Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis: Analyzing Variation at the Protein Level

Abstract

With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as high throughput sequencing. In this paper, we discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of nonsynonymous single nucleotide polymorphisms. We conclude by suggesting a protocol for future analyses of the structural effects of nonsynonymous single nucleotide polymorphisms on proteins and protein complexes.

Figure 1. Protocol for analyzing non-synonymous SNPs

A flowchart depicting the steps required to analyze the effects of non-synonymous SNPs using structural bioinformatics. The process can be divided into 3 phases: 1) data retrieval; 2) data creation; and 3) data analysis.