4.8 Article

MobiDB: intrinsically disordered proteins in 2021

期刊

NUCLEIC ACIDS RESEARCH
卷 49, 期 D1, 页码 D361-D367

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkaa1058

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资金

  1. European Union's Horizon 2020 research and innovation programme [778247]
  2. Italian Ministry of University and Research (MIUR), PRIN [2017483NH8]
  3. Research Foundation Flanders (FWO) [G.0328.16N]
  4. Cancer Research UK Senior Cancer Research Fellowship [C68484/A28159]
  5. Universidad Nacional de Quilmes [PUNQ 1004/11]
  6. ANPCyT [PICT-2014-3430]
  7. IDPfun [778247]

向作者/读者索取更多资源

The latest version of MobiDB database provides more flexibility and visualization tools, allowing users to search and download large datasets more quickly.
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.

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