| Literature DB >> 34473376 |
Tao Shen1, Jiaxiang Wu1, Haidong Lan1, Liangzhen Zheng1, Jianguo Pei1, Sheng Wang1, Wei Liu1, Junzhou Huang1.
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.Entities:
Keywords: CASP14; contact prediction; deep convolutional residual neural network; protein folding
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Year: 2021 PMID: 34473376 DOI: 10.1002/prot.26232
Source DB: PubMed Journal: Proteins ISSN: 0887-3585