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Briefings in Bioinformatics Advance Access originally published online on November 13, 2007
Briefings in Bioinformatics 2008 9(1):46-56; doi:10.1093/bib/bbm052
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© The Author 2007. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Correlated substitution analysis and the prediction of amino acid structural contacts

David S. Horner, Walter Pirovano and Graziano Pesole

Corresponding author. David S. Horner, Department of Biomolecular Sciences and Biotechnology, University of Milan, via Celoria 26, 20133 Milano, Italy. Tel: +39 50314880; Fax: +39 50314912; E-mail: David.horner{at}unimi.it

It has long been suspected that analysis of correlated amino acid substitutions should uncover pairs or clusters of sites that are spatially proximal in mature protein structures. Accordingly, methods based on different mathematical principles such as information theory, correlation coefficients and maximum likelihood have been developed to identify co-evolving amino acids from multiple sequence alignments. Sets of pairs of sites whose behaviour is identified by these methods as correlated are often significantly enriched in pairs of spatially proximal residues. However, relatively high levels of false-positive predictions typically render such methods, in isolation, of little use in the ab initio prediction of protein structure. Misleading signal (or problems with the estimation of significance levels) can be caused by phylogenetic correlations between homologous sequences and from correlation due to factors other than spatial proximity (for example, correlation of sites which are not spatially close but which are involved in common functional properties of the protein). In recent years, several workers have suggested that information from correlated substitutions should be combined with other sources of information (secondary structure, solvent accessibility, evolutionary rates) in an attempt to reduce the proportion of false-positive predictions. We review methods for the detection of correlated amino acid substitutions, compare their relative performance in contact prediction and predict future directions in the field.

Keywords: correlated mutation analysis, amino acid contacts, functional correlation, phylogeny

Submitted: July 31, 2007. Received (in revised form): October 8, 2007.


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