Unraveling the structural and functional consequences of non-synonymous single-nucleotide polymorphisms (nsSNPs) in human SOCS2: an in silico approach
Background
The protein from suppressors of cytokine signaling (SOCS) family regulates immune response by modulating the signaling pathways of cytokines. SOCS2, a member of this family, plays an important role in regulating growth hormone receptors, the JAK-STAT pathway, energy homeostasis, and other biological processes. Multiple non-synonymous SNPs (nsSNPs) have been found in the SOCS2 gene, which could affect protein function. However, there is limited understanding of disease susceptibility and abnormal functioning associated with these mutated SOCS2 gene.
Results
In this study, we examined nsSNPs in the human SOCS2 gene to evaluate their effects on protein stability, structure, and function through in silico approaches. SIFT; PhD-SNP, PROVEAN, PMut, PANTHER, PolyPhen-2, SNPs&GO, I-Mutant 3.0, and MUpro were the bioinformatics tools used to forecast the most harmful SNPs. ConSurf found the eight nsSNPs (L71F, G102R, G51E, G47R, R96Q, Y49H, P155Q, and I171S) to be present in highly conserved region, thus affecting their protein stability. The Project HOPE analysis predicts the 3D structure of the eight respective mutated proteins and assesses their potential molecular impact on protein function and structure. After a series of analysis, three mutants (G47R, Y49H, and I171S) were subjected to molecular dynamic simulation, principal component analysis, and free energy landscape to understand their impact during the course of mutation. The STRING algorithm was used to predict protein–protein interactions. Finally, KM plotter analysis showed that deregulation of SOCS2 gene expression has a significant impact on the patients’ survival rate of different types of cancers.
Conclusion
Our study has identified the eight most high-risk SNPs of SOCS2 gene that may contribute to diseases development associated with growth hormone signaling, immune dysregulation, and other energy metabolism.
