flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions

Gang Hu, Akila Katuwawala, Kui Wang, Zhonghua Wu, Sina Ghadermarzi, Jianzhao Gao and Lukasz Kurgan ()
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Gang Hu: LPMC and KLMDASR, Nankai University
Akila Katuwawala: Virginia Commonwealth University
Kui Wang: Nankai University
Zhonghua Wu: Nankai University
Sina Ghadermarzi: Virginia Commonwealth University
Jianzhao Gao: Nankai University
Lukasz Kurgan: Virginia Commonwealth University

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn’s webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/

Date: 2021
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DOI: 10.1038/s41467-021-24773-7

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