Isabelle Callebaut† 1

1 Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC) – Institut derecherche pour le développement [IRD] : UR206, Université Pierre et Marie Curie (UPMC) - Paris VI,CNRS : UMR7590, Muséum National d’Histoire Naturelle (MNHN) – Tour 23 - Barre 22-23 - 4e étage -

BC 115 4 place Jussieu 75252 PARIS, France

The intrinsic preferences of a polypeptidic chain to form -helices or -strands can be over-whelmed by the polar/nonpolar periodicity and the drive to form amphiphilic structures capableof burying hydrophobic surface area. As a consequence, the precise identity of a amino acid at aparticular location in a sequence may be less important than the simple choice of whether it ispolar or nonpolar.In this study, we considered two sets of 3D structures extracted from the SCOP database (classesa, b, c, d, e) at two levels of redundancy (ASTRAL 95 and ASTRAL 40) in order to clarify theroles played respectively by binary patterns and amino acid compositions in the formation of reg-ular secondary structures (RSS). We considered HCA-derived hydrophobic clusters (HC), whichare constrained binary patterns particularly well fitting RSS limits, to investigate the preferencesof binary patterns towards RSS and estimate the influence of amino acid composition.

We first used a “Quark” code, to decompose each HC into its basic units and thereby evalu-ate how its main periodicity correlates with its main state in terms of RSS. Results showed that-helices are more permissive to -strand patterns than -strands are to -helix patterns.

Then, we calculated the propensities of amino acids for the different states of secondary struc-tures, considering all the RSS associated with each HC or distinguishing between -helices and -strands. Concordant and discordant behaviors are then assigned following the agreement/disagreementof the observed RSS with that expected from the HC dictionary. In agreement with previous re-ports, three classes of amino acids are highlighted, with preferences for -helices (A,L,M,E,Q,K,R),-strands (V,I,F,W,Y,C,T) and coils (P,G,D,N,S). Histidine (H) has quasi-equal preferences forthe three states. On this basis, distinct behaviors can be highlighted between the concordantand discordant HC. In “-strand” HC, the weight of aliphatic hydrophobic amino acids appears tobe prominent for shifting from a concordant to a discordant behavior, whereas in “-helix” HC, allthe amino acids with preferences for -helices globally decrease at the benefit of non-hydrophobic∗Intervenant†Auteur correspondant: [email protected]

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amino acids with preference for -strands and coils.Altogether, our results enlighten the respective role played by binary pattern and amino acidcomposition for RSS formation. The original approach described here could complement thecurrently available methods to predict secondary structures in proteins and could assist proteinengineering and design, from the only information of a single amino acid sequence.

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Molecular features switching off themelatonin-induced activity from MT1 to

GPR50 receptors

Nicolas Renault ∗† 1, Xavier Laurent 1, Nathalie Clement 2, Jean-LucGuillaume 2, Amaury Farce 1, Ralf Jockers 2, Philippe Chavatte 1,3

1 Faculté des Sciences Pharmaceutiques de Lille – Université Lille 2 - LIRIC U995 Inserm – 3, rue duProfesseur Laguesse - 59000 Lille, France

2 Institut Cochin – CNRS : UMR8104, Inserm : U1016, Université Paris V - Paris Descartes – 22 rueMéchain, 75014 Paris, France

3 Institut de Chimie Pharmaceutique Albert Lespagnol – Université Lille II - Droit et santé – 3, rue duProfesseur Laguesse. 59000 LILLE, France

Recent crystal structures of G protein-coupled receptors (GPCRs) highlight the previouslyunappreciated role of the second extracellular loop (E2) in ligand binding, ligand gating orreceptor activation. In agreement with functional in vitro assays, molecular dynamics simulationsreveal the critical role of E2 in activation of the melatonin MT1 receptor and in inactivation ofthe closely related orphan GPR50 receptor.


143



Coarse-Graining Parameterization of SpecificCardiac Membrane Lipids

Edithe Selwa ∗ 1, Bogdan Iorga 1

1 Institut de Chimie des Substances Naturelles (ICSN) – CNRS : UPR2301 – Avenue de la terrasse91198 Gif sur yvette cedex, France

Recently, a Cardiac Targeting Peptide (CTP) [1] was shown to be able to transduce specif-ically the myocardium and can be used as a new strategy to deliver therapeutic drugs in theframework of treating heart failure [2,3].

Our general aim is to understand how CTP interacts and penetrates the cardiomyocyte mem-brane by molecular dynamics simulations. However, simulation of such large biological systemsis challenging, because of the multitude of spatial and temporal scales involved. Biological pro-cesses, such as self-assembly of lipid bilayers, remain inaccessible by all-atom simulations, sincethese time-consuming approaches are computationally expensive. Coarse grained (CG) models,which have gained a lot of popularity lately, overcome these limits, by replacing full-atomisticdetails by lower resolution particles, while retaining chemical specificity.

In this work, given the complexity and the size of the system, we have used a CG represen-tation of the system components, based on MARTINI force field [4,5]. We have parameterizeda number of missing molecules using this force field, and then generated a realistic model of thecardiomyocyte membrane that will be used in the future for the study of the interaction withCTP.

∗Intervenant

144


Prediction of peptide conformational stability

Marie Amandine Laurent 1, Raphael Licha 1, Jananan Pathmanathan 1,Jacques Chomilier 1, Dirk Stratmann ∗ 1

1 Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC) – Institut derecherche pour le développement [IRD] : UR206, Université Pierre et Marie Curie (UPMC) - Paris VI,CNRS : UMR7590, Muséum National d’Histoire Naturelle (MNHN) – Tour 23 - Barre 22-23 - 4e étage -

BC 115 4 place Jussieu 75252 PARIS, France

Protein-protein interactions play a key role in biological functions. Their inhibition becomean important issue for drug design. Peptides are suitable candidates to target specific interactionsespecially if they are extracted from the interaction interface. Among these protein fragments,Tightened End Fragments (TEF) (**) have the advantage to be potentially more stable in termsof their conformation. This should confer them a better affinity. A TEF is not unique at theinteraction interface and the choice can be optimized in term of stability and affinity. Our studyis about the aspect of conformational stability. Using molecular dynamics simulations, we de-veloped a protocol for peptide conformational stability prediction. To validate our method, weassembled a benchmark of peptide structures associated with protein fragments. Our method suc-ceeds in distinguishing stable from unstable peptides. In addition, it allowed us to identify somefragments that are stable outside the protein. (**) http://www.impmc.upmc.fr/software/tef

∗Intervenant

145


Dynamique d’assemblage de la capside desnorovirus

Thibault Tubiana ∗† 1,2, Stéphane Bressanelli‡ 1,2, Yves Boulard§ 1,2

1 Institut de Biologie Intégrative de la Cellule (I2BC) – CNRS : UMR9198 – Bâtiment 21 Avenue de laTerrasse 91190 Gif-sur-Yvette Cedex, France

2 Institut de Biologie et de Technologies de Saclay (IBITECS / SB2SM) – CEA – CEA Saclay bat 53291191 Gif sur Yvette cedex, France

Les norovirus sont des petits virus non enveloppés à ARN simple brin de polarité positive.Ils sont la cause principale des gastroentérites virales aigues chez les humains et les animauxet présentent un intérêt mondial pour la santé. Leur capside est composée de 180 copies d’uneseule protéine structurale VP1 composée de deux domaines principaux : le domaine shell (S)et le domaine protruding (P) qui contient 2 sous domaines (P1 et P2)[1]. Le domaine P est lapartie exposée au milieu biologique, il permet notamment de stabiliser et d’ajuster la taille de lacapside[2]. Le domaine S quant à lui est le module d’assemblage de la capside virale[2].

Des études ont montré que les capsides de certains norovirus (NB2-VLP, NV-VLP) peuventêtre désassemblées et réassemblées in vitro selon le pH du milieu[3] et que l’assemblage de NB2-VLP (NewBury2 – Virus-Like particles) à partir de dimères de VP1 est un processus en deuxétapes : une apparition rapide d’intermédiaires moléculaires puis une formation lente de la cap-side, ce qui est en désaccord avec les modèles actuels d’assemblage de capside[4]. Ces étudesnous conduisent à l’hypothèse que le bras N-terminal serait un bon candidat dans la stabilisationallostérique des intermédiaires et que la (dé)protonation de certains résidus serait impliquée dansl’assemblage ou le désassemblage (dépendant au pH) de la capside in vitro.

A l’aide d’approches computationnelles, nous cherchons donc à étendre ces études cinétiquesaux norovirus humains et ainsi à déterminer les bases moléculaires du mécanisme physique origi-nal d’assemblage des norovirus. Des techniques de bioinformatique telles que des alignements deséquences et modélisations par homologie ont été appliquées afin d’obtenir une structure tridi-mensionnelle du dimère de NB2-VP1 (Génogroupe III), basée sur la structure cristallographiquede son homologue du génogroupe I : le Norwalk Virus (1IHM[1]). Ce modèle a ensuite été utilisédans des simulations de dynamique moléculaire et de recuits simulés afin d’analyser le comporte-ment du bras N-terminal dans un environnement aqueux.Nos résultats préliminaires étendent les études réalisées auparavant dans l’équipe [3,4]. Ces résul-tats montrent que sous sa forme déprotonée le bras N-terminal établit de nombreuses interactionspolaires de type pont-salins permettent sa stabilisation sous le domaine S de la protéine. Cespont-salins ont ensuite été neutralisés par protonation des acides aminés acides impliqués dans∗Intervenant†Auteur correspondant: [email protected]‡Auteur correspondant: [email protected]§Auteur correspondant: [email protected]

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l’interaction afin d’étudier l’importance de ces liaisons pour la stabilité du bras N-terminal.

1. Prasad BV V. X-ray Crystallographic Structure of the Norwalk Virus Capsid. Science (80- ).1999;286: 287–290. doi:10.1126/science.286.5438.2872. Bertolotti-Ciarlet A, White LJ, Chen R, Prasad BV V., Estes MK. Structural Requirements forthe Assembly of Norwalk Virus-Like Particles. J Virol. 2002;76: 4044–4055. doi:10.1128/JVI.76.8.4044-4055.20023. Tresset G, Decouche V, Bryche J-F, Charpilienne A, Le Cœur C, Barbier C, et al. Unusualself-assembly properties of Norovirus Newbury2 virus-like particles. Arch Biochem Biophys. El-sevier Inc.; 2013;537: 144–52. doi:10.1016/j.abb.2013.07.0034. Tresset G, Le Coeur C, Bryche J-F, Tatou M, Zeghal M, Charpilienne A, et al. Noroviruscapsid proteins self-assemble through biphasic kinetics via long-lived stave-like intermediates. JAm Chem Soc. 2013;135: 15373–81. doi:10.1021/ja403550f

147


Identification and automated weighting ofensemble-averaged NOE restraints

Silke Wieninger ∗ 1, Beat Vögeli 2, Nelly Morellet 3, Benjamin Bardiaux 1,Guillaume Bouvier 1, Michael Nilges 1

1 Bioinformatique Structurale – Institut Pasteur de Paris – France2 Laboratory of Physical Chemistry, ETH Zürich – Suisse

3 Institut de Chimie des Substances Naturelles – Centre de Recherches de Gif – France

NMR data represent a time and ensemble average. Accordingly, not all restraints derivedfrom NMR experiments can be fulfilled by a single structure. To avoid introducing errors andto estimate the dynamical behavior of the protein, ensemble calculations are performed, whichmodel a few conformations simultaneously. By treating only a subset of the NOEs as ensemblerestraints instead of the full restraint set, we reduce the risk of data overfitting. We identifymutually incompatible restraints by generating a large set of conformations in a conventionalARIA run[1]. Clustering of the conformations using self-organizing maps[2,3] enables us to assignthe restraints to different classes, which are weighted automatically during the ensemble ARIArun, based on the deviation of the atom distances from the NOE target distances[4]. Exemplary,we determine the structure of the 56-residue protein GB3, the third immunoglobulin bindingdomain of protein G, taking advantage of an extensive set of exact NOEs[5]. On another system,the piggyBac transposase, we show that the ensemble strategy allows to identify conformationswhich cannot be obtained using standard structure determination protocols.W Rieping, M Habeck, B Bardiaux, A Bernard, TE Malliavin, M Nilges (2007), Bioinformatics,23:381-382.

T Kohonen (2013), Neural Networks, 37:52-65.

G Bouvier, N Duclert-Savatier, N Desdouits, D Meziane-Cherif, A Blondel, P Courvalin, MNilges, TE Malliavin (2014), Chemical information and modeling, 54:289-301.

M Habeck, W Rieping, M Nilges (2006), PNAS, 103:1756-1761.

B Vögeli, S Kazemi, P Gntert, R Riek (2012), Nature structural and molecular biology, 19:1053-1057.

∗Intervenant

148


149

Listes des auteurs

Abdellatif, Nadhir, 83 Abi Hussein, Hiba, 59 Aci-Sèche, Samia, 34, 100 Agbulut, Onnik, 93 Aghajari, Nushin, 37 Allain, Fabrice, 74 Allouche, David, 45, 98 Alvarez, Pablo, 105 Amillastré, Emilie, 105 André, Isabelle, 45, 96, 98, 105 Andre-Leroux, Gwenaëlle, 130 Antonczak, Serge, 65, 113, 119 Archontis, Georgios, 103 Artemova, Svetlana, 83 Asencio Hernández, Julia, 39 Aubert, Marc, 83 Audit, Edouard, 103 Baaden, Marc, 117, 118, 135 Ballut, Lionel, 37 Baltus, Christine B., 132 Banarès, Jean-Louis, 123 Barakat, Fatima, 111 Barbault, Florent, 55 Barbe, Sophie, 45, 96, 98, 105 Bardiaux, Benjamin, 74, 148 Batista, Paulo R., 104 Baud, Stéphanie, 112 Bazgier, Václav, 132 Belloy, Nicolas, 119 Becker, Hubert F., 51 Ben Ouirane, Kaouther, 69 Bender, Andreas, 85 Benoit, Etienne, 24 Berka, Karel, 132 Bersweiler, Antoine, 38, 91 Best, Robert, 61 Bisch, Paulo M., 104 Blondel, Arnaud, 137 Blumberger, Jochen, 61 Boitard, Solène, 93 Bonnet, Pascal, 26, 34, 100 Borrel, Alexandre, 59 Bosc, Nicolas, 34 Boschi-Muller, Sandrine, 30, 88

Bouakouk-Chitti, Zohra, 106 Boukhenouna, Samia, 95 Boulard, Yves, 146 Bourg, Stéphane, 26, 100, 109 Bouvier, Guillaume, 76, 85, 148 Braka, Abdennour, 100 Branlant, Guy, 38, 91, 95 Bressanelli, Stéphane, 146 Broutin, Isabelle, 69 Brut, Marie, 102 Bui, Linh Chi, 90, 93 Bureau, Ronan, 53 Busca, Patricia, 55 Callebaut, Isabelle, 141 Cambon, Emmanuelle, 96 Camproux, Anne-Claude, 59, 121 Carrique, Loic, 37 Castaing, Bertrand, 26, 109 Cerdan, Rachel, 27 Charpentier, Emmanuelle, 119 Chatron, Nolan, 24 Chauvot De Beauchene, Isaure, 79 Chavatte, Philippe, 143 Chavent, Matthieu, 67 Cherfils, Jacqueline, 22 Cheron, Jean-Baptiste, 113, 119, 121 Chomienne, Christine, 90 Chomilier, Jacques, 145 Chrétien, Denis, 135 Clement, Nathalie, 143 Coadou, Gaël, 28 Colin, Sabrina, 30 Colloc’h, Nathalie, 89 Cools, Jan, 90 Cortes, Isidro, 85 Cortes, Juan, 114 Costa, Mauricio, 104, 123 Coste, Franck, 26, 109 Cragnolini, Tristan, 118 Crouzy, Serge, 115 Culard, Françoise, 26, 109 Czjzek, Mirjam, 135 Damian, Marjorie, 123

150

Daniellou, Richard, 28 Dauchez, Manuel, 112, 119 David, Benoît, 63 De Brevern, Alexandre, 81 De Givry, Simon, 98 De March, Claire, 119 De Sancho, David, 61 De Vecchis, Dario, 117 De Vries, Sjoerd, 79 Debiec, Hanna, 107 Dejaegere, Annick, 52, 62 Delalande, Olivier, 134, 135 Delamar, Michel, 55 Delarue, Patrice, 111 Delsuc, Marc-André, 39 Dementin, Sébastien, 35, 94 Derreumaux, Philippe, 118, 125 Desdouits, Nathan, 137 Diharce, Julien, 65 Djaout, Kamel, 51 Dobbek, Holger, 94 Domnik, Lilith, 94 Donald, Bruce, 45 Douguet, Dominique, 50 Doutreligne, Sébastien, 118 Druart, Karen, 103 Duncan, Anna, 67 Duneau, Jean-Pierre, 70 Dupret, Jean-Marie, 90, 93 Duquesne, Sophie, 105 Durant, Laeticia, 90 Duval, Romain, 90 Eberhardt, Jérôme, 62 Emonard, Hervé, 119 Etchebest, Catherine, 69, 138 Etique Nicolas, 119 Farce, Amaury, 143 Fehrentz, Jean-Alain, 123 Ferber, Mathias, 76 Ferey, Nicolas, 135 Fiorucci, Sébastien, 65, 113, 135 Flatters , Delphine, 121 Floquet, Nicolas, 104, 123 Fogha, Jade, 53 Fourmond, Vincent, 35, 94

Gageat, Cédric, 118, 125 Gaillard, Thomas, 43 Garkavenko, Maria, 126 Garnier, Norbert, 26, 100, 109 Gate, Jocelyn, 83 Gelly, Jean-Christophe, 81 Geneix, Colette, 59 Ghouzam, Yassine, 81 Gloaguen, Céline, 53 Goffinont, Stéphane, 26, 109 Golebiowski, Jérôme, 65, 113, 119 Grudinin, Sergei, 77, 83, 126 Guca, Ewelina, 27 Gueroult, Marc, 105 Guichou, Jean-François, 27 Guidez, Fabien, 90 Guillaume, Jean-Luc, 143 Guyon, Frédéric, 57 Hélie, Jean, 67 Hénin, Jérôme, 117 Hadj-Said, Jessica, 35, 94 Hilvert, Donald, 42 Hoh, François, 27 Hu, Rongjing, 55 Hubert, Jean-François, 134, 135 Iorga, Bogdan, 107, 144 Isenberg, Tobias, 73 Jacob, Christophe, 95 Jaillet, Léonard, 83, 139 Jockers, Ralf, 143 Jorda, Radek, 132 Katava, Marina, 128 Kauffmann, Brice, 29 Kazennov, Andrei, 126 Kellou-Taïri, Saf a, 106 Krebs, Fanny, 52 Kriznik, Alexandre, 38, 91 Kryštof, Vladimír, 132 Kubas, Adam, 61 Léger, Christophe, 35, 94 Lafite, Pierre, 28 Lambert, Eléonore, 112 Langenfeld, Florent, 24

