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.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Abifadel, M., Varret, M., Rabès, J.P., Allard, D., Ouguerram, K., Devillers, M. & Boileau, C. 2003. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature Genetics, 34(2): 154-156.

Adzhubei, I., Jordan, D.M. & Sunyaev, S.R. 2013. Predicting functional effect of human missense mutations using PolyPhen-2. Current Protocols in Human Genetics, 76(1): 7-20.

Au, A. & Wei, L.K. 2017. The impact of APOA5, APOB, APOC3 and ABCA1 gene polymorphisms on ischemic stroke: evidence from a meta-analysis. Atherosclerosis, 265: 60-70.

Au, A., Griffiths, L.R., Cheng, K.K., Kooi, C.W., Irene, L. & Wei, L.K. 2015. The influence of OLR1 and PCSK9 gene polymorphisms on ischemic stroke: evidence from a meta-analysis. Scientific Reports, 5: 18224.

Benjannet, S., Hamelin, J., Chrétien, M. & Seidah, N.G. 2012. Loss-and gain-of-function PCSK9 variants cleavage specificity, dominant negative effects, and low density lipoprotein receptor (LDLR) degradation. Journal of Biological Chemistry, 287(40): 33745-33755.

Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M., Bansal, P., Bridge, A.J., ... & Xenarios, I. 2016. UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: how to use the entry view. In: Plant Bioinformatics: Methods and Protocols, pp. 23-54. Springer, New York.

Bromberg, Y. & Rost, B. 2007. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research, 35(11): 3823-3835.

Cameron, J., Holla, Ø.L., Laerdahl, J.K., Kulseth, M.A., Ranheim, T., Rognes, T. & Leren, T.P. 2008. Characterization of novel mutations in the catalytic domain of the PCSK9 gene. Journal of Internal Medicine, 263(4): 420-431.

Capriotti, E. & Fariselli, P. 2017. PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Research, 45(W1): W247-W252.

Capriotti, E., Calabrese, R., Fariselli, P., Martelli, P.L., Altman, R.B. & Casadio, R. 2013. WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics, 14(3): S6.

Chaudhary, R., Garg, J., Shah, N. & Sumner, A. 2017. PCSK9 inhibitors: a new era of lipid lowering therapy. World Journal of Cardiology, 9(2): 76-91.

Cheng, J., Randall, A. & Baldi, P. 2006. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics, 62(4): 1125-1132.

Choi, Y. & Chan, A.P. 2015. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 31(16): 2745-2747.

Chorba, J.S., Galvan, A.M. & Shokat, K.M. 2018. Stepwise processing analyses of the single-turnover PCSK9 protease reveal its substrate sequence specificity and link clinical genotype to lipid phenotype. Journal of Biological Chemistry, 293(6): 1875-1886.

Deng, W., Wang, Y., Ma, L., Zhang, Y., Ullah, S. & Xue, Y. 2017. Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins. Briefings in Bioinformatics, 18(4): 647-658.

Ferrer-Costa, C., Gelpí, J.L., Zamakola, L., Parraga, I., De La Cruz, X. & Orozco, M. 2005. PMUT: a web-based tool for the annotation of pathological mutations on proteins. Bioinformatics, 21(14): 3176-3178.

Glaser, L. 1988. The biology and enzymology of eukaryotic protein acylation. Annual Review of Biochemistry, 57: 69-99.

Han, B.H., Sutin, D., Williamson, J.D., Davis, B.R., Piller, L.B., Pervin, H., Pressel, S.L. & Blaum, C.S. 2017. Effect of statin treatment vs usual care on primary cardiovascular prevention among older adults: the ALLHAT-LLT randomized clinical trial. JAMA Internal Medicine, 177(7): 955-965.

Hu, J., Li, Y., Zhang, Y. & Yu, D.J. 2018. ATPbind: accurate protein–ATP binding site prediction by combining sequence-profiling and structure-based comparisons. Journal of Chemical Information and Modeling, 58(2): 501-510.

Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., De Castro, E., Langendijk-Genevaux, P.S., ... & Sigrist, C.J. 2006. The PROSITE database. Nucleic Acids Research, 34(suppl_1): D227-D230.

Howe, K. L., Achuthan, P., Allen, J., Allen, J., Alvarez-Jarreta, J., Amode, M. R., ... & Flicek, P. 2021. Ensembl 2021. Nucleic Acids Research, 49(D1): D884-D891.

