In Silico identification, structural modeling, and ligand docking analysis of deleterious FOXA1 mutations associated with prostate cancer progression

Prostate cancer remains a leading cause of cancer-related mortality in men, Particularly in developed countries. Mutations in the FOXA1 Gene (hepatocyte nuclear factor3-alpha) are known to play a crucial role in prostate cancer development, with approximately 8-10% of prostate exhibiting mutations in this gene. This study focuses on identifying and analyzing 16 highly deleterious non-synonymous single nucleotide polymorphism(nsSNPs) in FOXA1 to evaluate their effects on protein stability, structure, and function. A total 14,302 SNPs were retrieved, with 1303, nsSNPs identified, 665 of which were classified as deleterious. Using PolyPhen and other predictive tools (PANTHER, SNP&GO, PhD-SNP, SuSPect, and Meta-SNP), 42 Mutations were identified as potentially disease-related, while 32 mutations were predicted to significantly destabilize FOXA1 Protein. Structural analysis using SWISS-MODEL confirmed that several mutations clustered in Cancer-related domains, with validations metrics indicating higher accuracy that AlphaFold prediction. Furthermore, Protein-ligand docking and molecular dynamics simulation identified mutations such as Y175N (rs751849657), T192M (rs926601303), D202V (rs772839489), and I218T (rs780416288), which significantly alter ligand binding, potentially disrupting tumor-suppressive function. These results provide valuable insight into the Molecular mechanism underlying FOXA1 -associated prostate cancer and support the development of targeted therapeutic strategies.