Browsing by Author "Dosztányi, Zsuzsanna"

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    D2P2: database of disordered protein predictions
    (Oxford University Press, 2013) Oates, Matt E.; Romero, Pedro; Ishida, Takashi; Ghalwash, Mohamed; Mizianty, Marcin J.; Xue, Bin; Dosztányi, Zsuzsanna; Uversky, Vladimir N.; Obradovic, Zoran; Kurgan, Lukasz; Dunker, A. Keith; Gough, Julian; Center for Computational Biology and Bioinformatics, School of Medicine
    We present the Database of Disordered Protein Prediction (D(2)P(2)), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D(2)P(2) will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.
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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.