Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

ABSTRACT: The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor. Both benchmark tests and CASP results show that the new pipeline is a considerable improvement over the original trRosetta, and it is faster and requires less computing resources, completing the entire modeling process in a median < 3 h in CASP14.

SUBMITTER: Anishchenko I 

PROVIDER: S-EPMC8616808 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature