The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.
Critical assessment of protein intrinsic disorder prediction
Open accessPublished version of a preprint
Abstract
Abstract 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.
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“Conservative estimates indicate that roughly 30% of sequences, 30residues or longer, drawn at random from the human proteome, are likely to be IDRs. 3,5”
From AlphaFold and Implications for Intrinsically Disordered Proteins (2021) · cites this paper as [5]
Experimental — accuracy not yet measuredIn the citing paper
“pLDDT scores have been shown to be a competitive disorder predictor compared to standards in disorder prediction.5”
From AlphaFold and Implications for Intrinsically Disordered Proteins (2021) · cites this paper as [5]
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Metadata
- Authors
- Necci, M., Piovesan, D., Predictors, C., Hoque, M. T., Walsh, I., Iqbal, S., Vendruscolo, M., Sormanni, P., Wang, C., Raimondi, D., Sharma, R., Zhou, Y., Litfin, T., Galzitskaya, O. V., Lobanov, M. Y., Vranken, W., Wallner, B., Mirabello, C., Malhis, N., Dosztányi, Z., Erdős, G., Mészáros, B., Gao, J., Wang, K., Hu, G., Wu, Z., Sharma, A., Hanson, J., Paliwal, K., Callebaut, I., Bitard-Feildel, T., Orlando, G., Peng, Z., Xu, J., Wang, S., Jones, D. T., Cozzetto, D., Meng, F., Yan, J., Gsponer, J., Cheng, J., Wu, T., Kurgan, L., Curators, D., Promponas, V. J., Tamana, S., Marino-Buslje, C., Martínez-Pérez, E., Chasapi, A., Ouzounis, C., Dunker, A. K., Kajava, A. V., Leclercq, J. Y., Aykac-Fas, B., Lambrughi, M., Maiani, E., Papaleo, E., Chemes, L. B., Álvarez, L., González-Foutel, N. S., Iglesias, V., Pujols, J., Ventura, S., Palopoli, N., Benítez, G. I., Parisi, G., Bassot, C., Elofsson, A., Govindarajan, S., Lamb, J., Salvatore, M., Hatos, A., Monzon, A. M., Bevilacqua, M., Mičetić, I., Minervini, G., Paladin, L., Quaglia, F., Leonardi, E., Davey, N., Horvath, T., Kovacs, O. P., Murvai, N., Pancsa, R., Schad, E., Szabo, B., Tantos, A., Macedo-Ribeiro, S., Manso, J. A., Pereira, P. J. B., Davidović, R., Veljkovic, N., Hajdu-Soltész, B., Pajkos, M., Szaniszló, T., Guharoy, M., Lazar, T., Macossay-Castillo, M., Tompa, P., & Tosatto, S. C. E.
- Affiliations
- University of Padua (IT)
- LSU New Orleans (US)
- Agency for Science, Technology and Research (SG)
- Broad Institute (US)
- University of Cambridge (GB)
- Columbia University (US)
- Fiji National University (FJ)
- Griffith University (AU)
- Russian Academy of Sciences (RU)
- Linköping University (SE)
- Eötvös Loránd University (HU)
- Nankai University (CN)
- Sorbonne Université (FR)
- Tianjin University (CN)
- Toyota Technological Institute at Chicago (US)
- University College London (GB)
- University of Alberta (CA)
- University of British Columbia (CA)
- University of Missouri (US)
- Virginia Commonwealth University (US)
- University of Cyprus (CY)
- Fundación Instituto Leloir (AR)
- Centre for Research and Technology Hellas (GR)
- Université de Montpellier (FR)
- Danish Cancer Society (DK)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Universitat Autònoma de Barcelona (ES)
- National University of Quilmes (AR)
- Stockholm University (SE)
- University of Copenhagen (DK)
- Institute of Cancer Research (GB)
- Institute of Molecular Life Sciences (HU)
- Universidade do Porto (PT)
- Vrije Universiteit Brussel (BE)
- Corresponding author
- Tosatto, Silvio C. E.
- ORCID
- 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
- Vranken, Wim
- Wallner, Björn
- Mirabello, Claudio
- Malhis, Nawar
- Dosztányi, Zsuzsanna
- Mészáros, Bálint
- Gao, Jianzhao
- Wang, Kui
- Hu, Gang
- Wu, Zhonghua
- Sharma, Alok
- Hanson, Jack
- Paliwal, Kuldip
- Callebaut, Isabelle
- Orlando, Gabriele
- Peng, Zhenling
- Xu, Jinbo
- Wang, Sheng
- Jones, David T.
- Cozzetto, Domenico
- Yan, Jing
- Gsponer, Jörg
- Cheng, Jianlin
- Wu, Tianqi
- Kurgan, Lukasz
- Promponas, Vasilis J.
- Tamana, Stella
- Chasapi, Anastasia
- Ouzounis, Christos
- Dunker, A. Keith
- Kajava, Andrey V.
- Leclercq, Jeremy Y.
- Lambrughi, Matteo
- Maiani, Emiliano
- Papaleo, Elena
- Chemes, Lucia Beatriz
- Pujols, Jordi
- Ventura, Salvador
- Parisi, Gustavo
- Bassot, Claudio
- Elofsson, Arne
- Lamb, John
- Salvatore, Marco
- Hatos, András
- Bevilacqua, Martina
- Mičetić, Ivan
- Minervini, Giovanni
- Paladin, Lisanna
- Quaglia, Federica
- Leonardi, Emanuela
- Davey, Norman
- Murvai, Nikoletta
- Pancsa, Rita
- Pereira, Pedro José Barbosa
- Davidović, Radoslav
- Pajkos, Mátyás
- Szaniszló, Tamás
- Guharoy, Mainak
- Tosatto, Silvio C. E.
- Venue
- Nature Methods
- Published
- 2021-04-19
- Volume
- 18
- Issue
- 5
- Pages
- 472-481
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC8105172
- OpenAlex
- W3156051371
- Cited by
- 343
- References
- 58
- Citations by year
- 2026: 35 · 2025: 64 · 2024: 68 · 2023: 72 · 2022: 69 · 2021: 32
- FWCI
- 18.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion, cc-by)
- CONICET Digital (CONICET) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- ELTE Digital Institutional Repository (EDIT) (Eötvös Loránd University) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- VinaR (Institute of Nuclear Sciences "Vinča") (submittedVersion, cc-by)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- EC | Horizon 2020 Framework Programme (778247)
- Ministero dell’Istruzione, dell’Università e della Ricerca (2017483NH8)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- intrinsically disordered proteins, CAID, blind test, deep learning, physicochemical methods, DisProt, prediction accuracy, benchmarking
- MeSH terms
- Amino Acid Sequence, Protein Binding, Protein Conformation, Software, Protein Folding, Computational Biology, Databases, Protein, Intrinsically Disordered Proteins
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