{"entity": "researcher", "timestamp": "2026-08-20T21:05:04.066Z", "family": "Necci", "given": "Marco", "initials": "M", "orcid": "0000-0001-9377-482X", "affiliations": ["Department of Biomedical Sciences, University of Padua, Padua, Italy."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/cc0ce89796824d1e987e9e9b5196d07b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/cc0ce89796824d1e987e9e9b5196d07b"}}, "publications": [{"entity": "publication", "iuid": "1f3a14b3f8db4223978947b162817254", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1f3a14b3f8db4223978947b162817254.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1f3a14b3f8db4223978947b162817254"}}, "title": "Critical assessment of protein intrinsic disorder prediction.", "authors": [{"family": "Necci", "given": "Marco", "initials": "M", "orcid": "0000-0001-9377-482X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cc0ce89796824d1e987e9e9b5196d07b.json"}}, {"family": "Piovesan", "given": "Damiano", "initials": "D", "orcid": "0000-0001-8210-2390", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/994db11834b0446895c5cd4b28be1d8b.json"}}, {"family": "CAID Predictors", "given": "", "initials": ""}, {"family": "DisProt Curators", "given": "", "initials": ""}, {"family": "Tosatto", "given": "Silvio C E", "initials": "SCE", "orcid": "0000-0003-4525-7793", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/152b8eca864e44f1954ee4952c60aa0d.json"}}], "type": "journal article", "published": "2021-05-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "18", "issue": "5", "pages": "472-481", "issn-l": "1548-7091"}, "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 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.", "doi": "10.1038/s41592-021-01117-3", "pmid": "33875885", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8105172"}, {"db": "pii", "key": "10.1038/s41592-021-01117-3"}], "notes": [], "created": "2026-08-20T09:02:38.822Z", "modified": "2026-08-20T09:02:38.937Z"}]}