Machine Learning Generation of Dynamic Protein Conformational Ensembles.

ABSTRACT: Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically dis

SUBMITTER: Zheng LE 

PROVIDER: S-EPMC10220786 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature