SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

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Hanson, Jack
Paliwal, Kuldip K
Litfin, Thomas
Zhou, Yaoqi
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2019
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https://creativecommons.org/licenses/by/4.0/
Abstract

Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.

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Genomics, Proteomics and Bioinformatics
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17
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6
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© 2019 The Authors. Published by Elsevier B.V. and Science Press on behalf of Beijing Institute of Genomics, Chinese Academy of Sciences and Genetics Society of China. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Subject
Mathematical sciences
Biological sciences
Information and computing sciences
Deep learning
Intrinsic disorder
Machine learning
Molecular recognition feature
Protein structure
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Hanson, J; Paliwal, KK; Litfin, T; Zhou, Y, SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning, Genomics, Proteomics and Bioinformatics, 2019, 17 (6), pp. 645-656
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