INTEGRATIVE PIPELINE FOR PREDICTING PROTEIN STABILITY FROM GENOMIC VARIANTS IDENTIFIED THROUGH HIGH-THROUGHPUT SEQUENCING

While computational predictors excel at interpreting single genetic variants, they often fail to capture the cumulative molecular impact of missense variants contributing to disease phenotypes. This study presents a novel integrative bioinformatic pipeline to quantitatively assess the cumulative impact of variants on protein stability and function, applied to high-throughput sequencing data from a Qatari breast cancer cohort. The pipeline integrates outputs from SnpSift, SnpEff, CADD, ClinVar, InterVar, and MutPred2, synthesizing population frequency, functional consequence, evolutionary constraint, clinical significance, and mechanistic disruption into a unified scoring framework. Weighted and normalized variant scores are aggregated to derive gene-level instability indices, enabling robust quantitative comparisons of mutational burden across genes. Application of this framework identified RAD51C, CHEK2, and PTEN as high-impact genes (Cumulative Impact ≥ 0.5), exhibiting significant destabilization despite fewer variants compared to BRCA1/2. Mechanistically, single missense substitutions were shown to disrupt secondary structural elements, propagating alterations to tertiary and quaternary protein architecture, resulting in instability and functional impairment. This scalable, gene-centric approach advances precision functional genomics for complex diseases.