DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.

ABSTRACT:

Motivation

Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures.

Results

Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular m

SUBMITTER: Lee JW 

PROVIDER: S-EPMC10699847 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature