INTEGRATIVE COMPUTATIONAL ANALYSIS OF CFTR MUTATIONS LINKED TO CYSTIC FIBROSIS

The CFTR gene encodes a chloride channel essential for epithelial ion transport, and deleterious nsSNPs are a major cause of cystic fibrosis (CF). CF is an autosomal recessive disorder affecting multiple systems, with symptoms including chronic cough, recurrent lung infections, bronchiectasis, reduced pulmonary function, pancreatic insufficiency, malabsorption, poor growth, salty-tasting skin, male infertility, liver disease, and diabetes. To date, over 4,000 CFTR mutations have been reported, with F508del being the most common, accounting for nearly 70% of cases. This study aimed to identify deleterious nsSNPs in the CFTR gene and evaluate their structural and functional impact using an integrative in silico pipeline. Additionally, molecular docking was performed to explore potential therapeutic modulators for cystic fibrosis. The NCBI database reported over 282,000 SNPs in CFTR, of which SNPnexus analysis identified 132 nsSNPs as highly deleterious. Furthermore, 30 nsSNPs were consistently predicted to be deleterious across 15 computational tools. Notably, variants such as I105N, K273Q, and G1249R demonstrated destabilizing effects, with RMSD values ranging from 0.93 to 0.98 Å, indicating substantial conformational alterations. Molecular docking revealed strong ligand interactions with both wild-type and mutant CFTR, particularly for apoptozole, Congo Red, NAD, melanin, and cAMP, while repurposed drugs such as nelfinavir and amprenavir demonstrated favorable binding, supporting their potential to rescue misfolded proteins. This integrative in silico study highlights 30 pathogenic CFTR nsSNPs with destabilizing structural effects and identifies potential modulators, providing a robust framework for experimental validation, drug repurposing, and personalized therapeutic strategies in cystic fibrosis.