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Data for: Fast sampling of protein conformational dynamics

Sauer, Michael1; Mondal, Souvik1; Neff, Brandon1; Maiti, Sthitadhi1; Heyden, Matthias1

Published Mar 24, 2026 on Dryad. https://doi.org/10.5061/dryad.m37pvmdfs

Data files

Mar 24, 2026 version files 1.35 GB

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Abstract

Protein function often depends on dynamic transitions between conformations rather than just static structures. However, our current ability to characterize or predict such dynamics lags behind recent advances in protein structure prediction. Enhanced sampling methods can speed up molecular dynamics simulations to study protein conformational transitions, but require prior knowledge of key collective motions involved. Here, we demonstrate for a series of proteins of varying complexity that the required information is encoded in anharmonic low-frequency vibrations. Using recently developed methods, we show that this information can be easily extracted from short dynamics simulations without requiring prior knowledge. Combined with enhanced sampling, we correctly predict conformational transitions in all test proteins and generate highly reproducible free energy landscapes. This allows for the rapid generation of accurate protein conformational ensembles, which is critical to unravel the complex relationship between protein sequence, structure, and dynamics.