Critical assessment of protein intrinsic disorder prediction
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Necci, Marco | Piovesan, Damiano | Tosatto, Silvio, C E | 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, Björn | Mirabello, Claudio | Malhis, Nawar | Dosztányi, Zsuzsanna | Erdős, Gábor | Mészáros, Bálint | Gao, Jianzhao | Wang, Kui | Hu, Gang | Wu, Zhonghua | Sharma, Alok | Hanson, Jack | Paliwal, Kuldip | Callebaut, Isabelle | Bitard-Feildel, Tristan | Orlando, Gabriele | Peng, Zhenling | Xu, Jinbo | Wang, Sheng | Jones, David T. | Cozzetto, Domenico | Meng, Fanchi | Yan, Jing | Gsponer, Jörg | Cheng, Jianlin | Wu, Tianqi | Kurgan, Lukasz | Promponas, Vasilis J. | Tamana, Stella | Marino-Buslje, Cristina | Martínez-Pérez, Elizabeth | Chasapi, Anastasia | Ouzounis, Christos | Dunker, A. Keith | Kajava, Andrey V. | Leclercq, Jeremy Y. | Aykac-Fas, Burcu | Lambrughi, Matteo | Maiani, Emiliano | Papaleo, Elena | Chemes, Lucia Beatriz | Álvarez, Lucía | González-Foutel, Nicolás S. | Iglesias, Valentin | Pujols, Jordi | Ventura, Salvador | Palopoli, Nicolás | Benítez, Guillermo Ignacio | Parisi, Gustavo | Bassot, Claudio | Elofsson, Arne | Govindarajan, Sudha | Lamb, John | Salvatore, Marco | Hatos, András | Monzon, Alexander Miguel | Bevilacqua, Martina | Mičetić, Ivan | Minervini, Giovanni | Paladin, Lisanna | Quaglia, Federica | Leonardi, Emanuela | Davey, Norman | Horvath, Tamas | Kovacs, Orsolya Panna | Murvai, Nikoletta | Pancsa, Rita | Schad, Eva | Szabo, Beata | Tantos, Agnes | Macedo-Ribeiro, Sandra | Manso, Jose Antonio | Pereira, Pedro José Barbosa | Davidović, Radoslav | Veljkovic, Nevena | Hajdu-Soltész, Borbála | Pajkos, Mátyás | Szaniszló, Tamás | Guharoy, Mainak | Lazar, Tamas | Macossay-Castillo, Mauricio | Tompa, Peter
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CCSD ; Nature Publishing Group -
International audience.
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 max = 0.483 on the full dataset and F max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.
