Improving deep learning-based protein distance prediction in CASP14.

ABSTRACT: Accurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different

SUBMITTER: Guo Z 

PROVIDER: S-EPMC8504632 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature