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Critical assessment of protein intrinsic disorder prediction

2021; Nature Portfolio; Volume: 18; Issue: 5 Linguagem: Inglês

10.1038/s41592-021-01117-3

1548-7105

Marco Necci, Damiano Piovesan, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana V. Galzitskaya, Michail Yu. Lobanov, Wim Vranken, Björn Wallner, Claudio Mirabello, Nawar Malhis, Zsuzsanna Dosztányi, Gábor Erdős, Bálint Mészáros, Jianzhao Gao, Kui Wang, Gang Hu, Zhonghua Wu, Alok Sharma, Jack Hanson, K.K. Paliwal, Isabelle Callebaut, Tristan Bitard-Feildel, Gabriele Orlando, Zhenling Peng, Jinbo Xu, Sheng Wang, David T. Jones, Domenico Cozzetto, Fanchi Meng, Jing Yan, Jörg Gsponer, Jianlin Cheng, Tianqi Wu, Lukasz Kurgan, Vasilis J. Promponas, Stella Tamana, Cristina Marino‐Buslje, Elizabeth Martínez‐Pérez, Anastasia Chasapi, Christos Ouzounis, A. Keith Dunker, Andrey V. Kajava, Jérémy Leclercq, Burcu Aykaç Fas, Matteo Lambrughi, Emiliano Maiani, Elena Papaleo, Lucía B. Chemes, Lucía Álvarez, Nicolás S. González Foutel, Valentín Iglesias, Jordi Pujols, Salvador Ventura, Nicolás Palópoli, Guillermo Ignacio Benítez, Gustavo Parisi, Claudio Bassot, Arne Elofsson, Sudha Govindarajan, John Lamb, Marco Salvatore, András Hatos, Alexander Miguel Monzón, Martina Bevilacqua, Ivan Mičetić, Giovanni Minervini, Lisanna Paladin, Federica Quaglia, Emanuela Leonardi, Norman E. Davey, Tamás Horváth, Orsolya Panna Kovacs, Nikoletta Murvai, Rita Pancsa, Éva Schád, Beáta Szabó, Ágnes Tantos, Sandra Macedo‐Ribeiro, José A. Manso, Pedro José Barbosa Pereira, Radoslav Davidović, Nevena Veljković, Borbála Hajdu-Soltész, Mátyás Pajkos, Tamás Szaniszló, Mainak Guharoy, Tamás Lázár, Mauricio Macossay-Castillo, Péter Tompa, Silvio C. E. Tosatto,

RNA and protein synthesis mechanisms

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 Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.