A reinforcement-learning-based approach to enhance exhaustive protein loop sampling
Author(s) -
Amélie Barozet,
Kevin Molloy,
Marc Vaisset,
Thierry Siméon,
Juan Cortés
Publication year - 2019
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz684
Subject(s) - computer science , reinforcement learning , representation (politics) , software , benchmark (surveying) , solver , loop (graph theory) , algorithm , flexibility (engineering) , artificial intelligence , mathematics , statistics , geodesy , combinatorics , politics , political science , law , programming language , geography
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.
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