Prediction by Graph Theoretic Measures of Structural Effects in Proteins Arising from Non-Synonymous Single Nucleotide Polymorphisms

Abstract

Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo ( Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue–residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.

Author Summary

Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base differences between individual genomes that lead to amino acid changes in protein sequences. They may influence an individual's susceptibility to disease or response to drugs through their impacts on a protein's structure and hence cause functional changes. In this paper, we present a new methodology to estimate the impact of nsSNPs on disease susceptibility. This is made possible by characterising the protein structure and the change of structural stability due to nsSNPs. We show that our computer program Bongo, which describes protein structures as interlinked amino acids, can identify conformational changes resulting from nsSNPs that are closely associated with pathological consequences. Bongo requires only structural information to analyze nsSNPs and thus is complementary to methods that use evolutionary information. Bongo helps us investigate the suggestion that most disease-causing mutations disturb structural features of proteins, thus affecting their stability. We anticipate that making Bongo available to the community will facilitate a better understanding of disease-associated nsSNPs and thus benefit personal medicine in the future.