3D-equivariant graph neural networks for protein model quality assessment.

ABSTRACT:

Motivation

Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods.

Results

We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structu

SUBMITTER: Chen C 

PROVIDER: S-EPMC10089647 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature