The team is specialized in structural bioinformatics and computational systems biology. In this last topic, the team utilizes the formalism of deterministic and stochastic differential equations to model generic biological systems, with a focus on the role of intrinsic noise and its relation to the complexity of the system and other biophysical characteristics.

In the field of structural bioinformatics, the team’s research is mainly centered on the development of knowledge-based, physics-driven, software tools for predicting and analyzing protein structure, stability, interactions and function, and on the application of these computational tools to biological systems of interest in collaboration with experimenters. Most of their methods are based on coarse-grained protein structure representations and force fields, which allow fast, large-scale, analyses, while maintaining a good level of accuracy. With the help of free energy evaluations using statistical potentials and artificial neural network techniques, they developed original and efficient methods for predicting protein stability (SCooP), as well as thermodynamic stability changes (PoPMuSiC), thermal stability changes (HoTMuSiC), and affinity changes (BeATMuSiC) upon point mutations. They also developed methods for identifying deleterious, disease-causing mutations (SNPMuSiC). The team applies these software tools to the rational design of modified proteins, with valuable results. The developed prediction tools are available to the scientific community on the websites and

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