In-Silico Methods for Investigating the Effect of Single Nucleotide Polymorphisms on the Structure and Function of Proteins: A Review

Abstract

Single nucleotide polymorphisms (SNP) are associated with diseases and drug response variabilities in humans. Elucidating the damaging and disease-associated SNPs using wet-laboratory approaches can be challenging and resource-demanding due to the large number of SNPs in the human genome. Due to the growth in the field of computational biology and bioinformatics, algorithms have been developed to help screen and filter out the most deleterious SNPs that are worth considering for wet-laboratory studies. Here we review the existing in-silico based methods used to predict and characterize the effects of SNPs on protein structure and function. This cutting-edge approach will facilitate the search for novel therapeutics, help understand the etiology of diseases and fast-track the personalized medicine agenda.

An In silico Approach towards Finding the Cancer-Causing Mutations in Human MET Gene

“…Most of the bioinformatics algorithms make their calculations based on sequence, structure, physicochemical, and evolutionary conservation to identify the deleterious nsSNP. These in silico-based modeling help to clarify the different properties of the protein molecules, for detailed information on the conformational changes of the protein [ 9 ]. They can control many more variables much more specifically within a short time than wet laboratory experiments, which enable researchers to explore the different components in various ways within the shortest possible time.…”