Data for: Fast sampling of protein conformational dynamics

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.