ArticleScience advances2026
Fast sampling of protein conformational dynamics.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From Enhanced Sampling to Human-Readable Representations of Protein Dynamics.Journal of chemical theory and computation · 2026Article
- Adaptive Normal Mode Sampling (aMDeNM) Enhances Exploration of Protein Conformational Space and Reveals the Functional Role of Frequency Coupling.Journal of chemical theory and computation · 2026Article
- AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.Journal of chemical information and modeling · 2026Article
- High-Throughput Computation of Anharmonic Low-Frequency Protein Vibrations.The journal of physical chemistry. B · 2025Article
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Authors and funding
5 authors.
Funding
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.
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