Critical assessment of protein intrinsic disorder prediction
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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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pLDDT scores have been shown to be a competitive disorder predictor compared to standards in disorder prediction.
“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]
Conservative estimates indicate that roughly 30% of sequences, 30 residues or longer, drawn at random from the human proteome, are likely to be intrinsically disordered regions.
“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]
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A Perspective on the Prospective Use of AI in Protein Structure Prediction
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Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions
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DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction
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Assembly of JAZ–JAZ and JAZ–NINJA complexes in jasmonate signaling
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Protein Function Analysis through Machine Learning
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Evolutionary analyses of intrinsically disordered regions reveal widespread signals of conservation
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Intrinsically Disordered Proteins: Perspective on COVID-19 Infection and Drug Discovery
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flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins
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Metapredict Enables Accurate Disorder Prediction Across the Tree of LifePreprint
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AIUPred – Binding: Energy Embedding to Identify Disordered Binding Regions
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- 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 · 2020: 3
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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