MobiDB: intrinsically disordered proteins in 2021. Issue Volume 49:Issue D1(2021) (25th November 2020)

• Record Type: Journal Article
• Authors: Piovesan, Damiano; Necci, Marco; Escobedo, Nahuel; Monzon, Alexander Miguel; Hatos, András; Mičetić, Ivan; Quaglia, Federica; Paladin, Lisanna; Ramasamy, Pathmanaban; Dosztányi, Zsuzsanna; Vranken, Wim F; Davey, Norman E; Parisi, Gustavo; Fuxreiter, Monika; Tosatto, Silvio C E
• Is Part Of: Nucleic acids research. Volume 49:Issue D1(2021)
• Page Start: D361
• Page End: D367
• Publication Date: 2020-11-25
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkaa1058
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: 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.