Critical assessment of protein intrinsic disorder prediction
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Author(s)
Necci, M
Piovesan, D
Hoque, MT
Walsh, I
Iqbal, S
Vendruscolo, M
Sormanni, P
Wang, C
Raimondi, D
Sharma, R
Zhou, Y
Litfin, T
Hanson, J
Paliwal, K
et al.
Year published
2021
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Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning ...
View more >Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F = 0.483 on the full dataset and F = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude. max max max
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View more >Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F = 0.483 on the full dataset and F = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude. max max max
View less >
Journal Title
Nature Methods
Volume
18
Issue
5
Copyright Statement
© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Subject
Biological sciences
Biomedical and clinical sciences