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Cited by 343Protein Structure and DynamicsMachine Learning in BioinformaticsComputational Drug Discovery Methods

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Critical assessment of protein intrinsic disorder prediction

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Necci, Marco, Piovesan, Damiano, CAID Predictors, 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, DisProt Curators, 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.·2021·Nature Methods
DOI 10.1038/s41592-021-01117-3PMC PMC8105172

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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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Fig. 1 CAID dashboard. a, CAID timeline: phases of CAID from June 2018 to the present. The initial results were presented and discussed at the conferences Intelligent Systems for Molecular Biology (ISMB) and CASP. b, CAID process: iterative process of the CAID experiment in four phases. (1) Annotation: any process that produces unpublished annotation of IDR coordinates; in this edition, annotation refers to the DisProt round of annotation. (2) Prediction: annotations are used to build references with which we test predictors. (3) Evaluation: predictions are evaluated. (4) Report: a report of the evaluation is produced and published in peer-reviewed journals and on a web page that allows the reader to browse the evaluation of all CAID editions. c, Residue classification strategy for the DisProt and DisProt-PDB references. d, Number of residues for each class in different references. e, Number of proteins for each set of annotations that they contain. f, Number of proteins in each taxon.
Fig. 1 CAID dashboard. a, CAID timeline: phases of CAID from June 2018 to the present. The initial results were presented and discussed at the conferences Intelligent Systems for Molecular Biology (ISMB) and CASP. b, CAID process: iterative process of the CAID experiment in four phases. (1) Annotation: any process that produces unpublished annotation of IDR coordinates; in this edition, annotation refers to the DisProt round of annotation. (2) Prediction: annotations are used to build references with which we test predictors. (3) Evaluation: predictions are evaluated. (4) Report: a report of the evaluation is produced and published in peer-reviewed journals and on a web page that allows the reader to browse the evaluation of all CAID editions. c, Residue classification strategy for the DisProt and DisProt-PDB references. d, Number of residues for each class in different references. e, Number of proteins for each set of annotations that they contain. f, Number of proteins in each taxon.CC BY · PubMed Central
Fig. 2 Prediction success and CPU times for the ten top-ranking disorder predictors in the DisProt dataset. a, The reference used (DisProt, n = 646 proteins) in the analysis and how it was obtained. b–g, Performance of predictors expressed as maximum F1-score across all thresholds (Fmax) (b) and AUC (e) for the ten top-ranking methods (light gray) and baselines (white), and distribution of execution time per target (c,f) using the DisProt dataset. b,e, The horizontal line indicates, respectively, Fmax and AUC of the best baseline. d,g, Precision–recall (d) and ROC curves (g) of the ten top-ranking methods and baselines using the DisProt dataset, with level curves of F1-score and balanced accuracy, respectively. F, Fmax; C, coverage; A, AUC. c,f, Boxplots are defined as follows: the middle value of the dataset is the median (Q2/50th percentile) and box boundaries are the first quartile (Q1/25th percentile) and third quartile (Q3/75th percentile), respectively; maximum is Q3 + 1.5 × (Q3 – Q1) and minimum is Q1 – 1.5 × (Q3 – Q1). Outliers are hidden for clarity. c,f, Magenta dots indicate that the entire distribution of execution times is <1 s. Q1–Q3, first to third quartiles. TPR, true positive rate; FPR, false positive rate.
Fig. 2 Prediction success and CPU times for the ten top-ranking disorder predictors in the DisProt dataset. a, The reference used (DisProt, n = 646 proteins) in the analysis and how it was obtained. b–g, Performance of predictors expressed as maximum F1-score across all thresholds (Fmax) (b) and AUC (e) for the ten top-ranking methods (light gray) and baselines (white), and distribution of execution time per target (c,f) using the DisProt dataset. b,e, The horizontal line indicates, respectively, Fmax and AUC of the best baseline. d,g, Precision–recall (d) and ROC curves (g) of the ten top-ranking methods and baselines using the DisProt dataset, with level curves of F1-score and balanced accuracy, respectively. F, Fmax; C, coverage; A, AUC. c,f, Boxplots are defined as follows: the middle value of the dataset is the median (Q2/50th percentile) and box boundaries are the first quartile (Q1/25th percentile) and third quartile (Q3/75th percentile), respectively; maximum is Q3 + 1.5 × (Q3 – Q1) and minimum is Q1 – 1.5 × (Q3 – Q1). Outliers are hidden for clarity. c,f, Magenta dots indicate that the entire distribution of execution times is <1 s. Q1–Q3, first to third quartiles. TPR, true positive rate; FPR, false positive rate.CC BY · PubMed Central
