Critical assessment of protein intrinsic disorder prediction

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 D...

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Autores: Necci, Marco|||0000-0001-9377-482X, Piovesan, Damiano|||0000-0001-8210-2390, Hoque, M. T., Walsh, Ian, Iqbal, Sumaiya, Vendruscolo, Michele, Sormanni, Pietro, Wang, Chen, Raimondi, Daniele, Sharma, Ronesh, Zhou, Yaoqi, Litfin, Thomas, Galzitskaya, Oxxana Valerianovna, Lobanov, Michail Yu, Vranken, Wim|||0000-0001-7470-4324, Wallner, Björn, Mirabello, Claudio, Malhis, Nawar, Dosztányi, Zsuzsanna|||0000-0002-3624-5937, Erdős, Gábor|||0000-0001-6218-5192, Mészáros, Bálint|||0000-0003-0919-4449, Gao, Jianzhao|||0000-0002-9943-4786, Wang, Kui, Hu, Gang, Wu, Zhonghua, Sharma, Alok, Hanson, Jack, Callebaut, Isabelle, Bitard-Feildel, Tristan, Orlando, Gabriele, Peng, Zhenling, Xu, Jinbo, Wang, Sheng|||0000-0003-2611-3559, Jones, David T., Cozzetto, Domenico, Meng, Fanchi, Yan, Jing, Gsponer, Jörg, Cheng, Jianlin, Wu, Tianqi, Kurgan, Lukasz, Promponas, Vasilis J.|||0000-0003-3352-4831, Tamana, Stella, Marino-Buslje, Cristina|||0000-0002-6564-1920, Martínez-Pérez, Elisabeth, Chasapi, Anastasia|||0000-0003-1986-5007, Ouzounis, Christos|||0000-0002-0086-8657, Dunker, A. Keith, Kajava, Andrey V., Leclercq, Jeremy Y., Aykac-Fas, Burcu|||0000-0003-3842-731X, Lambrughi, Matteo|||0000-0002-0894-8627, Maiani, Emiliano|||0000-0003-1432-5394, Papaleo, Elena|||0000-0002-7376-5894, Chemes, Lucía Beatriz|||0000-0003-0192-9906, Álvarez, Lucía, González-Foutel, Nicolás S., Iglesias, Valentin|||0000-0002-6133-0869, Pujols Pujol, Jordi|||0000-0001-9424-5866, Ventura, Salvador|||0000-0002-9652-6351, Palopoli, Nicolas, Benítez, Guillermo I., Parisi, Gustavo|||0000-0001-7444-1624, Bassot, Claudio|||0000-0001-7161-9028, Elofsson, Arne|||0000-0002-7115-9751, Govindarajan, Sudha, Lamb, John, Salvatore, Marco, Hatos, András|||0000-0001-9224-9820, Monzon, Alexander Miguel|||0000-0003-0362-8218, Bevilacqua, Martina|||0000-0001-7619-871X, Mičetić, Ivan|||0000-0003-1691-8425, Minervini, Giovanni|||0000-0001-7013-5785, Paladin, Lisanna, Quaglia, Federica|||0000-0002-0341-4888, Leonardi, Emanuela|||0000-0001-8486-8461, Davey, Norman E.|||0000-0001-6988-4850, Horvath, Tamas, Kovacs, Orsolya Panna, Murvai, Nikoletta, Pancsa, Rita|||0000-0003-0849-9312, Schad, Eva|||0000-0002-3006-2910, Szabo, Beata, Tantos, Agnes, Macedo-Ribeiro, Sandra|||0000-0002-7698-1170, Manso, Jose Antonio, Barbosa Pereira, Pedro José, Davidović, Radoslav|||0000-0002-6097-6203, Veljkovic, Nevena|||0000-0001-6562-5800, Hajdu-Soltész, Borbála, Pajkos, Mátyás|||0000-0001-5791-9825, Szaniszló, Tamás|||0000-0002-3130-9284, Guharoy, Mainak, Lazar, Tamas|||0000-0001-7496-6711, Macossay-Castillo, Mauricio, Tompa, Peter|||0000-0001-8042-9939, Tosatto, Silvio|||0000-0003-4525-7793
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:249476
Acceso en línea:https://ddd.uab.cat/record/249476
https://dx.doi.org/urn:doi:10.1038/s41592-021-01117-3
Access Level:acceso abierto
Palabra clave:Amino Acid Sequence
Computational Biology
Databases, Protein
Intrinsically Disordered Proteins
Protein Binding
Protein Conformation
Protein Folding
Software
Descripción
Sumario: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.

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