Predicting the functional consequences of non-synonymous single nucleotide polymorphism (nsSNPs) in human IL-18 gene: an in-silico approach

Single nucleotide polymorphisms (SNPs) are prevalent genetic variations that can alter protein structure and function, contributing to disease susceptibility and progression. Among SNPs, non-synonymous SNPs (nsSNPs) occurring in coding regions lead to amino acid substitutions, potentially altering protein properties. Interleukin 18 (IL-18), a pro-inflammatory cytokine, plays a significant role in maintaining immune responses, inflammation, and cell signaling and has been associated with various inflammatory diseases and cancer. Understanding the impact of nsSNPs in IL-18 protein structure, function, and disease association can be crucial in understanding its biological roles and clinical implications. The study aims to predict the functional consequence of nsSNPs in the human IL18 gene and explore the correlation between IL-18 dysregulation and cancer patient survival rates. The study involves an in-silico approach to identify, characterize, and validate harmful nsSNPs. The tools include SIFT, PROVEAN, and PolyPhen-2 to identify deleterious SNP. I-Mutant 2.0 was used to assess protein stability, MutPred2 was used to identify disease-associated SNPs, and Clustal Omega and ConSurf were used for conservation analysis. Furthermore, the tertiary structure of the mutant protein was modelled and compared to the wild type using I-Tasser, ChimeraX, and ClusPro. Finally, the Kaplan Meier plot explores the correlation between gene deregulation and cancer patient survival rates. Analysis of 7802 SNPs identified 31 high-confidence nsSNPs in coding regions, with stability analysis revealing 23 destabilizing and 5 stabilizing nsSNPs. MutPred2 suggested potential functional changes. Conservation analysis identified critical residues, including D71G, E67D, E34A, and S111F (highly conserved and exposed) and Y24H, A162T, F137S, F137L, and V98G (conserved and buried). The mutant-modelled protein showed minor deviations from wild-type IL-18 proteins. The docking result revealed altered binding affinities with the IL-18 receptor. The Kaplan-Meier analysis revealed that high IL18 expression is associated with poor survival in gastric and lung cancers, while low expression is linked to poor outcomes in breast and ovarian cancers.