Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer.

ABSTRACT:

Motivation

The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph.

Results

The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets an

SUBMITTER: Wu T 

PROVIDER: S-EPMC10191610 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature