2013
DOI: 10.1080/07391102.2012.750249
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Bioinformatic analysis of protein families for identification of variable amino acid residues responsible for functional diversity

Abstract: Proteins within a single family usually share a common function but differ in more specific features and can be divided into subfamilies with different properties. Availability of genomic, structural, and functional information implemented into numerous databases provides new opportunities for bioinformatic analysis of homologous proteins. In this work, new method of bioinformatic analysis has been developed to identify subfamily-specific positions (SSPs)--conserved only within protein subfamilies, but differe… Show more

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Cited by 28 publications
(26 citation statements)
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“…Functional subfamilies can be defined according to experimentally obtained functional annotation. However, because the amount of experimental data is limited, similarity-based clustering strategies have been proposed to automatically predict functional subfamilies in large datasets [67]. SSPs were shown to be responsible for functional discrimination among homologous proteins; they are preferentially associated with catalytic and regulatory sites in enzymes [69].…”
Section: Systematic Rational Designmentioning
confidence: 99%
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“…Functional subfamilies can be defined according to experimentally obtained functional annotation. However, because the amount of experimental data is limited, similarity-based clustering strategies have been proposed to automatically predict functional subfamilies in large datasets [67]. SSPs were shown to be responsible for functional discrimination among homologous proteins; they are preferentially associated with catalytic and regulatory sites in enzymes [69].…”
Section: Systematic Rational Designmentioning
confidence: 99%
“…3A). The latest bioinformatic procedures to detect SSPs implement entropy-based measures of subfamily-dependent distribution of amino acids, take into account sequence and structural information, physicochemical properties of amino acid side chains and perform ranking to automatically select the most statistically significant hotspots for further evaluation [67,68]. Functional subfamilies can be defined according to experimentally obtained functional annotation.…”
Section: Systematic Rational Designmentioning
confidence: 99%
“…"QuickZebra + 3D" -sequence and structural analysis The "QuickZebra + 3D" mode in addition to the sequence input requires a PDB structure file that should correspond to one of the MSA sequences. Incorporation of the 3D information can significantly improve Zebra predictions (Suplatov et al, 2013). Moreover, the PDB file will be used to produce PyMol session files (.pse) with structural representations of the SSPs for each subfamily classification.…”
Section: "Quickzebra" -Sequence Analysis Onlymentioning
confidence: 99%
“…Zebra implements the recently developed bioinformatic algorithm (Suplatov et al, 2013). A multiple sequence alignment (MSA) and, optionally, structural information about the protein superfamily are used as an input.…”
Section: Identification Of Subfamily-specific Positionsmentioning
confidence: 99%
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