H2r: Identification of evolutionary important residues by means of an entropy based analysis of multiple sequence alignments

URN to cite this document:

urn:nbn:de:bvb:355-epub-109291 Copy

DOI to cite this document:

10.5283/epub.10929 Copy

Merkl, Rainer ; Zwick, Matthias

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Date of publication of this fulltext: 18 Nov 2009 10:04

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Details

Item type:	Article
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central
Volume:	9
Page Range:	p. 151
Date:	2007
Institutions:	Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Rainer Merkl
Identification Number:	Value Type 18366663 PubMed ID 10.1186/1471-2105-9-151 DOI
Classification:	Notation Type Algorithms* MESH Amino Acid Sequence MESH Base Sequence MESH Entropy MESH Evolution, Molecular* MESH Molecular Sequence Data MESH Proteins/chemistry* MESH Proteins/genetics* MESH Sequence Alignment/methods* MESH Sequence Analysis, DNA/methods* MESH Sequence Analysis, Protein/methods* MESH
Dewey Decimal Classification:	500 Science > 570 Life sciences
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Partially
Item ID:	10929

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Abstract

BACKGROUND: A multiple sequence alignment (MSA) generated for a protein can be used to characterise residues by means of a statistical analysis of single columns. In addition to the examination of individual positions, the investigation of co-variation of amino acid frequencies offers insights into function and evolution of the protein and residues. RESULTS: We introduce conn(k), a novel ...

Abstract

BACKGROUND: A multiple sequence alignment (MSA) generated for a protein can be used to characterise residues by means of a statistical analysis of single columns. In addition to the examination of individual positions, the investigation of co-variation of amino acid frequencies offers insights into function and evolution of the protein and residues. RESULTS: We introduce conn(k), a novel parameter for the characterisation of individual residues. For each residue k, conn(k) is the number of most extreme signals of co-evolution. These signals were deduced from a normalised mutual information (MI) value U(k, l) computed for all pairs of residues k, l. We demonstrate that conn(k) is a more robust indicator than an individual MI-value for the prediction of residues most plausibly important for the evolution of a protein. This proposition was inferred by means of statistical methods. It was further confirmed by the analysis of several proteins. A server, which computes conn(k)-values is available at http://www-bioinf.uni-regensburg.de. CONCLUSION: The algorithms H2r, which analyses MSAs and computes conn(k)-values, characterises a specific class of residues. In contrast to strictly conserved ones, these residues possess some flexibility in the composition of side chains. However, their allocation is sensibly balanced with several other positions, as indicated by conn(k).

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