Amino acid mutations may have diverse effects on protein structure and function. Thus reliable information about the protein sequence variations is essential to gain insights into disease genotype-phenotype correlations. With the recent availability of the complete genome sequence and the accumulation of variation data, determining the effects of amino acid substitution will be the next challenge in mutation research. The molecular consequences of amino acid mutations can readily be predicted by numerous bioinformatic methods, which analyze the mutation effects from different points of view. In this review, these approaches are categorized according to their analysis principles. The applicability of these tools for inference of mutation-structure-function relationship is also recapitulated. When the human diseases are likely to involve defects in multiple genes, most of the current mutation analysis focuses on single point mutation and lacks an expansive proteome-wide perspective. We propose in this review the application of the existing computational tools in the analysis of correlated mutations at a system level. Directions for future developments and implications are discussed, which will help to understand the networks underlying human disease.
Keywords: Proteome, amino acid mutation, computational analysis, missense mutation, multiple sequence alignment
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