Molecular Docking and Functional Analysis of APOA5 (G185C), PCSK9 (R46L, R93C), LPL (N318S), and LIPA (T16P) Genes Mutations Associated with Coronary Artery Disease

Background: Coronary artery disease (CAD) is strongly influenced by genetic factors, particularly non-synonymous single nucleotide polymorphisms (nsSNPs) that alter protein structure and function. Variants within lipid metabolism–related genes such as APOA5, PCSK9, LPL, and LIPA are implicated in atherosclerosis progression, yet their molecular consequences remain incompletely defined. Objective: This study aimed to comprehensively characterize the structural and functional impact of selected CAD-associated nsSNPs using an integrative computational approach. Methods: Reported nsSNPs from the GWAS catalog were retrieved, and detailed variant data were obtained from UniProt and NCBI. Functional impacts were predicted using sequence homology–based (SIFT, PROVEAN, Mutation Assessor), machine learning–based (SNAP2, SuSPect, PolyPhen-2), and consensus predictors (Meta-SNP). Structural stability was assessed by I-Mutant, MUpro, mCSM, and DynaMut2, while evolutionary conservation, surface accessibility, and post-translational modifications were analyzed with ConSurf, NetSurf-2.0, and MusiteDeep. Protein–protein interactions were mapped via STRING, and molecular docking was performed using ClusPro and SwissDock. Results: APOA5 G185C, PCSK9 R46L and R93C, LPL N318S, and LIPA T16P were consistently predicted to be deleterious, with most variants exhibiting negative ΔΔG values indicative of destabilization. Docking analysis revealed reduced binding affinities and altered interaction residues, suggesting disruption of lipid regulatory pathways. Conclusion: This integrative in-silico analysis highlights critical CAD-related nsSNPs that destabilize protein structure and impair molecular interactions, underscoring their potential as biomarkers and therapeutic targets.