Single nucleotide polymorphisms in the IL17A gene are associated with many types of cancer. Therefore, the identification of functional and structural polymorphisms in IL17A is important to study and determine therapeutic targets and their potential impact. In this study, several computational methods have been used to identify non-synonymous SNPs that are deleterious in the IL17A gene, including SIFT, PolyPhen2, PROVEAN, SNAP. PhD-SNP, SNP&GO and Pmut were used to predict disease-associated nsSNPs. I-mutant and Mupro were used to predict protein stability. ConSurf was used to predict conserved residues and functional regions of the protein. Finally, SOPMA followed by Project Hope software was used to predict the effect of these mutations on protein structure and function. Our study concludes that ten nsSNPs (R69Q, A92S, C94S, R95C, C99F, D107G, V121I, V142M, T145I and P149L) were shown to be potentially deleterious in IL17A. The present study represents a comprehensive in silico analysis of the IL17A gene and will be a useful tool for future studies.
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