Parthiban, V., Gromiha, M.M. & Schomburg, D. 2006. CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Research, 34(suppl_2): W239-W242.

Pejaver, V., Hsu, W.L., Xin, F., Dunker, A.K., Uversky, V.N. & Radivojac, P. 2014. The structural and functional signatures of proteins that undergo multiple events of post-translational modification. Protein Science, 23(8): 1077-1093.

Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K.A., Lin, G.N., Nam, H.J., Mort, M., Cooper, D.N., Sebat, J., Iakoucheva, L.M. & Mooney, S.D. 2017. MutPred2: inferring the molecular and phenotypic impact of amino acid variants. BioRxiv, 134981.

Peloso, G.M., Lange, L.A., Varga, T.V., Nickerson, D.A., Smith, J.D., Griswold, M.E., Musani, S., Polfus, L.M., Mei, H., Gabriel, S. & Quarells, R.C. 2016. Association of exome sequences with cardiovascular traits among blacks in the Jackson Heart Study. Circulation: Cardiovascular Genetics, 9(4): 368-374.

Peng, J. & Xu, J. 2011. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins: Structure, Function, and Bioinformatics, 79(S10): 161-171.

Pires, D.E., Ascher, D.B. & Blundell, T.L. 2014. DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Research, 42(W1): W314-W319.

Protein Data Bank. 1971. Protein Data Bank. Nature New Biology, 233(223): 10-1038.

Stenson, P.D., Ball, E.V., Mort, M., Phillips, A.D., Shiel, J.A., Thomas, N.S., ... & Cooper, D.N. 2003. Human gene mutation database (HGMD®): 2003 update. Human Mutation, 21(6): 577-581.

Thomas, P.D., Campbell, M.J., Kejariwal, A., Mi, H., Karlak, B., Daverman, R., Diemer, K., Muruganujan, A. & Narechania, A. 2003. PANTHER: a library of protein families and subfamilies indexed by function. Genome Research, 13(9): 2129-2141.

Udenwobele, D.I., Su, R.C., Good, S.V., Ball, T.B., Varma Shrivastav, S. & Shrivastav, A. 2017. Myristoylation: an important protein modification in the immune response. Frontiers in Immunology, 8: 751.

Vaser, R., Adusumalli, S., Leng, S.N., Sikic, M. & Ng, P.C. 2016. SIFT missense predictions for genomes. Nature Protocols, 11(1): 1-12.

Wang, L. & Sauer, U. 2010. Prediction of protein stability changes due to single amino acid mutations. BMC Bioinformatics, 11: 190.

Weider, E., Susan-Resiga, D., Essalmani, R., Hamelin, J., Asselin, M.C., Nimesh, S., Ashraf, Y., Wycoff, K.L., Zhang, J., Prat, A. & Seidah, N.G. 2016. Proprotein convertase subtilisin/kexin type 9 (PCSK9) single domain antibodies are potent inhibitors of low density lipoprotein receptor degradation. Journal of Biological Chemistry, 291(32): 16659-16671.

Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., ... & Yaschenko, E. 2007. Database resources of the national center for biotechnology information. Nucleic Acids Research, 36(suppl_1): D13-D21.

Yang, J., Roy, A. & Zhang, Y. 2013. Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 29(20): 2588-2595.

Zhang, C., Freddolino, P.L. & Zhang, Y. 2017. COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information. Nucleic Acids Research, 45(W1): W291-W299.

Zhang, L., Deng, T., Pan, S., Zhang, M., Zhang, Y., Yang, C., ... & Mi, J. 2024. DeepO-GlcNAc: A web server for prediction of protein O-GlcNAcylation sites using deep learning combined with attention mechanism. Frontiers in Cell and Developmental Biology, 12: 1456728.

Zhang, Y., Ultsch, M., Skelton, N.J., Burdick, D.J., Beresini, M.H., Li, W., Kong-Beltran, M., Peterson, A., Quinn, J., Chiu, C. & Wu, Y. 2017. Discovery of a cryptic peptide-binding site on PCSK9 and design of antagonists. Nature Structural & Molecular Biology, 24(10): 848-856.

Zhou, F., Xue, Y., Yao, X. & Xu, Y. 2006. CSS-Palm: palmitoylation site prediction with a clustering and scoring strategy (CSS). Bioinformatics, 22(7): 894-896.