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Structure-based peptide design

Equipe 1 du Dr P. Tufféry (DR Inserm)


Team's publications / Organization Chart

Recent progress in synthesis, bioavailability, systemic stability, and selective cell penetration of peptide make them a promising alternative to supplement small compounds as candidate therapeutics, which motivates studies to understand molecular mechanisms underlying their biological functions. Peptides can be endogeneous  (hormons, neuropeptides, ...) or exogeneous (AMPs, toxins, ...). "Short Linear Motifs" - SLiMs - that correspond to conserved protein fragments located in disordered regions of proteins, but also more generally protein fragments such as for instance those used in the context of vaccine design can also by extension be assimilated to peptides. Peptide biological functions  involve multiple interactions with membranes, nucleic acids and proteins. Particularly, peptide-protein interactions are involved in the regulation of cell and tissue activity, and in the immune system.

Our research is about the in silico characterization of peptides and their interactions. Several directions are considered: 

Thème 1 : Peptide and protein fragment de novo 3D structure prediction. 

Thème 2 : Peptide-protéine interactions .

Thème 3 : Interaction specificity and similarity. 

Thème 4 : Structural Bioinformatics.

In addition, the team hosts the RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. In this context, we also have a more classical structural bioinformatics activity, about the analysis and modeling of structure and function of proteins. On-line tools are available through our Mobyle portal.

Peptide 3D structure prediction

pepfold The knowledge of peptide 3D structure is a prerequisite for an accurate caracterization of their funcitonal interactions. When estimates of the number of peptide sequences occuring in life are of several millions, only ~ 2 000 structures of peptides between 10 and 50 amino acids were know early 2014.

We have developed  PEP-FOLD[3], an original and accurate approach for the prediction of peptide structure from sequence. PEP-FOLD, still in progress, is able to predict the structure of peptides from 9 up to 50 amino acids, linear or with disulfide bonds.

Peptide protein interactions

The detailled caracterization of peptide-protein interactions relies on a 2 step protocol, with (i) the identification of the binding site on protein surface, and (ii) peptide docking to get an accurate conformation of the complex. Our recent efforts have led to the development of PEP-SiteFinder, an approach to predict peptide binding site given the sequence of the peptide and the structure of a protein.

Interaction specificity and similarity

Conformational similarities are a key to assist protein structure modeling (similar folds), but also to caracerize the specificity of interactions, searching for non linear (patch)  similarities. The choice of the criterion to measure similarity largely conditions the quality of the results. Commonly used criteria have important limitations such as the dependence on the dimension of the vectors to compare, that introduces fuzziness in the signal.  We have recently proposed a new criterion to measure 3D similarity based on a Binet-Cauchy kernel. It is a measure of the geometrical correlation between structures  [1]. We are working on the application of such criterion to structural alignment, and to the general search of similarities among collections of un-aligned coordinates.

Structural Bioinformatics


Our developments about peptides can also be applied to the modeling of protein structure. We have recently developped SA-Frag[2], an approach to predict candidate 3D fragments from protein sequence. De novo prediction from sequence can also be applied to the modeling of protein regions out of the scope of homology modeling techniques.  Finally, we also participate in the design/development of Mobyle, a framework for the online publication of bioinformatic tools.

Selected recent publications

[1] de Vries SJ, Rey J, Schindler CEM, Zacharias M, Tuffery P.
The pepATTRACT web server for blind, large-scale peptide-protein docking.
Nucleic Acids Research / April 29, 2017 /  in press
[2] Rasolohery I, Moroy G, Guyon F
PatchSearch: A Fast Computational Method for Off-Target Detection.
J. Chem. Inf. Model. / March 24, 2017 /  24;57(4):769-777.
[3] Lamiable A, Thevenet P, Tuffery P
A critical assessment of Hidden Markov Model sub-optimal sampling strategies applied to the generation of peptide 3D models.
J. Comput. Chem. / June 14, 2016 / in press
[4] Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P
PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex.
Nucleic Acids Research / April 29, 2016 /  in press
[5] Yu J, Vavrusa M, Andreani J, Rey J, Tufféry P, Guerois R.
InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information.
Nucleic Acids Research / April 29, 2016 /  in press
[6] Guyon F, Martz F, Vavrusa M, Bécot J, Rey J, Tufféry P.
BCSearch: fast structural fragment mining over large collections of protein structures.
Nucleic Acids Research / July 01, 2015 /  2015 43(W1):W378-82.
[7] Labbé C, Rey J, Lagorce D, Vavruša M, Becot J, Sperandio O, Villoutreix B, Tuffery P, Miteva M
MTiOpenScreen: a web server for structure-based virtual screening.
Nucleic Acids Research / April 03, 2015 /  43(W1):W448-54
[8] Julien Rey, Patrick Deschavanne , Pierre Tuffery
BactPepDB: a database of predicted peptides from an exhaustive survey of complete prokaryote genomes
database / October 09, 2014 / bau119.
[9] F. Guyon , P. Tufféry
Fast protein fragment similarity scoring using a Binet-Cauchy Kernel.
Bioinformatics / 2014 /  30 (6): 792-800
[10] Shen Y., Picord G., Guyon F., Tufféry P.
Detecting protein candidate fragments using a structuralalphabet profile comparison approach.
Plos ONE / 2013 / 8(11):e80493
[11] Tufféry P., Derreumaux P.
Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches.
Journal of the Royal Society interface / 2012 /  9(66):20-33
[12] Thévenet P, Shen Y, Maupetit J, Guyon F, Derreumaux P, Tufféry P.
PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides.
Nucleic Acids Research / 2012 /  40(Web Server issue):W288-93

Structure-based peptide design

Team leader :

    (+33)1 57 27 83 74

Permanent Members :

    (+33)1 57 27 83 98
GUYON Frédéric
    (+33)1 57 27 83 76
MOROY Gautier
    (+33)1 57 27 83 85
    (+33)1 57 27 83 90
REY Julien
    (+33)1 57 27 83 95

Non Permanent Members :

KARAMI Yasaman
    (+33)1 57 27 84 37
POSTIC Guillaume
    (+33)1 57 27 83 81
Molécules Thérapeutiques in silico (MTi)
Université Paris Diderot - Inserm UMR-S 973
Bât Lamarck A, 4th & 5th floor , Mailbox 7113
35 Rue Hélène Brion
75205 PARIS CEDEX 13

Phone : (331) 57 27 83 86
Fax: : (331) 57 27 83 72

Molécules Thérapeutiques in silico (MTi)
Université Paris Diderot - Inserm UMR-S 973
Bât Lamarck A, 4th & 5th floor , Mailbox 7113
35 Rue Hélène Brion
75205 PARIS CEDEX 13

Phone : (331) 57 27 83 86
Fax: : (331) 57 27 83 72

Molécules Thérapeutiques in silico (MTi)
Université Paris Diderot - Inserm UMR-S 973
Bât Lamarck A, 4th & 5th floor , Mailbox 7113
35 Rue Hélène Brion
75205 PARIS CEDEX 13

Phone : (331) 57 27 83 86
Fax: : (331) 57 27 83 72