Structural Bioinformatics - CNRS URA 2185  

  HEADProf. NILGES Michael /
  MEMBERSDr. BLONDEL Arnaud / Dr. CHAU Pak-Lee / DUCLERT_SAVATIER Nathalie / Dr. HUYNH Tru / Dr. MALLIAVIN Thérèse / Dr. STOVEN Véronique / Dr. YERAMIAN Edouard
Dr. MASGRAU Laura / Dr. MARKWICK Phineus / GIGANTI David / BARDIAUX Benjamin / BERNARD Aymeric / PERIN Olivier / LAINE Elodie / BASTIANELLI Giaccomo

  Annual Report

The aim of our research is to complement structural studies (X-rays, NMR, Electron microscopy) with in silico studies, to:

  • better determine and predict three-dimensional structures;

  • better understand molecular recognition and molecular interactions.

Our research topics include medically relevant molecular processes (infectious diseases, cancer, and the action of general anesthetics). Collaboration with experimental groups on campus, and our own experimental projects, are of fundamental importance for the group.

We develop strategies for the structural analysis of NMR data to make experimental structure determination more reliable, and allow, for the first time, to obtain an unbiased estimate of quality of an NMR structure. We apply similar methods for structure prediction. We mostly use our homology modeling “pipeline” to answer questions about protein function. Other developments include new probabilistic methods for sequence alignment RNA structure prediction, and gene prediction by physics based genome analysis.

We study the dynamics of protein-protein interactions by docking and molecular dynamics calculations. This provides us new insights into the interplay between protein flexibility and molecular recognition, and the thermodynamics of protein-ligand interactions. The prediction of conformational changes during the binding of two proteins, or a protein and a small ligand, remains an important aim. Using a novel molecular dynamics method, we have obtained very encouraging results recently for the protein HasA binding to heme.

The field of protein-ligand interactions has fundamental as well as more applied aspects. In several collaborations with experimental groups we use empirical strategies for ligand docking and virtual screening. Targets include proteins from P. falciparum, T. brucei, T. cruzi, M. tuberculosis. We have identified several inhibitors for proteins from P. falciparum , M. tuberculosis that could be validated experimentally.

Keywords: Structural Bioinformatics, Molecular Dynamics, Genome Analysis, Gene Prediction, Structure Prediction, Protein-Protein Interactions, NMR data analysis


Illustration of an important part of the activity of the unit. Starting from structural information for a protein, we use various methods to predict and characterize its interactions with other molecules. The results serve as input for further experimental studies.


W. Rieping, M. Habeck, and M. Nilges. Inferential structure determination. Science, 309:303–306, 2005.

R. Grünberg, M. Nilges, and J. Leckner. Flexibility and conformational entropy in protein-protein binding. Structure, 14(4):683-93, 2006.

P. L. Chau, P.N.M. Hoang, S. Picaud and P. Jedlovszky (2007) A possible mechanism for pressure reversal of general anaesthetics from molecular simulations. Chem. Phys. Lett. 438, 294-297, 2007

A. Blondel. Ensemble variance in free energy calculations by thermodynamic integration: theory, optimal ’alchemical’ path, and practical solutions. J Comput Chem, 25(7):985–993, May 2004.

F. Tekaia and E. Yeramian. Evolution of proteomes: Fundamental signatures and global trends in amino acid compositions. BMC Genomics 7:307, 2006.

  Web Site

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Activity Reports 2007 - Institut Pasteur
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