High-throughput prediction of protein conformational distributions with subsampled AlphaFold2.

ABSTRACT: This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulatin

SUBMITTER: Monteiro da Silva G 

PROVIDER: S-EPMC10973385 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature