Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps.

ABSTRACT: Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing structure prediction method at CASP14) on cryo-EM refinement using the Phenix refinement suite for Alpha

SUBMITTER: Alshammari M 

PROVIDER: S-EPMC10361706 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature