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Genome-wide survey of remote homologues for protein domain superfamilies of known structure reveals unequal distribution across structural classes

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Abstract

Domains are the basic building blocks of proteins which can combine to give rise to different domain architectures. Annotation of domains in a sequence is the first step towards understanding the biological function. Since there are a limited number of folds and evolutionarily related proteins have a similar structure, function can be inferred through remote homology. Computational sequence searches were performed for remote homologues on genomes of around ∼160 000 different organisms, starting from nearly 11 000 superfamily queries of known structure. Case studies revealed that most of the associated domains are involved in the same biological process. Using all the proteins predicted to have at least one structural domain, a coverage of 61% of Pfam families was achieved which is higher than the existing methods (43.36% by SIFTS). Taxonomic analysis of the proteins revealed 493 superfamilies in all the major kingdoms of life and a few lateral gene transfers between viruses and cellular organisms. The distribution of remote homologues across different classes, folds and superfamilies was studied and reveals that sequences are unequally distributed across structural classes. Finally, domain architectures were computed for the homologues and these data were compiled for each superfamily and organism.

Graphical abstract: Genome-wide survey of remote homologues for protein domain superfamilies of known structure reveals unequal distribution across structural classes

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Publication details

The article was received on 09 Jan 2018, accepted on 22 Jun 2018 and first published on 27 Jun 2018


Article type: Research Article
DOI: 10.1039/C8MO00008E
Citation: Mol. Omics, 2018, Advance Article
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    Genome-wide survey of remote homologues for protein domain superfamilies of known structure reveals unequal distribution across structural classes

    M. S. Iyer, A. G. Joshi and R. Sowdhamini, Mol. Omics, 2018, Advance Article , DOI: 10.1039/C8MO00008E

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