Structural Location of Disease-associated Single-nucleotide Polymorphisms

Abstract

Non-synonymous single-nucleotide polymorphism (nsSNP) of genes introduces amino acid changes to proteins, and plays an important role in providing genetic functional diversity. To understand the structural characteristics of disease-associated SNPs, we have mapped a set of nsSNPs derived from the online mendelian inheritance in man (OMIM) database to the structural surfaces of encoded proteins. These nsSNPs are disease-associated or have distinctive phenotypes. As a control dataset, we mapped a set of nsSNPs derived from SNP database dbSNP to the structural surfaces of those encoded proteins. Using the alpha shape method from computational geometry, we examine the geometric locations of the structural sites of these nsSNPs. We classify each nsSNP site into one of three categories of geometric locations: those in a pocket or a void (type P); those on a convex region or a shallow depressed region (type S); and those that are buried completely in the interior (type I). We find that the majority (88%) of disease-associated nsSNPs are located in voids or pockets, and they are infrequently observed in the interior of proteins (3.2% in the data set). We find that nsSNPs mapped from dbSNP are less likely to be located in pockets or voids (68%). We further introduce a novel application of hidden Markov models (HMM) for analyzing sequence homology of SNPs on various geometric sites. For SNPs on surface pocket or void, we find that there is no strong tendency for them to occur on conserved residues. For SNPs buried in the interior, we find that disease-associated mutations are more likely to be conserved. The approach of classifying nsSNPs with alpha shape and HMM developed in this study can be integrated with additional methods to improve the accuracy of predictions of whether a given nsSNP is likely to be disease-associated.

Introduction

Single-nucleotide polymorphisms (SNPs) are the most common form of human genetic variation. The coding regions of the human genome contain about 500,000 SNPs.¹ Among these, the non-synonymous SNPs (nsSNPs) cause changes in the amino acid residues, and are likely to be an important factor contributing to the functional diversity of encoded proteins in the human population.² There are well known examples where nsSNPs affect the functional roles of proteins in signal transduction of visual, hormonal and other stimulants,3., 4. in gene regulation by altering DNA and transcription factor binding,⁵ and in maintaining the structural integrity of cells and tissues.⁶ In addition, by affecting drug-target proteins such as G-protein coupled receptors,⁷ enzymes,⁸ ion channels⁹ and proteins involved in the detoxification pathways,¹⁰ nsSNPs play important roles in the diverse responses in efficacy and toxicity of the human population to therapeutic agents.

nsSNPs can affect human physiology through many different mechanisms. nsSNPs may inactivate functional sites of enzymes¹¹ or alter splice sites and thereby form defective gene products.¹² They may destabilize proteins, or reduce protein solubility.¹³ To understand the mechanism of phenotypic variations due to nsSNPs, it is important to assess the structural consequences of the alteration of amino acid residue. A classical example is sickle-cell anemia, the first molecular disease discovered.¹⁴ First studied by Sir John Kendrew 50 years ago, sickle-cell anemia results from a single base change and residue V is changed to E at position 6 of the beta chain of hemoglobin. This residue is located at the interface of the alpha and beta chains, and the E6V mutation reduces the solubility of the deoxygenated form of hemoglobin markedly. The knowledge of structural role of this mutation is essential for understanding the disease mechanism of sickle-cell anemia.

With the advent of high-throughput SNP detection techniques, the number of known nsSNPs is growing rapidly, providing an important source of information for studying the relationship between genotypes and phenotypes of human diseases. An important study has shown recently that there is a strong correlation between disease-associated polymorphism and sites of low solvent-accessibility.¹⁵ In this study, we introduce new geometric classifications for characterizing disease associated SNPs. Here, we attempt to align SNPs to protein surface pockets and voids that may be potential functional binding regions.