Dataset Information

Gene3D: Extensive prediction of globular domains in proteins.

ABSTRACT: Gene3D (http://gene3d.biochem.ucl.ac.uk) is a database of globular domain annotations for millions of available protein sequences. Gene3D has previously featured in the Database issue of NAR and here we report a significant update to the Gene3D database. The current release, Gene3D v16, has significantly expanded its domain coverage over the previous version and now contains over 95 million domain assignments. We also report a new method for dealing with complex domain architectures that exist in Gene3D, arising from discontinuous domains. Amongst other updates, we have added visualization tools for exploring domain annotations in the context of other sequence features and in gene families. We also provide web-pages to visualize other domain families that co-occur with a given query domain family.

SUBMITTER: Lewis TE

PROVIDER: S-EPMC5753370 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Gene3D: Extensive prediction of globular domains in proteins.

Lewis Tony E TE Sillitoe Ian I Dawson Natalie N Lam Su Datt SD Clarke Tristan T Lee David D Orengo Christine C Lees Jonathan J

Nucleic acids research 20180101 D1

Gene3D (http://gene3d.biochem.ucl.ac.uk) is a database of globular domain annotations for millions of available protein sequences. Gene3D has previously featured in the Database issue of NAR and here we report a significant update to the Gene3D database. The current release, Gene3D v16, has significantly expanded its domain coverage over the previous version and now contains over 95 million domain assignments. We also report a new method for dealing with complex domain architectures that exist i ...[more]

PMID: 29112716

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Project description:BackgroundWell-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues.ResultsUsing a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi.ConclusionsProbabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.

Dataset Information

Gene3D: Extensive prediction of globular domains in proteins.

Publications

Gene3D: Extensive prediction of globular domains in proteins.

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