Prediction of the Most Deleterious Missense Variants of Human Somatostatin Gene by Combining Computational Algorithms, Molecular Docking and Dynamic Simulations

Al-Shuhaib, Mohammed Baqur S.

doi:10.30491/jabr.2021.286164.1380

Prediction of the Most Deleterious Missense Variants of Human Somatostatin Gene by Combining Computational Algorithms, Molecular Docking and Dynamic Simulations

Document Type : Original Article

Author

Mohammed Baqur S. Al-Shuhaib

Department of Animal Production, College of Agriculture, Al-Qasim Green University, Al-Qasim, Babil 51001, Iraq

10.30491/jabr.2021.286164.1380

Abstract

Introduction: Somatostatin (SST) is a versatile hormone that plays a key role in inhibiting the secretion of several pituitary hormones. It is well known that SST participates in the regulation of other essential proteins throughout the body. The abnormal function of the SST protein is still not fully understood. In this study, the disease susceptible Single Nucleotide Polymorphisms (SNPs) in SST were classified by using different computational algorithms.
Materials and Methods: Sequence-based and structure-based computational tools were employed to classify the most disease susceptible nsSNPs that would have the most harmful effects on SST protein. Docking and molecular dynamic simulations were performed to compare the ability of the normal SST and its most deleterious mutants to bind with corresponding SSTRs to assess the potential role of these nsSNPs to alter protein-protein interactions.
Results: Two nsSNPs, namely L13P, and G104S, were considered to have the most severe functional consequences on the 3D structure of SST. These results were confirmed by molecular dynamic simulations. Docking of SST and its mutant models with SST receptors (SSTR1-SSTR5) showed remarkable roles of both mutant L13P and G104S in altering the binding of SST with SSTR2 and SSTR5.
Conclusions: The findings of the present study provide the first comprehensive in silico prediction for assessing the damaging effects of nsSNPs on SST, which may help in a better understanding of how the altered SST would impact the overall health of the body. This study may provide a platform to conduct large-scale experiments on the genetic polymorphism of SST.

Keywords

Prediction

Protein

In Silico

SNP

SST

SSTR