Targeting PCSK9: Bioinformatics Analysis Reveals Functionally Damaging Missense Variants

Loo Keat Wei; Huey Chyi Loh; Lyn R Griffiths

doi:10.55230/mabjournal.v54i4.3563

Authors

Loo Keat Wei
lookw@utar.edu.my
Centre for Biomedical and Nutrition Research (CBNR), Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia; Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia https://orcid.org/0000-0003-1853-0352
Huey Chyi Loh Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia
Lyn R Griffiths Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD 4059, Australia

Keywords:

bioinformatics, low density lipoprotein receptor (LDLR), proprotein convertase subtilisin/kexin type 9 (PCSK9), single nucleotide polymorphisms (SNPs), in silico analysis

Abstract

Proprotein convertase subtilisin/kexin type 9 (PCSK9) modulates cholesterol homeostasis by targeting low-density lipoprotein receptor (LDLR) for lysosomal degradation. Genetic polymorphisms in PCSK9 can alter its autocatalytic processing, secretion, or binding affinity to LDLR. Reduce binding efficiency between PCSK9 and LDLR leads to elevated low-density lipoprotein cholesterol (LDL-C) level, thereby promoting atherosclerotic plaque formation and increasing the risk of ischemic stroke. The objective of this study was to identify the most functionally significant non-synonymous single-nucleotide polymorphisms (nsSNPs) in PCSK9 via an integrated in silico analysis combining functional prediction tools (PROVEAN, SIFT, PolyPhen-2, SNAP2), protein stability and disease-association predictors, ligand-binding assessment, and post-translational modification analysis. A total of 4,979 PCSK9 variants were retrieved from Ensembl GRCh37/hg19, and HGMD. Functional annotation using PROVEAN, SIFT, PolyPhen-2, and SNAP2 identified 253 nsSNPs, with PolyPhen-2 predicting the largest subset. Upon filtering through the protein stability, disease association, ligand binding, and post-translational modification, five nsSNPs (W156R, H226L, H229R, G337R, and G394V) emerged as the most deleterious, with potential to disrupt secondary autocatalytic processing and significantly impair LDLR-PCSK9 interactions. These findings highlight novel candidate variants that may serve as diagnostic biomarkers and therapeutic targets in dyslipidemia and cardiovascular disease.

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