Browsing by Author "Necci, Marco"

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    Critical assessment of protein intrinsic disorder prediction
    (Springer Nature, 2021) Necci, Marco; Piovesan, Damiano; CAID Predictors; DisProt Curators; Tosatto, Silvio C. E.; Biochemistry and Molecular Biology, School of Medicine
    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.
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    DisProt 7.0: a major update of the database of disordered proteins
    (Oxford University Press, 2017-01-04) Piovesan, Damiano; Tabaro, Francesco; Micetic, Ivan; Necci, Marco; Quaglia, Federica; Oldfield, Christopher J.; Aspromonte, Maria Cristina; Davey, Norman E.; Davidovic, Radoslav; Dosztanyi, Zsuzsanna; Elofsson, Arne; Gasparini, Alessandra; Hatos, Andras; Kajava, Andrey V.; Kalmar, Lajos; Leonardi, Emanuela; Lazar, Tamas; Macedo-Ribeiro, Sandra; Macossay-Castillo, Mauricio; Meszaros, Attila; Minervini, Giovanni; Murvai, Nikoletta; Pujols, Jordi; Roche, Daniel B.; Salladini, Edoardo; Schad, Eva; Schramm, Antoine; Szabo, Beata; Tantos, Agnes; Tonello, Fiorella; Tsirigos, Konstantinos D.; Veljkovic, Nevena; Ventura, Salvador; Vranken, Wim; Warholm, Per; Uversky, Vladimir N.; Dunker, A. Keith; Longhi, Sonia; Tompa, Peter; Tosatto, Silvio C.E.; Department of Biochemistry and Molecular Biology, IU School of Medicine
    The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.
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    DisProt: intrinsic protein disorder annotation in 2020
    (Oxford University Press, 2020-01-08) Hatos, András; Hajdu-Soltész, Borbála; Monzon, Alexander M.; Palopoli, Nicolas; Álvarez, Lucía; Aykac-Fas, Burcu; Bassot, Claudio; Benítez, Guillermo I.; Bevilacqua, Martina; Chasapi, Anastasia; Chemes, Lucia; Davey, Norman E.; Davidović, Radoslav; Dunker, A. Keith; Elofsson, Arne; Gobeill, Julien; González Foutel, Nicolás S.; Sudha, Govindarajan; Guharoy, Mainak; Horvath, Tamas; Iglesias, Valentin; Kajava, Andrey V.; Kovacs, Orsolya P.; Lamb, John; Lambrughi, Matteo; Lazar, Tamas; Leclercq, Jeremy Y.; Leonardi, Emanuela; Macedo-Ribeiro, Sandra; Macossay-Castillo, Mauricio; Maiani, Emiliano; Manso, José A.; Marino-Buslje, Cristina; Martínez-Pérez, Elizabeth; Mészáros, Bálint; Mičetić, Ivan; Minervini, Giovanni; Murvai, Nikoletta; Necci, Marco; Ouzounis, Christos A.; Pajkos, Mátyás; Paladin, Lisanna; Pancsa, Rita; Papaleo, Elena; Parisi, Gustavo; Pasche, Emilie; Barbosa Pereira, Pedro J.; Promponas, Vasilis J.; Pujols, Jordi; Quaglia, Federica; Ruch, Patrick; Salvatore, Marco; Schad, Eva; Szabo, Beata; Szaniszló, Tamás; Tamana, Stella; Tantos, Agnes; Veljkovic, Nevena; Ventura, Salvador; Vranken, Wim; Dosztányi, Zsuzsanna; Tompa, Peter; Tosatto, Silvio C. E.; Piovesan, Damiano; Medicine, School of Medicine
    The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome.