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Journal Articles Bioinformatics Year : 2020

A Reinforcement-Learning-Based Approach to Enhance Exhaustive Protein Loop Sampling

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Abstract

Motivation: Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation. Results: In this paper, we propose a method to exhaustively sample the conformational space of protein loops. It exploits structural information encoded in a large library of three-residue fragments, and enforces loop-closure using a closed-form inverse kinematics solver. A novel reinforcement-learning-based approach is applied to accelerate sampling while preserving diversity. The performance of our method is showcased on benchmark datasets involving 9-, 12-and 15-residue loops. In addition, more detailed results presented for streptavidin illustrate the ability of the method to exhaustively sample the conformational space of loops presenting several meta-stable conformations. Availability: We are developing a software package called MoMA (for Molecular Motion Algorithms), which includes modeling tools and algorithms to sample conformations and transition paths of biomolecules, including the application described in this work. The binaries can be provided upon request and a web application will also be implemented in the short future.
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Dates and versions

hal-02289207 , version 1 (16-09-2019)

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Amélie Barozet, Kevin Molloy, Marc Vaisset, Thierry Simeon, Juan Cortés. A Reinforcement-Learning-Based Approach to Enhance Exhaustive Protein Loop Sampling. Bioinformatics, 2020, 36 (4), pp.1099-1106. ⟨10.1093/bioinformatics/btz684⟩. ⟨hal-02289207⟩
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