Fig. 3 Prediction success and CPU times for the ten top-ranking disorder predictors in the DisProt-PDB dataset. a, The reference used (DisProt-PDB, n = 646 proteins) in the analysis and how it was obtained. b–g, Performance of predictors expressed as maximum F1-score across all thresholds (Fmax) (b) and AUC (e) for the ten top-ranking methods (light gray) and baselines (white), and distribution of execution time per target (c,f) using the DisProt-PDB dataset. b,e, The horizontal line indicates, respectively, Fmax and AUC of the best baseline. d,g, Precision–recall (d) and ROC curves (g) of the ten top-ranking methods and baselines using the DisProt-PDB dataset, with level curves of F1-score and balanced accuracy, respectively. c,f, boxplots are defined as follows: the middle value of the dataset is the median (Q2/50th percentile) and box boundaries are the first quartile (Q1/25th percentile) and third quartile (Q3/75th percentile), respectively; maximum is Q3 + 1.5 × (Q3 – Q1) and minimum is Q1 – 1.5 × (Q3 – Q1). Outliers are hidden for clarity. c,f, Magenta dots indicate that the entire distribution of execution times is <1 s.
Fig. 3 Prediction success and CPU times for the ten top-ranking disorder predictors in the DisProt-PDB dataset. a, The reference used (DisProt-PDB, n = 646 proteins) in the analysis and how it was obtained. b–g, Performance of predictors expressed as maximum F1-score across all thresholds (Fmax) (b) and AUC (e) for the ten top-ranking methods (light gray) and baselines (white), and distribution of execution time per target (c,f) using the DisProt-PDB dataset. b,e, The horizontal line indicates, respectively, Fmax and AUC of the best baseline. d,g, Precision–recall (d) and ROC curves (g) of the ten top-ranking methods and baselines using the DisProt-PDB dataset, with level curves of F1-score and balanced accuracy, respectively. c,f, boxplots are defined as follows: the middle value of the dataset is the median (Q2/50th percentile) and box boundaries are the first quartile (Q1/25th percentile) and third quartile (Q3/75th percentile), respectively; maximum is Q3 + 1.5 × (Q3 – Q1) and minimum is Q1 – 1.5 × (Q3 – Q1). Outliers are hidden for clarity. c,f, Magenta dots indicate that the entire distribution of execution times is <1 s.CC BY · PubMed Central
Fig. 4 Prediction success and CPU times for the ten top-ranking binding predictors in the DisProt-binding dataset. a, The reference used (DisProt-binding, n = 646 proteins) in the analysis and how it was obtained. b–g, Performance of predictors expressed as maximum F1-score across all thresholds (Fmax) (b) and AUC (e) for the ten top-ranking methods (light gray) and baselines (white), and distribution of execution time per target (c,f) using the DisProt-binding dataset. b,e, The horizontal line indicates, respectively, Fmax and AUC of the best baseline. d,g, Precision–recall (d) and ROC curves (g) of the ten top-ranking methods and baselines using the DisProt-binding dataset, with level curves of F1-score and balanced accuracy, respectively. c,f, boxplots are defined as follows: the middle value of the dataset is the median (Q2/50th percentile) and box boundaries are the first quartile (Q1/25th percentile) and third quartile (Q3/75th percentile), respectively; maximum is Q3 + 1.5 × (Q3 – Q1) and minimum is Q1 – 1.5 × (Q3 – Q1). Outliers are hidden for clarity. c,f, Magenta dots indicate that the entire distribution of execution times is <1 s.
Fig. 4 Prediction success and CPU times for the ten top-ranking binding predictors in the DisProt-binding dataset. a, The reference used (DisProt-binding, n = 646 proteins) in the analysis and how it was obtained. b–g, Performance of predictors expressed as maximum F1-score across all thresholds (Fmax) (b) and AUC (e) for the ten top-ranking methods (light gray) and baselines (white), and distribution of execution time per target (c,f) using the DisProt-binding dataset. b,e, The horizontal line indicates, respectively, Fmax and AUC of the best baseline. d,g, Precision–recall (d) and ROC curves (g) of the ten top-ranking methods and baselines using the DisProt-binding dataset, with level curves of F1-score and balanced accuracy, respectively. c,f, boxplots are defined as follows: the middle value of the dataset is the median (Q2/50th percentile) and box boundaries are the first quartile (Q1/25th percentile) and third quartile (Q3/75th percentile), respectively; maximum is Q3 + 1.5 × (Q3 – Q1) and minimum is Q1 – 1.5 × (Q3 – Q1). Outliers are hidden for clarity. c,f, Magenta dots indicate that the entire distribution of execution times is <1 s.CC BY · PubMed Central

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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
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2021-04-19
Volume
18
Issue
5
Pages
472-481
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-021-01117-3
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
Licence
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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