When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14—(tFold for CASP14 contact prediction). Issue 12 (23rd September 2021)

• Record Type: Journal Article
• Authors: Shen, Tao; Wu, Jiaxiang; Lan, Haidong; Zheng, Liangzhen; Pei, Jianguo; Wang, Sheng; Liu, Wei; Huang, Junzhou
• Other Names: Moult John guestEditor.; Kryshtafovych Andriy guestEditor.
• Is Part Of: Proteins. Volume 89:Issue 12(2021)
• Page Start: 1901
• Page End: 1910
• Publication Date: 2021-09-23
• Subjects: CASP14 -- contact prediction -- deep convolutional residual neural network -- protein folding; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.26232
• Languages: English
• ISSNs: 0887-3585

Abstract

Abstract: In this paper, we report our tFold framework's performance on the inter‐residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and structural decoys under an ultra‐deep network architecture. Squeeze‐excitation and axial attention mechanisms are employed to effectively capture inter‐residue interactions. In CASP14, our best predictor achieves 41.78% in the averaged top‐L precision for long‐range contacts for all the 22 free‐modeling (FM) targets, and ranked 1st among all the 60 participating teams. The tFold web server is now freely available at: https://drug.ai.tencent.com/console/en/tfold .