학술논문
Critical assessment of protein intrinsic disorder prediction
Document Type
Report
Author
Necci, Marco; Piovesan, Damiano; Hoque, Md Tamjidul; Walsh, Ian; Iqbal, Sumaiya; Vendruscolo, Michele; Sormanni, Pietro; Wang, Chen; Raimondi, Daniele; Sharma, Ronesh; Zhou, Yaoqi; Litfin, Thomas; Galzitskaya, Oxana Valerianovna; Lobanov, Michail Yu.; Vranken, Wim; Wallner, Bjorn; Mirabello, Claudio
Source
Nature Methods. May 2021, Vol. 18 Issue 5, p472, 10 p.
Subject
Language
English
ISSN
1548-7091
Abstract
Author(s): Marco Necci [sup.1] , Damiano Piovesan [sup.1] , Md Tamjidul Hoque [sup.2] , Ian Walsh [sup.3] , Sumaiya Iqbal [sup.4] , Michele Vendruscolo [sup.5] , Pietro Sormanni [sup.5] , [...]
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.sub.max = 0.483 on the full dataset and F.sub.max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F.sub.max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude. Results are presented from the first Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment, a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins.
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.sub.max = 0.483 on the full dataset and F.sub.max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F.sub.max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude. Results are presented from the first Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment, a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins.