IN-SILICO IDENTIFICATION OF nsSNPs IN BRCA1 TO PREDICT BREAST CANCER SUSCEPTIBILITY

Abstract

Breast cancer is among the most common cancers affecting women globally, with many inherited genetic variations arising from non-synonymous single-nucleotide polymorphisms that alter the function of tumor-suppressor and DNA-repair genes. This study employs an in-silico approach to identify and characterize potentially deleterious nsSNPs in the BRCA1 gene, a key component of the homologous recombination repair pathway. From NCBI dbSNP and UniProt databases using ID: P38398, 44306 BRCA1 nsSNPs were retrieved and 226 were selected for systematic analysis using SIFT, PROVEAN, FATHMM, and PolyPhen-2. Fifty-six variants consistently predicted as deleterious across all tools were further assessed for protein stability changes via SNPs&GO, I-Mutant, and MuPro. We identified 15 high-confidence deleterious BRCA1 nsSNPs which were further subjected to MutPred2 analysis. Several variants exceeded the pathogenicity threshold (>0.75), indicating strong confidence in their deleterious nature. These mutations were predicted to cause critical molecular disruptions, including altered transmembrane regions and DNA binding, loss or gain of post-translational modifications, structural instability, disruption of secondary structure elements (helix, loop, strand), and changes in solvent accessibility. 3D structural modeling with SWISS- MODEL revealed seven mutations M18R, G98D, V11E, L22S, M18T, C39Y, and C61W predicted to significantly destabilize the BRCA1 protein and impair its DNA repair efficiency. These findings provide valuable insights into the structural and functional implications of BRCA1 variants, forming a basis for future experimental validation in hereditary breast cancer risk prediction.