Protein inter‐residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14. Issue 12 (19th August 2021)

• Record Type: Journal Article
• Authors: Li, Yang; Zhang, Chengxin; Zheng, Wei; Zhou, Xiaogen; Bell, Eric W.; Yu, Dong‐Jun; Zhang, Yang
• Other Names: Moult John guestEditor.; Kryshtafovych Andriy guestEditor.
• Is Part Of: Proteins. Volume 89:Issue 12(2021)
• Page Start: 1911
• Page End: 1921
• Publication Date: 2021-08-19
• Subjects: CASP -- coevolution -- contact‐map prediction -- deep learning -- protein structure prediction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.26211
• Languages: English
• ISSNs: 0887-3585

Abstract

Abstract: This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning‐based contact/distance predictor was employed based on the ensemble of two complementary coevolution features coupling with deep residual networks. We also improved our multiple sequence alignment (MSA) generation protocol with wholesale meta‐genome sequence databases. On 22 CASP14 free modeling (FM) targets, the proposed model achieved a top‐ L /5 long‐range precision of 63.8% and a mean distance bin error of 1.494. Based on the predicted distance potentials, 11 out of 22 FM targets and all of the 14 FM/template‐based modeling (TBM) targets have correctly predicted folds (TM‐score >0.5), suggesting that our approach can provide reliable distance potentials for ab initio protein folding.