AlphaFold2's training set powers its predictions of fold-switched conformations.

ABSTRACT: AlphaFold2 (AF2), a deep-learning based model that predicts protein structures from their amino acid sequences, has recently been used to predict multiple protein conformations. In some cases, AF2 has successfully predicted both dominant and alternative conformations of fold-switching proteins, which remodel their secondary and tertiary structures in response to cellular stimuli. Whether AF2 has learned enough protein folding principles to reliably predict alternative conformations outside of its training set is unclear. Here, we address this question by assessing whether CFold-an implementation of the AF2 network trained on a more limited subset of experimentally determined protein structures- predicts alternative conformations of eight fold switchers from six protein families. Previous w

SUBMITTER: Schafer JW 

PROVIDER: S-EPMC11722258 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature