Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM.

ABSTRACT: Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc)

SUBMITTER: Farrell DP 

PROVIDER: S-EPMC7467173 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature