Bip:Bip

Présentation

About Bip:Bip

The project Bip:Bip promotes an integrated use of known and modelled three-dimensional structures of proteins as an aid in complete genome annotation. The architectural organization of biological macromolecules is essential for the understanding and manipulation of their function. In parallel, more and more experimental techniques (transcriptomics, proteomics, high-resolution microscopies, …) are now providing new and detailed insights into cellular processes. However, each method gives only access to a small fraction of the information necessary for a complete view of each system. Connecting them efficiently will be instrumental for proper simulations of living organisms.

The project follows a multi-scale and multi-techniques approach for integrative structural bioinformatics:

1. from the structure of proteins alone to protein-ligand complexes and large multi-protein assemblies,
2. from isolated structures to dynamically relevant structure ensembles and
3. from individual proteins to whole families and entire proteomes.

The project brings together different fields in modern bioinformatics that we think will mostly benefit from novel multi-scale solutions. The algorithmic framework for is Bayesian statistics, in order to make inferences in an optimal way and to obtain rigorous estimates of the reliability of the results. The developed methods will be made available to the researchers with the help of the French bioinformatics platform, and by deposition of results in dedicated databases.

Partnership

The project unites 13 partners in France with complementary competences:

• determination of multi-component complexes
• large-scale structure prediction
• docking/ drug design
• structure-aided annotation
• data bases
• web-based services

The current partners are:

• UMR 3258, Nilges / Unité de Bio-informatique structurale
• UMR-S 973, Tuffery / Molécules Thérapeutiques par approches in silico
• UMR 5048, Labesse / Centre de Biochimie Structurale
• UMR 6098, Henrissat / Architecture et Fonction des Macromolécules Biologiques
• UMR 7357, Poch / ICube, sciences de l’ingénieur, de l’informatique et de l’imagerie
• URA 2006, Guerois / Commissariat à l’energie atomique et aux énergies alternatives
• UMR 6286, Sanejouand / UFIP
• UMR 7161, Liberti / Laboratoire d’Informatique de l’Ecole Polytechnique
• UMR 7104, Dejaegere / IGBMC, CNRS DELEGATION ALSACE
• UMR 5247, Floquet / IBMM Centre National de la Recherche Scientifique
• UMR 3258, Delarue / Unité de Dynamique Structurale des Macromolecules
• UMR 3258, Fronzes / Groupe Biologie Structurale de Sécretion Bactérienne
• UMR 3258, Chamot-Rooke / Laboratoire des Mécanismes Réactionnels

Connection to other networks

The bip:bip network connects not only a large fraction of the bioinformatics community in France, but also provides ties to other networks. For example, Bip:Bip partners are members of :

• the EQUIPEX CACSICE
• the EQUIPEX FRISBI
• the network of national bio-informatics platforms RENABI

Publications

2012

1. Andreani J, Faure G, Guerois R (2012). Versatility and Invariance in the Evolution of Homologous Heteromeric Interfaces. PLoS Comput Biol 8: e1002677.
2. Aspeborg H, Coutinho PM, Wang Y, Brumer H 3rd, Henrissat B (2012). Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5). BMC Evol Biol. 12:186.
3. Luu TD, Rusu AM, Walter V, Ripp R, Moulinier L, Muller J, Toursel T, Thompson JD, Poch O, Nguyen H. (2012). MSV3d: database of human MisSense variants mapped to 3D protein structure. Database (Oxford); bas018
4. Luu TD, Rusu AM, Walter V, Linard B, Poidevin L, Ripp R, Moulinier L, Muller J, Raffelsberger W, Wicker N, Lecompte O, Thompson JD, Poch O, Nguyen H. (2012). KD4v: Comprehensible Knowledge Discovery System For Missense Variant. Nucleic Acids Res. W71-75

2013

1. Nguyen H, Luu TD, Poch O , Thompson JD. (2013) Knowledge Discovery from a Variant Database using Inductive Logic Programming. Bioinformatics and Biology Insights.
2. T.E. Malliavin, A. Mucherino, M. Nilges (2013). Distance Geometry in Structural Biology: New Perspectives. In: “Distance Geometry: Theory, Methods and Applications”, A. Mucherino, C. Lavor, L. Liberti, N. Maculan (Eds.), Springer, 329-350, 2013.
3. Andrea Cassioli, Oktay Günlük, Carlile Lavor, Leo Liberti (2013). Discretization vertex Orders in Distance Geometry, IBM Technical Report RC25434.
4. Louet M, Karakas E, Perret A, Perahia D, Martinez J, Floquet N (2013). Conformational restriction of G-proteins Coupled Receptors (GPCRs) upon complexation to G-proteins: a putative activation mode of GPCRs? FEBS Lett. 587(16):2656-61.
5. Shen Y., Picord G., Guyon F., Tufféry P. (2013). Detecting protein candidate fragments using a structural alphabet profile comparison approach.  Plos ONE  8(11):e80493
6. Fabio Sterpone, Simone Melchionna, Pierre Tuffery, Samuela Pasquali, Normand Mousseau, Tristan Cragnolini, Yassmine Chebaro, Jean-Francois Saint-Pierre, Maria Kalimeri, Alessandro Barducci, Yohan Laurin, Alex Tek, Marc Baaden, Phuong Hoang Nguyen, and Philippe Derreumaux (2013). The opep coarse-grained protein model: from single molecules, amyloid formation, role of macromolecular crowding and hydrodynamics to rna/dna complexes. Chem. Soc. Rev., in press, DOI:10.1039/C4CS00048J.
7. Pérot S, Regad L, Reynès C, Spérandio O, Miteva MA, Villoutreix BO, Camproux AC (2013). Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction. PLoS One. 8:e63730.
8. Thomas Gaillard, Benjamin B L Schwarz, Yassmine Chebaro, Roland H Stote and Annick Dejaegere (2013). Protein structural statistics with PSS J. Chem. Inf. Model. 2013, 53, 2471-2482
9. Y. Chebaro, I. Amal, N. Rochel, C. Rochette-Egly, R. H. Stote, A. Dejaegere (2013). Phosphorylation of the Retinoic Acid Receptor alpha induces a mechanical allosteric regulation and changes in internal dynamics. Plos Comp Biol. 9, e1003012
10. Smaoui MR, Poitevin F, Delarue M, Koehl P, Orland H, Waldispühl J (2013). Computational assembly of polymorphic amyloid fibrils reveals stable aggregates. Biophys J. 104:683-93.
11. Structural basis for ion permeation mechanism in pentameric ligand-gated ion channels (2013). Sauguet L, Poitevin F, Murail S, Van Renterghem C, Moraga-Cid G, Malherbe L, Thompson AW, Koehl P, Corringer PJ, Baaden M, Delarue M. EMBO J. 32:728-41.
12. Allot A, Anno YN, Poidevin L, Ripp R, Poch O, Lecompte O. (2013) PARSEC: PAtteRn SEarch and Contextualization. Bioinformatics, 29(20):2643-44

2014

1. Liberti L, Lavor, Maculan, Mucherino (2014). Euclidean Distance Geometry and Applications, SIAM Review 56(1):3-69.
2. Liberti, Masson, Lee, Lavor, Mucherino (2014). On the number of realizations of certain Henneberg graphs arising in protein conformation”, Discrete Applied Mathematics, 165:213-232.
3. Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B (2014). The Carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490-D495
4. F. Guyon , P. Tufféry (2014). Fast protein fragment similarity scoring using a Binet-Cauchy Kernel.  Bioinformatics 30 (6): 792-800
5. P. Thevenet and P. Tuffery (2014). Exploring a sub-optimal Hidden Markov Model sampling approach for de novo peptide structure modeling. Proceedings of Bioinformatics 2014, 3-6 mars 2014, Angers, France.
6. Sauguet L, Shahsavar A, Poitevin F, Huon C, Menny A, Nemecz À, Haouz A, Changeux JP, Corringer PJ, Delarue M (2014). Crystal structures of a pentameric ligand-gated ion channel provide a mechanism for activation. Proc Natl Acad Sci U S A. 2014 Jan 21;111(3):966-71.
7. Khenoussi W, Vanhoutreve R, Poch O, Thompson JD. (2014). SIBIS: A Bayesian model for inconsistent protein sequence estimation, Bioinformatics, in press

Web services

Web server dedicated to the valorisation of the research programs

• InterEvol (database and applications)
• SA-Frag server
• Pepfold server
• PEP-SiteFinder
• Protein structure modelling
• AquaSax
• HHalign
• HHalign