Computational Analysis of Deleterious nsSNPs in INS Gene Associated with Permanent Neonatal Diabetes Mellitus

Abstract

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

Keywords: computational methods; genetic variations; insulin genes; non-synonymous single nucleotide polymorphisms; permanent neonatal diabetes mellitus; personalized medicine.

Figure 1

Flowchart to identify and categorize nsSNPs in the INS gene; each step indicates the tools used. If one nsSNP is considered deleterious in each step using certain tool it will be directly analyzed in the following tool or in the next step using another computational tool. In steps 8 and 9, tools are used to analyze and display structural changes.

Figure 2

A Root-Mean-Square Deviation (RMSD) analysis of proteins throughout molecular dynamics (MD) simulations.

Figure 3

The Root-Mean-Square fluctuation (RMSF) of individual atoms in a protein throughout the MD simulation.

Figure 4

The radius of gyration of a wild-type protein and its three variations (C96R, C96Y, and P52R) as a function of time throughout a simulation duration of 1000 ps.

Figure 5

The Solvent Accessible Surface Area (SASA) of a wild-type protein and the three variants (C96R, C96Y, and P52R) during a 1000 ps molecular dynamics simulation.

Figure 6

Quantification of the hydrogen bonds present in a wild-type protein and its three variations (C96R, C96Y, and P52R) over a 1000 ps molecular dynamics simulation.

Figure 7

Three-dimensional structure of the wild-type INS protein (PDB ID: 4i30), and its mutations, P52R, C96R, and C96Y. The sites of the mutations are shown in the surface presentation and red color. This figure was prepared using PyMol Version 2.0.

Figure 8

The three-dimensional structure of the wild-type INS protein (PDB ID: 3i40). This figure was prepared using PyMol software.

Figure 9

The three-dimensional structure of the INS protein (PDB ID: 3i40) for the mutation P52R; the site of the mutation is red. This figure was prepared using PyMol software.

Figure 10

The three-dimensional structure of the INS protein (PDB ID: 3i40) for the mutation C96R; the site of the mutation is red. This figure was prepared using PyMol software.

Figure 11

The three-dimensional structure of the INS protein (PDB ID: 3i40) for the mutation C96Y; the site of the mutation is red. This figure was prepared using PyMol software.