A comprehensive in silico analysis of multiple sclerosis related non-synonymous SNPs and their potential effects on protein structure and function.

ABSTRACT

BACKGROUND: Multiple Sclerosis (MS) is an autoimmune and central nervous system disease characterized by an inflammatory demyelinating process in the brain. Although the exact cause of MS is still unclear, environmental, and genetic factors are known to play a role in the development of disease. New molecular markers must be identified to understand the mechanism of disease formation and progression. We investigated the effects of MS-related non-synonymous single-nucleotide polymorphisms (nsSNPs) on the structure and function of identified proteins in this study. METHODS: Missense variations associated with MS were extracted from the NHGRI-EBI GWAS database. Functional and structural analysis of nsSNPs on mapped genes was performed using gProfiler, Wikipathway, KEGG, Reactome and Gene ontology programs (p < 0.05 was accepted statistically significant). Amino acid sequence-based analysis was performed to identify deleterious variants by using PROVEAN and PredictSNP tools. Finally, protein structure analyzes were performed on deleterious protein variants by DynaMut, Mutabind2 and Missense3D servers to identify changes in protein stability and flexibility. RESULTS: 10 target nsSNPs were identified. Among these rs34536443, rs10936599, rs2293152, rs11808092, rs1129183 were found deleterious according to amino acid sequence-based analysis. Furthermore, structure-based analyses show that TYK2 (P1104A), MYNN (H6Q), EVI5 (Q612H), and LZTFL1 (D246N) substitutions increase protein stability and decrease structure flexibility, whereas STAT3 (R426G) substitution decreases protein stability and increases structure flexibility. CONCLUSION: We revealed that identified nsSNPs have potential effects on stability and flexibility of the target proteins. The prominent target genes are thought to have significant impacts on the pathogenesis of MS. Further in vitro and in vivo studies are required to validate our in silico results.