MoMA-LoopSampler: a web server to exhaustively sample protein loop conformations

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.

Automated summary

MoMA-LoopSampler is a sampling method that explores the conformational space of flexible protein loops globally, using a large structural library and reinforcement-learning-based approach. It generates a set of statistically likely loop states satisfying geometric constraints and can sample experimentally observed conformations.