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While the structure of rigid domains can be accurately determined using experimental methods or predictors such as AlphaFold2, the structural study of flexible regions remains a challenge. It requires computational methods for the construction of conformational ensemble models that are fitted or refined on the basis of experimental measurements. In recent years, we have developed several algorithms, based on fragment databases and robotics-inspired techniques, for the conformational sampling of flexible loops [3] and intrinsically disordered regions [4]. Building on this work, we are currently developing a unified approach to sample conformations of proteins with complex architectures composed of rigid and flexible regions. Our approach integrates a multi-agent reinforcement learning technique to improve sampling performance while taking into account the specificities of each flexible/disoriented region of the protein.

[1] I. Clerc, A. Sagar, A. Barducci, N. Sibille, P. Bernadó, J. Cortés. The diversity of molecular interactions involving intrinsically disordered proteins: A molecular modeling perspective. Computational and Structural Biotechnology Journal, 19:3817-3828, 2021.
[2] A. Barozet, P. Chacón, J. Cortés. Current approaches to flexible loop modeling. Current Research in Structural Biology, 3:187-191, 2021.
[3] A. Barozet, K. Molloy, M. Vaisset, T. Siméon, H. Minoux, J. Cortés. A reinforcement learning-based approach to enhance exhaustive protein loop sampling. Bioinformatics, 36(4):1099-1106, 2020.
[4] A. Estañna, N. Sibille, E. Delaforge, M. Vaisset, J. Cortés, P. Bernadó. Realistic ensemble models of intrinsically disordered proteins using a structure-encoding coil database. Structure, 27(2):381-391, 2019.

Publié le mars 10, 2023