MDSubSampler: a posteriori sampling of important protein conformations from biomolecular simulations

Abstract

Motivation: Molecular dynamics (MD) simulations have become routine tools for the study of protein dynamics and function. Thanks to faster GPU-based algorithms, atomistic and coarse-grained simulations are being used to explore biological functions over the microsecond timescale, yielding terabytes of data spanning multiple trajectories, thereby extracting relevant protein conformations without losing important information is often challenging. Results: We present MDSubSampler, a Python library and toolkit for a posteriori subsampling of data from multiple trajectories. This toolkit provides access to uniform, random, stratified, weighted sampling, and bootstrapping sampling methods. Sampling can be performed under the constraint of preserving the original distribution of relevant geometrical properties. Possible applications include simulations post-processing, noise reduction, and structures selection for ensemble docking.