151

Lattard, Virginie, 24 Laurent, Marie Amandine, 145 Laurent, Xavier, 143 Laurin, Yoann, 129 Le Guen, Yann, 96 Le Rumeur, Elisabeth, 134, 135 Lec, Jean-Christophe, 30, 88 Legeai-Mallet, Laurence, 55 Li, Yan, 55 Licha, Raphael, 145 Limoges, Benoit, 29 Louet, Maxime, 66 Malliavin, Therese, 85 Manga, Khushdeep, 135 Mano, Nicolas, 29 Marie, Jacky, 123 Marot, Christophe, 132 Marques, Stéphanie, 28 Marroun, Sami, 28 Martinez, Jean, 104, 123 Martiny, Laurent, 119 Martiny, Virginie, 107 Marty, Alain, 105 Mary, Sophie, 123 Mathieu, Cécile, 90, 93 Maurel, François, 55 Mavre, François, 29 Mazon, Hortense, 30, 88, 91, 95 Menhart, Nick, 135 Mereu, Ilaria, 133 Merrouch, Meriem, 94 Meyer, Christophe, 34 Mias-Lucquin, Dominique, 134 Mignon, David, 43, 103 Molza, Anne-Elisabeth, 134, 135 Monet, Damien, 137 Montaut, Sabine, 28 Morellet, Nelly, 148 Mornon, Jean-Paul, 141 Moroy, Gautier, 57 Morris, May C., 104 Moulis, Claire, 96 Mulard, Laurence, 96 Munier-Lehmann, Hélène, 33 Myllykallio, Hannu, 51

Narth, Christophe, 71 Neveu, Emilie, 77 Ng Fuk Chong, Matthieu, 138 Nguyen, Khoa, 83 Nilges, Michael, 74, 76, 85, 137, 148 Oulyadi, Hassan, 28 Overington, John P., 85 Pandélia, Maria-Eirini, 35 Panel, Nicolas, 43 Pasco-Brassart, Sylvie, 112 Pasquali, Samuela, 118, 125 Pathmanathan, Jananan, 145 Perahia, David, 104, 123 Petit, Emile, 90 Petitjean, Michel, 59 Piquemal, Jean-Philip, 71 Popov, Petr, 77 Postic, Guillaume, 81 Poulain, Laurent, 53 Prangé, Thierry, 89 Prestat, Guillaume, 55 Prevost, Chantal, 129, 135 Prié, Gildas, 132 Rahuel-Clermont, Sophie, 38, 91, 95 Rasolohery, Inès, 57 Raussin, Florent, 104 Rebehmed, Joseph, 141 Reddy, Tyler, 67 Redon, Stephane, 83, 139 Regad, Leslie, 59, 121 Remaud-Siméon, Magali, 96 Renault, Nicolas, 143 Renault, Pedro, 104 Rieux, Charlotte, 26, 109 Rinaldo, David, 48 Ritchie, Dave, 77 Robert, Charles, 129 Roberts, Kyle, 45 Rodrigues-Lima, Fernando, 90, 93 Rollin, Patrick, 28 Ronco, Pierre, 107 Sacquin-Mora, Sophie, 129 Salamone, Stéphane, 96

152

Sanejouand, Yves-Henri, 63 Sansom, Mark, 67 Santolini, Jérôme, 23 Savarin, Philippe, 129 Schiex, Thomas, 45, 98 Schuler, Marie, 28 Selwa, Edithe, 107, 144 Senac, Caroline, 121 Senet, Patrick, 111 Sidore, Marlon, 70 Simoncini, David, 98 Simonson, Thomas, 43 Sopkova-De-Oliveira Santos Jana, 53 Sterpone, Fabio, 128 Stines-Chaumeil, Claire, 29 Stote, Roland, 52, 62 Stratmann, Dirk, 145 Strick, Terence, 32 Sturgis, James, 70 Talfournier, François, 30, 88 Taly, Antoine, 117, 118 Tatibouet, Arnaud, 28 Tchertanov, Luba, 24

Tellier, Charles, 63 Toledano, Michel, 95 Traore, Seydou, 45, 98 Tubiana, Thibault, 146 Vögeli, Beat, 148 Van Westen, Gerard J.P., 85 Verges, Alizée, 96 Verzeaux, Laurie, 119 Vial, Henri, 27 Viaud-Massuard, Marie-Claude, 132 Violot, Sébastien, 37 Viricel, Clément, 98 Voisin-Chiret, Anne Sophie, 53 Vos, Marten, 51 Wang, Po-Hung, 61 Wieninger, Silke, 148 Wroblowski, Berthold, 34 Yengui, Mohamed, 83 Zacharias, Martin, 79 Zhang, Rhuizheng, 55

153

Liste des participants

ABI HUSSEIN HibaMTi – Inserm – UMR‐S 973 Université Paris Diderot‐Paris 7 Bât Lamarck A, 4e‐5e étage , Courrier 7113 35, rue Hélène Brion 75205 PARIS Cedex 13 France hiba.abihussein@univ‐paris‐diderot.fr

BARAKAT FatimaLaboratoire Interdisciplinaire Carnot de Bourgogne UMR 6303 CNRS – Université de Bourgogne 9, avenue A. Savary, BP 47870 21078 DIJON Cedex France fatima.barakat@u‐bourgogne.fr

ACI‐SÈCHE Samia Institut de Chimie organique et Analytique Université d'Orléans – Pôle de chimie Rue de Chartres ‐BP 6759 45067 ORLEANS Cedex 2 France samia.aci@cnrs‐orleans.fr

BARBAULT FlorentITODYS, UMR 7086 Université Paris Diderot 15, rue J‐A de Baïf 75205 PARIS France florent.barbault@univ‐paris‐diderot.fr

ALLAIN Fabrice Unité de Bioinformatique Structurale CNRS UMR3528 Institut Pasteur 25‐28, rue du Docteur Roux 75015 PARIS France [email protected]

BARBE SophieLaboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP) INSA/CNRS 5504 – UMR INSA/INRA 792 135, avenue de Rangueil 31077 TOULOUSE Cedex 04 France sophie.barbe@insa‐toulouse.fr

ANDRE Isabelle Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP) INSA/CNRS 5504 – UMR INSA/INRA 792 135, avenue de Rangueil 31077 TOULOUSE Cedex 04 France isabelle.andre@insa‐toulouse.fr

BAUD StéphanieSiRMa – UMR CNRS/URCA N° 7369 Plateau de Modélisation Moléculaire Multi‐échelle – UFR Sciences Exactes et Naturelles, Moulin de la Housse 51687 REIMS Cedex 2 France stephanie.baud@univ‐reims.fr

BAADEN Marc Laboratoire de Biochimie Théorique – CNRS UPR9080 Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

BELLOY NicolasPlateau de Modélisation Moléculaire Multi‐échelle Université de Reims Champagne‐Ardenne UFR SEN, Campus Moulin de la Housse 51687 REIMS France nicolas.belloy@univ‐reims.fr

154

BEN OUIRANE Kaouther INSERM UMR_S 1134,DSIMB Université Paris Diderot‐ Paris 7 INTS 6, rue Alexandre Cabanel 75739 PARIS Cedex 15 France [email protected]

BRAKA AbdennourInstitut de Chimie Organique et Analytique Université d'Orléans – Pôle de chimie Rue de Chartres – BP 6759 45067 ORLEANS Cedex 2 France abdennour.braka@univ‐orleans.fr

BLUMBERGER Jochen Department of Physics and Astronomy University College London Gower Street LONDON WC1E 6BT, UK Grande‐Bretagne [email protected]

BRUT MarieLaboratoire analyse et architecture des systèmes (LAAS) – CNRS 7, avenue du Colonel Roche BP 54200 31031 TOULOUSE Cedex 4 France [email protected]

BOSC Nicolas Institut de Chimie Organique et Analytique Université d'Orléans Rue de Chartres 45067 ORLEANS France nicolas.bosc@univ‐orleans.fr

BUI Linh ChiUnité Biologie Fonctionnelle et Adaptative CNRS UMR 8251 – Université Paris Diderot Bâtiment Buffon – 3ème étage – Pièce 348A 4, rue Marie‐Andrée Lagroua Weill Hallé 75205 PARIS Cedex 13 France linh‐chi.bui@univ‐paris‐diderot.fr

BOSCHI‐MULLER Sandrine IMoPA UMR 7365 CNRS‐UL – Equipe Enzymologie Moléculaire – Université de Lorraine – Faculté de médecine 9, avenue de la Forêt de Haye – CS 50184 54506 VANDOEUVRE‐LES‐NANCY Cedex France sandrine.boschi@univ‐lorraine.fr

BUSI FlorentUnité Biologie Fonctionnelle et Adaptative CNRS UMR 8251 – Équipe des RMCX Université Paris Diderot Paris 7 4, rue Marie Andrée Lagroua Weill Hallé 75205 PARIS Cedex 13 France florent.busi@univ‐paris‐diderot.fr

BOUVIER Guillaume Bioinformatique structurale Institut Pasteur Unité de Bioinformatique Structurale CNRS UMR 3528 – Département de Biologie Structurale et Chimie 75015 PARIS France [email protected]

CARRIQUE LoïcBiocrystallography and Structural Biology of Therapeutic Targets Group Institute for the Biology and Chemistry of Proteins 7, passage du Vercors 69367 LYON Cedex 07 France [email protected]

155

CASTAING Bertrand Centre de Biophysique Moléculaire UPR4301 CNRS Rue Charles Sadron 45071 ORLEANS Cedex 2 France castaing@cnrs‐orleans.fr

CHAVENT MatthieuBiochemistry Department SBCB unit South Park Road OXFORD OX1 3QU Grande‐Bretagne [email protected]

CERDAN Rachel Dynamique des Interactions Membranaires Normales et Pathologiques – UMR5235 CNRS‐Université de Montpellier Place Eugène Bataillon – Bât 24 – cc107 34095 MONTPELLIER Cedex 5 France rachel.cerdan@univ‐montp2.fr

CHERFILS Jacqueline Laboratoire de Biologie et Pharmacologie Appliquée CNRS – Ecole Normale Supérieure Cachan 61, avenue du Président Wilson 94235 CACHAN cedex France jacqueline.cherfils@ens‐cachan.fr

CHALOIN Laurent Centre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34293 MONTPELLIER Cedex 5 France [email protected]

CHERON Jean‐Baptiste Institut de Chimie de Nice UMR 7272, Université de Nice‐Sophia Antipolis – CNRS Parc Valrose 06108 NICE Cedex 2 France [email protected]

CHATRON Nolan CMLA CNRS UMR 8536 ENS Cachan Bât. Laplace, 1er étage 61, av. du président Wilson 94235 CACHAN Cedex France nchatron@ens‐cachan.fr

COLLOC'H NathalieISTCT UMR 6301 CNRS Université de Caen Centre Cyceron Bd Becquerel 14074 CAEN France [email protected]

CHAUVOT DE BEAUCHENE IsaureTheoretical biophysics – Molecular dynamics TUM JamesFranceanck Strasse 1 85748 GARCHING Allemagne [email protected]

CORTES IsidroUnité de Bioinformatique Structurale Institut Pasteur 25‐28, rue du Dr. Roux 75015 PARIS France [email protected]

156

CORTES Juan Laboratoire analyse et architecture des systèmes (LAAS) – CNRS 7, avenue du Colonel Roche BP 54200 31031 TOULOUSE Cedex 4 France [email protected]

DE VECCHIS DarioLaboratoire de Biochimie Théorique – UPR 9080 Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

CROUZY Serge Chimie et Biologie des Métaux CEA 17, rue des martyrs 38054 GRENOBLE Cedex 9 France [email protected]

DEKIMECHE KatiaSchrödinger GmbH Dynamostr. 13 D‐68165 MANNHEIM Allemagne [email protected]

DAUCHEZ ManuelCNRS UMR 7369 MEDyC, P3M‐MaSCA Université de Reims Champagne‐Ardenne UFR Sciences Exactes et Naturelles Moulin de la Housse – BP1039 51687 REIMS Cedex 2 France manuel.dauchez@univ‐reims.fr

DELALANDE OlivierInstitut de Génétique et Développement de Rennes (IGDR) Université de Rennes 1 2, avenue du Professeur Léon Bernard 35000 RENNES France olivier.delalande@univ‐rennes1.fr

DAVID Benoît UFIP, UMR CNRS 6286 Université de Nantes UFR SCIENCES ET TECHNIQUES 2, rue de la Houssinière 44322 NANTES Cedex 03 France [email protected]‐nantes.fr

DELSUC Marc‐André Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS 1, rue LaurentFranceies 67404 ILLKIRCH France [email protected]

DE LAMOTTE Frédéric UMR Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales INRA Avenue Agropolis – TA A‐108/03 34398 MONTPELLIER Cedex 5 France [email protected]

DEMENTIN Sébastien Bioénergétique et Ingénierie des Protéines BIP6 CNRS 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected]

157

DIHARCE Julien Institut de Chimie de Nice, ICN UMR CNRS 7272 28, avenue Valrose 06108 NICE Cedex 2 France [email protected]

DUNEAU Jean‐Pierre Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM) CNRS – Aix‐Marseille Université 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected]

DJAOUT Kamel Laboratoire d'Optique et Biosciences (LOB) Ecole Polytechnique Route de Saclay 91128 PALAISEAU France [email protected]

DUPRET Jean‐Marie Unité de Biologie Fonctionnelle et Adaptative Université Paris Diderot – CNRS UMR 8251 Bâtiment Buffon, 3ème étage 4, rue Marie Andrée Lagroua Weill Hallé 75205 PARIS Cedex 13 France jean‐marie.dupret@univ‐paris‐diderot.fr

