Critical assessment of protein intrinsic disorder prediction
| Autor: | 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, 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, Tosatto Silvio C., E., Caid, Predictors, DisProt, Curators |
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| Přispěvatelé: | Università degli Studi di Padova = University of Padua (Unipd), Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC), Muséum national d'Histoire naturelle (MNHN)-Institut de recherche pour le développement [IRD] : UR206-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Necci, Marco [0000-0001-9377-482X], Piovesan, Damiano [0000-0001-8210-2390], Tosatto, Silvio C. E. [0000-0003-4525-7793], Apollo - University of Cambridge Repository, Informatics and Applied Informatics, Chemistry, Basic (bio-) Medical Sciences, Department of Bio-engineering Sciences, Faculty of Sciences and Bioengineering Sciences, Structural Biology Brussels, Tosatto, Silvio CE [0000-0003-4525-7793], ANR-17-CE12-0016,FUNBRCA2,Caractérisation d'un nouveau site de liaison à l'ADN dans la protéine BRCA2(2017), Universita degli Studi di Padova, CAID Predictors, DisProt Curators |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Protein Folding
Protein Conformation Computer science 631/45/612 analysis [SDV]Life Sciences [q-bio] purl.org/becyt/ford/1.7 [https] MESH: Amino Acid Sequence Biochemistry purl.org/becyt/ford/1 [https] Protein structure MESH: Protein Conformation 631/114/2398 Databases Protein Biological sciences ComputingMilieux_MISCELLANEOUS MESH: Intrinsically Disordered Proteins 0303 health sciences 030302 biochemistry & molecular biology disorder Critical assessment Protein folding Protein Binding Biotechnology MESH: Computational Biology MESH: Databases Protein disorder prediction MESH: Protein Folding Computational biology Intrinsically disordered proteins Orders of magnitude (entropy) 03 medical and health sciences MESH: Software Computational platforms and environments 631/114/2411 Machine learning Molecule MESH: Protein Binding [INFO]Computer Science [cs] Amino Acid Sequence Molecular Biology 030304 developmental biology business.industry Deep learning Computational Biology Proteins Cell Biology 631/114/1305 Intrinsically Disordered Proteins CAID 631/114/794 Protein structure predictions Artificial intelligence business Software |
| Zdroj: | Nature Methods Nature Methods, 2021, 18 (5), pp.472-481. ⟨10.1038/s41592-021-01117-3⟩ Nature Methods, Nature Publishing Group, 2021, ⟨10.1038/s41592-021-01117-3⟩ CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET Nature Methods, 2021, ⟨10.1038/s41592-021-01117-3⟩ Dipòsit Digital de Documents de la UAB Universitat Autònoma de Barcelona Nature Methods, Nature Publishing Group, 2021, 18 (5), pp.472-481. ⟨10.1038/s41592-021-01117-3⟩ |
| ISSN: | 1548-7091 1548-7105 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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