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PEDS Advance Access published online on December 19, 2007
Protein Engineering Design and Selection, doi:10.1093/protein/gzm084
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Physicochemical feature-based classification of amino acid mutations

Bairong Shen1,3, Jinwei Bai1 and Mauno Vihinen1,2

1 Institute of Medical Technology, FI-33014 University of Tampere, Finland 2Research Unit, Tampere University Hospital, FI-33520 Tampere, Finland

3 To whom correspondence should be addressed. E-mail: bairong.shen{at}uta.fi

A huge quantity of gene and protein sequences have become available during the post-genomic era, and information about genetic variations, including amino acid substitutions and SNPs, is accumulating rapidly. To understand the effects of these changes, it is often essential to apply bioinformatics tools. Where there is a lack of homologous sequences or a three-dimensional structure, it becomes essential to predict the effects of mutations based solely on protein sequence information. Several computational methods utilizing machine learning techniques have been developed. These predictions generally use the 20-alphabet amino acid code to train the model. With limited available data, the 20-alphabet amino acid features may introduce so many parameters that the model becomes over-fitted. To decrease the number of parameters, we propose a physicochemical feature-based method to forecast the effects of amino acid substitutions on protein stability. Protein structure alterations caused by mutations can be classified as stabilizing or destabilizing. Based on experimental folding-unfolding free energy ({Delta}{Delta}G) values, we trained a support vector machine with a cleaned data set. The physicochemical properties of the mutated residues, the number of neighboring residues in the primary sequence and the temperature and pH were used as input attributes. Different kernel functions, attributes and window sizes were optimized. An average accuracy of 80% was obtained in cross-validation experiments.

Keywords: amino acid/mutation/physicochemical properties/protein stability/support vector machine

Received August 28, 2007; revised October 22, 2007; accepted November 22, 2007.


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 Protein Eng Des SelHome page
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