DOUGUET Dominique Institut de Pharmacologie Moléculaire et Cellulaire CNRS UMR7275 660, route des lucioles 06560 VALBONNE France [email protected]

DUVAL RomainUnité Biologie Fonctionnelle et Adaptative Université Paris Diderot – CNRS UMR 8251 Bâtiment Buffon 4, rue Marie Andrée Lagroua Weill‐Hallé 75013 PARIS France [email protected]

DOUTRELIGNE Sébastien Laboratoire de Biochimie Théorique CNRS 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

EBERHARDT Jérôme Modélisation Moléculaire IGBMC 1, rue LaurentFranceies BP 10142 67404 ILLKIRCH Cedex France [email protected]

DRUART Karen Laboratoire de Biochimie (BIOC) Ecole Polytechnique Route de Saclay 91128 PALAISEAU France [email protected]

FARCE AmauryTherapeutic innovation targetting inflammation – LIRIC – UMR 995 Inserm Faculté de Médecine – Pôle Recherche Place Verdun 59045 LILLE Cedex France amaury.farce‐2@univ‐lille2.fr

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FERBER Mathias Bio‐Informatique Structurale Institut Pasteur 25, rue du docteur Roux 75015 PARIS France [email protected]

GAGEAT CédricInstitut de Biologie Physico‐Chimique (IBPC) CNRS 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

FIORUCCI Sébastien Institut de Chimie de Nice – UMR 7272 CNRS Université de Nice Sophia Antipolis ICN, UMR 7272 CNRS 28, avenue Valrose 06108 NICE Cedex 2 France [email protected]

GAILLARD ThomasLaboratoire de Biochimie (BIOC) Ecole Polytechnique 91128 PALAISEAU Cedex France [email protected]

FLATTERS DelphineUMRS 973, MTi Université Paris Diderot 35, rue H. Brion Bât. Lamarck A (courrier 7113) 75205 PARIS Cedex 13 France delphine.flatters@univ‐paris‐diderot.fr

GARNIER NorbertCentre de Biophysique Moléculaire CNRS Rue Charles Sadron 45071 ORLEANS Cedex 2 France norbert.garnier@cnrs‐orleans.fr

FLOQUET Nicolas Institut des Biomolécules Max Mousseron (IBMM) – UMR5247 CNRS Faculté de Pharmacie 15, avenue Charles Flahault – BP 14491 34093 MONTPELLIER Cedex 05 France nicolas.floquet@univ‐montp1.fr

GELLY Jean‐Christophe INSERM UMR_S 1134,DSIMB, Université Paris Diderot‐ Paris 7,INTS 6, rue Alexandre Cabanel 75739 PARIS Cedex 15 France jean‐christophe.gelly@univ‐paris‐diderot.fr

FORTUNE AntoineDépartement de Pharmacochimie Moléculaire UMR5063 CNRS / Université Joseph Fourier 470, avenue de la chimie 38400 SAINT MARTIN D'HERES France antoine.fortune@ujf‐grenoble.fr

GOFFINONT Stéphane Centre de Biophysique Moléculaire – CNRS UPR 4301 Rue Charles Sadron CS 80054 45071 ORLEANS Cedex 2 France [email protected]

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GRUDININ Sergei LJK CNRS NANO – D Inria Minatec Campus 17, rue des Martyrs 38054 GRENOBLE France [email protected]

IORGA BogdanInstitut de Chimie des Substances Naturelles (ICSN) CNRS 1, avenue de la Terrasse Bât. 27 91198 GIF‐SUR‐YVETTE France [email protected]

GUCA Ewelina Dynamique des Interactions Membranaires Normales et Pathologiques – UMR5235 CNRS‐Université de Montpellier Place Eugène Bataillon – Bât 24 – cc107 34095 MONTPELLIER Cedex 5 France [email protected]

ISENBERG TobiasAVIZ research team Inria Saclay Université Paris‐Sud Bât. 660 91405 ORSAY France [email protected]

Marc GUEROULT LISBP INSA 135, avenue de Rangueil 31077 TOULOUSE France marc.gueroult@insa‐toulouse.fr

JAILLET LéonardNANO‐D Inria Rhône‐Alpes Bâtiment 51 C Minatec Campus 17, rue des Martyrs 38054 GRENOBLE Cedex France [email protected]

HADJ‐SAID JessicaBioénergétique et Ingénierie des Protéines ‐ UMR 7281 IMM 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected]

KAJAVA AndreyCRBM CNRS 1919, route de Mende 34293 MONTPELLIER France [email protected]

HILVERT Donald Laboratory of Organic Chemistry ETH Zurich HCI F339 Vladimir‐Prelog‐Weg 3 ZURICH Suisse [email protected]

KAPLAN EliseCentre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34090 MONTPELLIER Cedex 5 France [email protected]

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KATAVA Marina Laboratoire de Biochimie Théorique CNRS – UPR 9080 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

LAFITE PierreInstitut de Chimie Organique et Analytique Université Orléans – CNRS UMR 7311 BP6759 Rue de Chartres 45067 ORLEANS Cedex 2 France pierre.lafite@univ‐orleans.fr

KELLOU‐TAIRI SafiaPhysico‐Chimie Théorique et Chimie Informatique Université des Sciences et de la Technologie Houari Boumediene – BP 32 El Alia ALGER Algérie [email protected]

LAURIN YoannLaboratoire de Biochimie Théorique Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

KREBS Fanny Biocomputing group Structural Biology & Genomics Dept. – IGBMCUniversité de Strasbourg 1, rue LaurentFranceies 67404 ILLKIRCH‐GRAFFENSTADEN Cedex France [email protected]

LEC Jean‐Christophe UMR 7365 IMoPA – Equipe 3 / Enzymologie Moléculaire et Structurale 9, rue de la Forêt de Haye Bâtiment Biopole 54505 VANDOEUVRE‐LES‐NANCY France jean‐christophe.lec@univ‐lorraine.fr

KRIZNIK AlexandreUMR 7365 CNRS – Université de Lorraine Ingénierie Moléculaire et Physiopathologie Articulaire (IMoPA) –Campus Biologie Santé 9, avenue de la Forêt de Haye – CS 50184 54505 VANDOEUVRE LES NANCY Cedex France alexandre.kriznik@univ‐lorraine.fr

LEGENDRE AudreyUnité de Bioinformatique Structurale Institut Pasteur/CNRS 28, rue du Dr. Roux 75015 PARIS France [email protected]

