Description du sujet de thèse

Modeling multiple protein conformations and dynamic protein-protein interfaces using evolutionary signals.

The PhD project will focus on developing computational methods to model alternative conformations of proteins, both as isolated entities and in interaction with other proteins, using AlphaFold2 (AF2) and evolutionary data. The student will enhance an AF2-based pipeline to explore conformational diversity, design strategies for MSA editing to guide predictions, and investigate how AF2 exploits coevolutionary and conservation signals. The project will also model dynamic protein-protein interfaces, particularly those involving intrinsically disordered regions (IDRs), by combining structural templates with novel scoring methods based on classical statistical techniques and deep learning.

Contexte de travail

The thesis will be conducted at the Institute for Integrative Biology of the Cell (I2BC), within the Molecular Assemblies and Genome Integrity (MAGI) team. The group operates at the interface between structural bioinformatics and molecular evolution and is embedded in a multidisciplinary lab with experimental and computational researchers. The PhD student will work under the supervision of Dr. Zea, contributing to the ANR-funded SPPICES (Scoring and Predicting Protein Interactions and Conformations based on Evolutionary Signals) project. They will benefit from access to high-performance computing facilities and collaboration opportunities.