MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavors in proteins

Abstract Motivation The earlier version of MobiDB-lite is currently used in large-scale proteome annotation platforms to detect intrinsic disorder. However, new theoretical models allow for the classification of intrinsically disordered regions into subtypes from sequence features associated with specific polymeric properties or compositional bias. Results MobiDB-lite 3.0 maintains its previous speed and performance but also provides a finer classification of disorder by identifying regions with characteristics of polyolyampholytes, positive or negative polyelectrolytes, low-complexity regions or enriched in cysteine, proline or glycine or polar residues. Subregions are abundantly detected in IDRs of the human proteome. The new version of MobiDB-lite represents a new step for the proteome level analysis of protein disorder. Availability and implementation Both the MobiDB-lite 3.0 source code and a docker container are available from the GitHub repository: https://github.com/BioComputingUP/MobiDB-lit

Critical assessment of protein intrinsic disorder prediction.

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

MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins

The MobiDB (URL: mobidb.bio.unipd.it) database of protein disorder and mobility annotations has been significantly updated and upgraded since its last major renewal in 2014. Several curated datasets for intrinsic disorder and folding upon binding have been integrated from specialized databases. The indirect evidence has also been expanded to better capture information available in the PDB, such as high temperature residues in X-ray structures and overall conformational diversity. Novel nuclear magnetic resonance chemical shift data provides an additional experimental information layer on conformational dynamics. Predictions have been expanded to provide new types of annotation on backbone rigidity, secondary structure preference and disordered binding regions. MobiDB 3.0 contains information for the complete UniProt protein set and synchronization has been improved by covering all UniParc sequences. An advanced search function allows the creation of a wide array of custom-made datasets for download and further analysis. A large amount of information and cross-links to more specialized databases are intended to make MobiDB the central resource for the scientific community working on protein intrinsic disorder and mobility

Critical assessment of protein intrinsic disorder prediction

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

DisProt 7.0: a major update of the database of disordered proteins

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

MobiDB: Intrinsically disordered proteins in 2021

The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.Fil: Piovesan, Damiano. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Escobedo, Nahuel Abel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Viczián, András. Università di Padova; ItaliaFil: Mičetić, Ivan. Università di Padova; ItaliaFil: Quaglia, Federica. Università di Padova; ItaliaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Ramasamy, Pathmanaban. Vrije Unviversiteit Brussel; Bélgica. University of Ghent; Bélgica. Interuniversity Institute of Bioinformatics in Brussels; BélgicaFil: Dosztányi, Zsuzsanna. Eötvös Loránd University; HungríaFil: Vranken, Wim F.. Vrije Unviversiteit Brussel; Bélgica. Interuniversity Institute of Bioinformatics in Brussels; BélgicaFil: Davey, Norman E.. The Institute Of Cancer Research; Reino UnidoFil: Parisi, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Fuxreiter, Monika. Università di Padova; ItaliaFil: Tosatto, Silvio C. E.. Università di Padova; Itali

MobiDB 3.0: More annotations for intrinsic disorder, conformational diversity and interactions in proteins

The MobiDB (URL: mobidb.bio.unipd.it) database of protein disorder and mobility annotations has been significantly updated and upgraded since its last major renewal in 2014. Several curated datasets for intrinsic disorder and folding upon binding have been integrated from specialized databases. The indirect evidence has also been expanded to better capture information available in the PDB, such as high temperature residues in X-ray structures and overall conformational diversity. Novel nuclear magnetic resonance chemical shift data provides an additional experimental information layer on conformational dynamics. Predictions have been expanded to provide new types of annotation on backbone rigidity, secondary structure preference and disordered binding regions. MobiDB 3.0 contains information for the complete UniProt protein set and synchronization has been improved by covering all UniParc sequences. An advanced search function allows the creation of a wide array of custom-made datasets for download and further analysis. A large amount of information and cross-links to more specialized databases are intended to make MobiDB the central resource for the scientific community working on protein intrinsic disorder and mobility.Fil: Piovesan, Damiano. Università di Padova; ItaliaFil: Tabaro, Francesco. Università di Padova; ItaliaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; Italia. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaFil: Micetić, Ivan. Università di Padova; ItaliaFil: Camilloni, Carlo. Università degli Studi di Milano; ItaliaFil: Davey, Norman. Universidad de Dublin; IrlandaFil: Dosztányi, Zsuzsanna. Eötvös Loránd University; HungríaFil: Mészáros, Bálint. Eötvös Loránd University; HungríaFil: Monzón, Alexander. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Parisi, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Schad, Eva. Hungarian Academy Of Sciences; HungríaFil: Sormanni, Pietro. University of Cambridge; Reino UnidoFil: Tompa, Peter. Vrije Unviversiteit Brussel; BélgicaFil: Vendruscolo, Michele. University of Cambridge; Reino UnidoFil: Vranken, Wim F.. Vrije Unviversiteit Brussel; BélgicaFil: Tosatto, Silvio C. E.. Università di Padova; Itali

RepeatsDB in 2021: Improved data and extended classification for protein tandem repeat structures

The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures.Fil: Paladin, Lisanna. Università di Padova; ItaliaFil: Bevilacqua, Martina. Università di Padova; ItaliaFil: Errigo, Sara. Università di Padova; ItaliaFil: Piovesan, Damiano. Università di Padova; ItaliaFil: Mičetić, Ivan. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Fabre, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: López, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Nilsson, Juliet Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Ríos, Javier Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Lorenzano Menna, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Cabrera, Maia Diana Eliana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: González Buitrón, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Gonçalves Kulik, Mariane. Johannes Gutenberg Universitat Mainz; AlemaniaFil: Fernández Alberti, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Fornasari, Maria Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Parisi, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Lagares, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Biológicas; ArgentinaFil: Hirsh, Layla. Pontificia Universidad Católica de Perú; PerúFil: Andrade Navarro, Miguel A.. Johannes Gutenberg Universitat Mainz; AlemaniaFil: Kajava, Andrey V. Centre National de la Recherche Scientifique; FranciaFil: Tosatto, Silvio C E. Università di Padova; Itali

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin