In silico prediction of deleterious non-synonymous SNPs in STAT3.

Abstract

Background: STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.

Objective: We identified disease susceptible single nucleotide polymorphisms (SNPs) in STAT3 and predicted structural changes associated with mutants that disrupt normal protein-protein interactions using different computational algorithms.

Methods: Several in silico tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&GO, were used to determine nsSNPs of the STAT3. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.

Result: 417 nsSNPs of STAT3 were identified, 6 of which are considered deleterious by in silico SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein-protein interaction tools.

Conclusion: The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of STAT3 may serve as potential targets for different proteomic studies, large population-based studies, diagnoses, and therapeutic interventions.