Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2.

ABSTRACT: This paper presents a novel 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 NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted chang

SUBMITTER: da Silva GM 

PROVIDER: S-EPMC10402055 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature