Defining and predicting structurally conserved regions in protein superfamilies
Author(s) -
Ivan Huang,
Jimin Pei,
Nick V. Grishin
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts682
Subject(s) - protein superfamily , pairwise comparison , homologous chromosome , computational biology , set (abstract data type) , computer science , sequence alignment , matthews correlation coefficient , sequence (biology) , conserved sequence , homology (biology) , protein structure , structural alignment , multiple sequence alignment , biology , genetics , artificial intelligence , peptide sequence , gene , biochemistry , support vector machine , programming language
The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment.
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