Abstract
MoMA-LoopSampler is a sampling method that globally explores the conformational space of flexible protein loops. It combines a large structural library of three-residue fragments and a novel reinforcement-learning-based approach to accelerate the sampling process while maintaining diversity. The method generates a set of statistically likely loop states satisfying geometric constraints, and its ability to sample experimentally observed conformations has been demonstrated. This paper presents a web user interface to MoMA-LoopSampler through the illustration of a typical use-case. MoMA-LoopSampler is freely available at: https://moma.laas.fr/applications/LoopSampler/. We recommend users to create an account, but anonymous access is possible. In most cases, jobs are completed within a few minutes. The waiting time may increase depending on the server load, but it very rarely exceeds an hour. For users requiring more intensive use, binaries can be provided upon request. Supplementary data are available at Bioinformatics online.
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