GeoPacker: A novel deep learning framework for protein side-chain modeling.

ABSTRACT: Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in protein sequence design. However, highly efficient and accurate protein side-chain prediction methods that can give detailed atomic interactions are still lacking. In the present study, we developed a deep learning based method, GeoPacker, that uses geometric deep learning coupled ResNet for protein side-chain modeling. GeoPacker explicitly represents atomic interactions with rotational and translational invariance for information extraction of relative locations. GeoPacker outperformed the state-of-the-art energy function-based methods in side-cha

SUBMITTER: Liu J 

PROVIDER: S-EPMC9667900 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature