MobiDB: 10 years of intrinsically disordered proteins

Abstract

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

Figure 1.

Ten years of MobiDB. Major achievements of MobiDB as described in the corresponding publications.

Figure 2.

Conditional disorder in MobiDB. MobiDB entry page of the human Paxillin protein, accession P49023. On the left, the ‘AlphaFold-structure (pLDDT)’ (top) and ‘AlphaFold-disorder (RSA)’ (bottom) features, representing high-confidence well-structured regions (cyan) and exposed residues (red), respectively. On the right, the predicted LIPs (violet), resulting from the intersection between the two sets of residues highlighted on the left. The screenshots are taken from the ‘Disorder’ tab (left) and from the ‘Binding’ tab (right) of the entry page. Residue annotations are calculated with the AlphaFold-disorder script (26) applied to AlphaFoldDB predictions.