Modeling protein conformational ensembles by guiding AlphaFold2 with Double Electron Electron Resonance (DEER) distance distributions.

ABSTRACT: We describe a modified version of AlphaFold2 that incorporates experimental distance distributions into the network architecture for protein structure prediction. Harnessing the OpenFold platform, we fine-tune AlphaFold2 on structurally dissimilar proteins to explicitly model distance distributions between spin labels determined from Double Electron-Electron Resonance (DEER) spectroscopy. We benchmark the performance of the modified AlphaFold2, refer to as DEERFold, in switching the predicted conformations of a set of membrane transporters using experimental DEER distance distributions. Guided by sparse sets of simulated distance distributions, we showcase the generality of DEERFold in predicting conformational ensembles on a large benchmark set of water soluble and membrane proteins. We f

SUBMITTER: Wu T 

PROVIDER: S-EPMC12318079 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature