z-logo
open-access-imgOpen Access
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom