An end-to-end deep learning method for protein side-chain packing and inverse folding.

ABSTRACT: Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× c

SUBMITTER: McPartlon M 

PROVIDER: S-EPMC10266014 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature