Single-sequence protein structure prediction using a language model and deep learning.

ABSTRACT: AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents C_α backbone geometry in a translationally and rotationally

SUBMITTER: Chowdhury R 

PROVIDER: S-EPMC10440047 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature