In silico prediction of deleterious non-synonymous SNPs in .

Background: , 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 and predicted structural changes associated with mutants that disrupt normal protein-protein interactions using different computational algorithms.

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

Result: 417 nsSNPs of were identified, 6 of which are considered deleterious by 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 may serve as potential targets for different proteomic studies, large population-based studies, diagnoses, and therapeutic interventions.