Machine-learning-based methods to generate conformational ensembles of disordered proteins.

ABSTRACT: Intrinsically disordered proteins are characterized by a conformational ensemble. While computational approaches such as molecular dynamics simulations have been used to generate such ensembles, their computational costs can be prohibitive. An alternative approach is to learn from data and train machine-learning models to generate conformational ensembles of disordered proteins. This has been a relatively unexplored approach, and in this work we demonstrate a proof-of-principle approach to do so. Specifically, we devised a two-stage computational pipeline: in the first stage, we employed supervised machine-learning models to predict ensemble-derived two-dimensional (2D) properties of a sequence, given the conformational ensemble of a closely related sequence. In the second stage, we used d

SUBMITTER: Taneja I 

PROVIDER: S-EPMC10808026 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature