Molecular Effects of Missense Mutations
Acta Universitatis Tamperensis,
No. 1528


By Janita Thusberg
July 2010
Tampere University Press
Distributed By Coronet Books
ISBN: 9789514481017
149 pages
$82.50 Paper Original

Available data on polymorphisms in the human genome are expanding rapidly, however knowledge of the possible disease association of polymorphisms and the molecular mechanisms of genetic disease is lagging due to the laborious and time-consuming nature of experimental studies. Bioinformatics studies can efficiently produce useful information to rationalise and guide further experimental study, and to short-list the most interesting cases from the pool of accumulating data.

Some genetic variations, termed non-synonymous single nucleotide polymorphisms (nsSNPs), cause amino acid substitutions in the protein product of the gene. nsSNPs are the most common type of genetic variation among humans, and pathogenic nsSNPs, also termed missense mutations, account for approximately half of the allelic variants causative of hereditary disease. Amino acid substitutions may have diverse effects on protein structure and function, although some are functionally neutral.

In this study, the wide-ranging effects of amino acid substitutions were investigated at the protein level, and based on the analyses of missense mutations, the molecular basis of a number of hereditary diseases was elucidated. A protocol for the bioinformatics study of mutational effects was designed and implemented. The protocol serves as a basis for the development of a new service for predicting the effects of a missense mutation.

Several computational methods for predicting the possible pathogenicity of nsSNPs have been developed. These methods are based on evolutionary information and/or varying structural descriptors of the protein in question. These methods aim at automating the annotation process of nsSNP effects and therefore would be very useful for the mutation research community. In this study the performance of nine available prediction methods was evaluated using a dataset of over 60,000 missense mutations and polymorphisms. Significant differences in the prediction power of individual programs were observed, regardless of the apparent similarities between the programs. Some of the predictors perform well enough to be used in prioritising cases for further investigation; however more accurate methods are needed for reliable annotation of the putative effects of an nsSNP.

This study yielded interesting insights at the molecular level mechanisms of hereditary diseases, which can be utilised in further experimental studies. The protocol for mutation analysis is a comprehensive method for studying mutational effects and its further development into a web service will provide the mutation research community a novel tool for efficient analysis beyond the scope of existing methods.

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