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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. [ABSTRACT FROM AUTHOR]

Background: The lack of effective treatment against the highly infectious SARS-CoV-2 has aggravated the already catastrophic global health issue. Here, in an attempt to design an efficient vaccine, a thorough immunoinformatics approach was followed to predict the most suitable viral proteins epitopes for building that vaccine. Methods: The amino acid sequences of four structural proteins (S, M, N, E) along with one potentially antigenic accessory protein (ORF1a) of SARS-CoV-2 were inspected for the most appropriate epitopes to be used for building the vaccine construct. Several immunoinformatics tools were used to assess the antigenicity (VaxiJen server), immunogenicity (IEDB immunogenicity tool), allergenicity (AlgPred), toxigenicity (ToxinPred server), interferongamma inducing capacity (IFNepitope server), and the physicochemical properties of the construct (ProtParam tool). Results: The final candidate vaccine construct consisted of 468 amino acids, encompassing 29 epitopes. The CTL epitopes that passed the antigenicity, allergenicity, toxigenicity and immunogenicity assessment were four epitopes from S protein, one from M protein, two from N protein, 12 from the ORF1a polyprotein and none from E protein. While the HTL epitopes that passed the antigenicity, allergenicity, toxigenicity and INF-γ were one from S protein, three from M protein, six from the ORF1a polyprotein and none from N and E proteins. All the vaccine properties and its ability to trigger the humoral and cell-mediated immune response were validated computationally. Molecular modeling, docking to TLR3, simulation, and molecular dynamics were also carried out. Finally, a molecular clone using pET28::mAID expression plasmid vector was prepared. Conclusion: The overall results of the study suggest that the final multi-epitope chimeric construct is a potential candidate for an efficient protective vaccine against SARS-CoV-2. [ABSTRACT FROM AUTHOR]