Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Abstract

Genetic and epigenetic alterations have been reported to significantly influence the global burden of obesity. Single nucleotide polymorphisms (SNPs) including both coding and non-coding amino acid changes are the key regulators of the protein structural and functional modifications. The current computational study utilizing in silico techniques focused on the screening and identification of the most pathogenic missense SNPs of the selected candidate genes of the leptin-melanocortin and adiponectin signaling pathways provoking obesity. A total of 2424 SNPs from 9 candidate genes were extracted from the NCBI database followed by pathogenicity prediction using seven servers, SIFT, PANTHER, Meta-SNP, PhD-SNP, PredictSNP, PolyPhen-2, and SNAP2. The shortlisted variants (n = 7) were analyzed for structural stability using DynaMut, iMutant, INPS3D, MuPro, and iStable followed by the functional stability analysis (n = 3) using Mut-Pred2, Project HOPE, and I-TASSER. Gene-network analysis of the finally screened SNPs (n = 3) was created using the STRING database. Two SNPs of ADIPOR1 (rs1419320091 and rs1654109863) and one variant of MC4R (rs1159323398) were predicted in the study to be the most pathogenic resulting in altered protein functionality. Therapeutic approaches designed based on early pathogenicity predictions using in silico analysis techniques would be a new horizon for the effective control of disease prevalence.