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• W2991759014 abstract "Abstract Motivation Threading is one of the most effective methods for protein structure prediction. In recent years, the increasing accuracy in protein contact map prediction opens a new avenue to improve the performance of threading algorithms. Several preliminary studies suggest that with predicted contacts, the performance of threading algorithms can be improved greatly. There is still much room to explore to make better use of predicted contacts. Results We have developed a new contact-assisted threading algorithm named CATHER using both conventional sequential profiles and contact map predicted by a deep learning-based algorithm. Benchmark tests on an independent test set and the CASP12 targets demonstrated that CATHER made significant improvement over other methods which only use either sequential profile or predicted contact map. Our method was ranked at the Top 10 among all 39 participated server groups on the 32 free modeling targets in the blind tests of the CASP13 experiment. These data suggest that it is promising to push forward the threading algorithms by using predicted contacts. Availability and implementation http://yanglab.nankai.edu.cn/CATHER/. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W2991759014 created "2019-12-13" @default.
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• W2991759014 date "2019-12-02" @default.
• W2991759014 modified "2023-10-16" @default.
• W2991759014 title "CATHER: a novel threading algorithm with predicted contacts" @default.
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• W2991759014 doi "https://doi.org/10.1093/bioinformatics/btz876" @default.
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