LABESSE Gilles Centre de Biochimie Structurale INSERM U1054 – CNRS UMR5048 – UM 29, rue de Navacelles 34090 MONTPELLIER Cedex France [email protected]

LEGER ChristopheBioénergétique et Ingénierie des Protéines CNRS / AMU 31, chemin Joseph Aiguier, 13402 MARSEILLE Cedex 20 France [email protected]

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LEROUX GwenaëlleUnité Mathématiques et Informatique Appliquées, du Génome à l'Environnement (MaIAGE) – INRA Domaine de Vilvert 78352 JOUY‐EN‐JOSAS France [email protected]

MARTINY VirginieMolecular Modelling and Structural Crystallography ICSN – CNRS UPR 2301, LabEx LERMIT Avenue de la Terrasse, Bât. 27, Bureau 310 91198 GIF‐SUR‐YVETTE France [email protected]

LIONNE Corinne Centre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34293 MONTPELLIER Cedex 5 France [email protected]

MATHIEU CécileUnité Biologie Fonctionnelle et Adaptative Université Paris Diderot, CNRS UMR 8251 Bâtiment Buffon 4, rue M.A Lagroua Weill Hallé 75013 PARIS France [email protected]

LOUET Maxime Molécules Thérapeutiques in silico (MTi) Université Paris Diderot – Inserm UMR‐S 973 Bât Lamarck A, 4e et 5e étage , Courrier 7113 35, rue Hélène Brion 75205 PARIS Cedex 13 France [email protected]

MEREU IlariaMultiscale Biomolecular Simulation Group Steinbuch Centre for Computing (SCC) Karlsruhe Institute of Technology (KIT) Hermann‐von‐Helmholtz‐Platz 1 76344 EGGENSTEIN‐LEOPOLDSHAFEN Allemagne [email protected]

MANA Zohra Bio‐Logic SAS 1, rue de l'Europe 38640 CLAIX France zohra.mana@bio‐logic.net

MERROUCH Meriem Laboratoire de Bioénergétiques et Ingénieries des Protéines UMR 72‐81 CNRS 31, chemin Joseph Aiguier 13402 MARSEILLE cedex 20 France [email protected]

MAROT ChristopheUMR CNRS 7292 GICC – Equipe 4 Innovation Moléculaire et Thérapeutique UFR Sciences Pharmaceutiques Univ. Tours 31, avenue de Monge 37200 TOURS France Christophe.marot@univ‐orleans.fr

MIAS‐LUCQUIN Dominique Institut de Génétique et Développement de Rennes (IGDR) Université de Rennes 1 2, avenue du Professeur Léon BERNARD 35000 RENNES France [email protected]‐rennes1.fr

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MOLZA Anne‐Elisabeth Institut de Génétique et Développement de Rennes‐ Equipe Structure et Interactions Moléculaires (SIM) – Université de Rennes 1 2, avenue du Pr. Léon Bernard CS 34317 35043 RENNES Cedex France [email protected]

NEVEU EmilieNANO‐D – INRIA DRT/LETI/DACLE/ Bâtiment 51 C Minatec Campus 17, rue des Martyrs 38054 GRENOBLE Cedex France [email protected]

MONET Damien Unité de Bioinformatique Structurale Institut Pasteur 25, rue du Docteur Roux 75015 PARIS France [email protected]

NG FUK CHONGMatthieu INSERM UMR_S 1134 DSIMB 6, rue Alexandre Cabanel 75739 PARIS Cedex 15 France matthieu.ng‐fuk‐[email protected]

MOROY Gautier Molécules Thérapeutiques in silico (MTi) Université Paris Diderot – Inserm UMR‐S 973 35, rue Hélène Brion 75205 PARIS Cedex 13 France gautier.moroy@univ‐paris‐diderot.fr

NGUYEN Minh Khoa Laboratoire Jean Kuntzmann (LJK) INRIA Grenoble Minatec Campus 17, rue des Martyrs 38054 GRENOBLE France minh‐[email protected]

MUNIER‐LEHMANN Hélène Unité de Chimie et Biocatalyse Institut Pasteur, CNRS UMR3523 Unité de Chimie et Biocatalyse 28, rue du Dr Roux 75724 PARIS cedex 15 France [email protected]

PETIT EmileUMR 8251 Equipe RMCX Université Denis‐Diderot Bâtiment A 4, rue Lagroua Well Hallé 75013 PARIS France [email protected]

NARTH ChristopheLaboratoire de Chimie Théorique Université Pierre et Marie Currie Barre 12‐13 4ième étage – CC 137 4, place Jussieu 75252 PARIS France [email protected]

RAHIMOVA RahilaCentre d'études d'agents Pathogènes et Biotechnologies pour la Santé (CPBS) CNRS – Université de Montpellier 1919, route de Mende 34293 MONTPELLIER Cedex 5 France [email protected]

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RAHUEL‐CLERMONT Sophie Laboratoire IMoPA – Equipe Enzymologie Moléculaire et Structurale UMR 7365 CNRS – Université de Lorraine 9, avenue de la Forêt de Haye, CS 50184 54505 VANDOEUVRE‐LES‐NANCY Cedex France sophie.rahuel@univ‐lorraine.fr

RENAULT NicolasTherapeutic innovation targetting inflammation – LIRIC – UMR 995 Inserm – Université Lille 2 / CHRU Lille Pôle Recherche, Place Verdun 59045 LILLE Cedex France nicolas.renault‐3@univ‐lille2.fr

RASOLOHERY InèsMolécules Thérapeutiques in silico (MTi) Université Paris Diderot INSERM UMR‐S 973 Bât Lamarck A, 4e et 5e étage , Courrier 7113 35, rue Hélène Brion 75205 PARIS Cedex 13 France ines.rasolohery@univ‐paris‐diderot.fr

RIEUX CharlotteCentre de Biophysique Moléculaire CNRS Rue Charles Sadron 45071 ORLEANS Cedex 2 France charlotte.rieux@cnrs‐orleans.fr

REBEHMED JosephIMPMC Université Pierre & Marie Curie Case 115 4, place Jussieu 75252 PARIS Cedex 05 France [email protected]

RINALDO DavidSchrödinger GmbH Zeppelinstraße 73 81669 MÜNCHEN Allemagne [email protected]

REDON Stéphane NANO‐D – INRIA DRT/LETI/DACLE/ Bâtiment 51 C Minatec Campus 17, rue des Martyrs 38054 GRENOBLE Cedex France [email protected]

RODRIGUES‐LIMA Fernando Unité Biologie Fonctionnelle et Adaptative Université Paris Diderot – CNRS UMR 8251 4, rue MA Lagroua Weill Halle 75013 PARIS France fernando.rodrigues‐lima@univ‐paris‐diderot.fr

REMAUD‐SIMEONMagali LISBP‐INSAT Insa Toulouse 135, avenue de Rangueil 31077 TOULOUSE Cedex 4 France remaud@insa‐toulouse.fr

SANTOLINI JérômeUMR 8221 CEA/iBiTec‐S/SB2SM Bât. 532 CEA Saclay 91191 GIF‐SUR‐YVETTE Cedex France [email protected]

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SELWA Edithe ICSN CNRS Avenue de la Terrasse Bâtiment 27 91198 GIF‐SUR‐YVETTE France [email protected]

STRATMANN DirkIMPMC – UPMC – Paris VI, UMR 7590 CNRS Campus Jussieu, Couloir 22/23, 4ème étage, Bureau 408 – Case courrier 115 4, place Jussieu 75252 PARIS Cedex 05 France [email protected]

SENAC Caroline Laboratoire d'imagerie biomédicale (LIB) UPMC CNRS INSERM 4, place Jussieu 75005 PARIS France [email protected]

STRICK TerenceEquipe Nanomanipulation de Biomolécules Institut Jacques Monod 15, rue Hélène Brion 75013 PARIS France [email protected]‐paris‐diderot.fr

SIDORE Marlon Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM) CNRS/Université Aix‐Marseille 31, chemin Joseph Aiguier 13402 MARSEILLE Cedex 20 France [email protected]

TALFOURNIER François IMoPA UMR 7365 CNRS – Université de Lorraine 34, cours Léopold 54000 NANCY France francois.talfournier@univ‐lorraine.fr

SOPKOVA‐DE OLIVEIRA SANTOS JanaCERMN Université de Caen Basse‐Normandie Bd Becquerel 14032 CAEN France [email protected]

TALY AntoineLaboratoire de Biochimie Théorique UPR 9080 Institut de Biologie Physico‐Chimique (IBPC) 13, rue Pierre et Marie Curie 75005 PARIS France [email protected]

STINES‐CHAUMEIL Claire Centre de Recherche Paul Pascal CNRS UPR 8641 115, avenue Albert Schweitzer 33600 PESSAC France stines@crpp‐bordeaux.cnrs.fr

TELLIER CharlesUFIP, UMR CNRS n°6286 Université de Nantes 2, rue de la Houssinière BP 92208 44322 NANTES Cedex 3 France charles.tellier@univ‐nantes.fr

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THOMAS Aline Département de Pharmacochimie Moléculaire CNRS – UFR de Pharmacie 470, rue de la Chimie – BP 53 38041 GRENOBLE Cedex 9 France aline.thomas@ujf‐grenoble.fr

VERZEAUX LaurieLaboratoire SiRMa UMR CNRS/URCA MEDyC n°7369 UFR SEN – Bâtiment 18 Chemin des rouliers 51687 REIMS Cedex 2 France [email protected]

TINTILLIER ThibaultIBMM Faculté de Pharmacie 15, avenue Charles Flahault BP 14491 34093 MONTPELLIER Cedex 5 France thibault.tintillier@univ‐montp1.fr

WEIN‐GRATRAUD Sharon Dynamique des Interactions Membranaires Normales et Pathologiques – UMR5235 CNRS‐Université de Montpellier Place Eugène Bataillon – Bât 24 – cc107 34095 MONTPELLIER Cedex 5 France wein@univ‐montp2.fr

TRAORE Seydou CEA Saclay – IBITEC‐S/SB2SM/LBSR Point courrier 22 Bâtiment 144 91191 GIF‐SUR‐YVETTE Cedex France [email protected]

WIENINGER SilkeUnité de Bioinformatique Structurale Institut Pasteur Paris 25‐28, rue du Dr Roux 75015 PARIS France [email protected]

TUBIANA ThibaultInstitut de Biologie Intégrative de la Cellule I2BC CNRS CEA Saclay – Bât 532 91191 GIF‐SUR‐YVETTE Cedex France [email protected]

Lundi 25 Mai Mardi 26 Mai Mercredi 27 mai Jeudi 28 Mai

9h Enz. comme cibles thérap. Visualisation et préd. struct.

PLENIERE 4 PLENIERE 6

Régulation et efficacité Dominique DOUGUET Tobias ISENBERG

PLENIERE 2 Flash n°4 Fabrice ALLAIN

10h Terence STRICK Kamel DJAOUT Mathias FERBER

Hélène MUNIER‐LEHMANN Fanny KREBS Emilie NEVEU

Nicolas BOSC Jana SOPKOVA‐DE OLIVEIRA I. CHAUVOT DE BEAUCHENE

Jessica HADJ‐SAID Pause café Pause café

11h Accueil Pause café

Flash n°5 Jean‐Christophe GELLY

Loic CARRIQUE Florent BARBAULT Stéphane REDON

Sophie RAHUEL‐CLERMONT Inès RASOLOHERY Prix GGMM

12h Marc‐André DELSUC Hiba ABI HUSSEIN Isidro CORTES

Déjeuner Prix poster GT Enzymes Prix poster GGMM

Déjeuner Déjeuner Déjeuner

13h

14h Bienvenue GGMM Rel. dynamique‐fonction

Bienvenue GT Enzymes Ingénierie enz., évol. dirigée PLENIERE 5 Durées présentations

Structures et mécanismes PLENIERE 3 Jochen BLUMBERGER Plénière : 45 min

PLENIERE 1 Donald HILVERT Jérôme EBERHARDT Flash : 1 min

15h Jacqueline CHERFILS Flash n°2 Benoît DAVID Com. orale : 15 min

Jérôme SANTOLINI Thomas GAILLARD Julien DIHARCE

Nolan CHATRON Seydou TRAORE Maxime LOUET LEGENDE

Bertrand CASTAING David RINALDO Présentation Jeu "L'Expert" Présentations Enzymes

16h Pause café Flash n°3 Pause café Présentations communes

PROGRAMME

16h Pause café Flash n°3 Pause café Présentations communes

Pause café Présentations GGMM

Flash n°1 Matthieu CHAVENT Flash poster

Ewelina GUCA Posters (1h45) Kaouther BEN OUIRANE Sessions posters

17h Pierre LAFITE Jean‐Pierre DUNEAU

Claire STINES‐CHAUMEIL Christophe NARTH

Jean‐Christophe LEC AG GGMM

Présentation Elsevier

18h Apéritif de bienvenue Dégustation

Posters (1h30) produits régionaux

Apéritif Sétois Posters (1h30)

19h

Diner Diner Diner

20h

Soirée festive

21h Jeu "L'Expert est

dans la salle"

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