In silico analysis of LDLR isoform gene for the identification of natural inhibitors against familial hypercholesterolemia
DOI:
https://doi.org/10.71336/jabs.1501Keywords:
Familial Hypercholesterolemia, , Low density lipoprotein, Guggulsterone, Artificial intelligence, ToxicityAbstract
Familial hypercholesterolemia (FH) is a genetic disorder characterized by elevated low-density lipoprotein cholesterol (LDL-C), increasing the risk of premature cardiovascular disease. Loss-of-function mutations in the LDL receptor (LDLR) gene are the primary cause, reducing receptor-mediated LDL clearance. Current therapies, including statins, ezetimibe, and PCSK9 inhibitors, aim to enhance LDLR function but may have side effects or limited efficacy, highlighting the need for alternative strategies. This study employed an in silico approach to identify natural phytochemicals capable of modulating LDLR. Five compounds guggulsterone, curcumin, β-sitosterol, oleuropein, and resveratrol were evaluated for binding affinity, pharmacokinetics, and toxicity. LDLR secondary and tertiary structures were predicted using GOR IV, PSIPRED, and AlphaFold, with binding sites identified via DeepSite. Molecular docking using PyRx and CB-Dock2 showed guggulsterone as the top candidate, exhibiting a binding affinity of -9.4 kcal/mol and stable hydrogen bond and hydrophobic interactions with key residues. ADMET analysis indicated high gastrointestinal absorption, Lipinski rule compliance, and minimal organ-specific toxicity, although blood-brain barrier permeability was predicted. These findings suggest that guggulsterone may stabilize LDLR or influence receptor regulation, providing a potential natural therapeutic approach for FH. Further in vitro and in vivo studies are warranted to confirm efficacy and elucidate mechanisms.
References
Benito-Vicente, A., Uribe, K. B., Jebari, S., Galicia-Garcia, U., Ostolaza, H., Martin, C. (2018). Familial hypercholesterolemia: the most frequent cholesterol metabolism disorder caused disease. International Journal of Molecular Sciences 19:3426. https://doi.org/10.3390/ijms19113426 DOI: https://doi.org/10.3390/ijms19113426
Foody, J. M., Vishwanath, R. (2016). Familial hypercholesterolemia/autosomal dominant hypercholesterolemia: molecular defects, the LDL-C continuum, and gradients of phenotypic severity. Journal of Clinical Lipidology 10:970–986. https://doi.org/10.1016/j.jacl.2016.04.009 DOI: https://doi.org/10.1016/j.jacl.2016.04.009
Di Taranto, M. D., Fortunato, G. (2023). Genetic heterogeneity of familial hypercholesterolemia: repercussions for molecular diagnosis. International Journal of Molecular Sciences 24:3224. https://doi.org/10.3390/ijms24043224 DOI: https://doi.org/10.3390/ijms24043224
Rogozik, J., Główczyńska, R., Grabowski, M. (2024). Genetic backgrounds and diagnosis of familial hypercholesterolemia. Clinical Genetics 105:3–12. https://doi.org/10.1111/cge.14435 DOI: https://doi.org/10.1111/cge.14435
Suryawanshi, Y. N., Warbhe, R. A. (2023). Familial hypercholesterolemia: a literature review of the pathophysiology and current and novel treatments. Cereus. https://doi.org/10.7759/cureus.49121 DOI: https://doi.org/10.7759/cureus.49121
Hanif, N., Arif, A. A. A., Ali, S. A. S., Anees, M. A. M., Anees, M. A. M., Arshad, A., Asim, M. (2025). Computational drug design targeting MYH7 for hypertrophic cardiomyopathy integrating molecular docking, Density Functional Theory, and Molecular Dynamics Simulations. Journal of Applied Biological Sciences 19:179–192. https://doi.org/10.71336/jabs.1477 DOI: https://doi.org/10.71336/jabs.1477
Naveed, M., Ali, N., Aziz, T., Hanif, N., Fatima, M., Ali, I., Albekairi, T. H. (2024). The natural breakthrough: phytochemicals as potent therapeutic agents against spinocerebellar ataxia type 3. Scientific Reports 14:1529. https://doi.org/10.1038/s41598-024-51954-3 DOI: https://doi.org/10.1038/s41598-024-51954-3
Naveed, M., Hussain, M., Aziz, T., Hanif, N., Kanwal, N., Arshad, A., Alharbi, M. (2024). Computational biology assisted exploration of phytochemicals derived natural inhibitors to block BZLF1 gene activation of Epstein–Bar virus in host. Scientific Reports 14:31664. https://doi.org/10.1038/s41598-024-81037-2 DOI: https://doi.org/10.1038/s41598-024-81037-2
Hussain, M., Kanwal, N., Jahangir, A., Ali, N., Hanif, N., Ullah, O. (2024). Computational modeling of cyclotides as antimicrobial agents against Neisseria gonorrhoeae PorB porin protein: integration of docking, immune, and molecular dynamics simulations. Frontiers in Chemistry 12:1493165. https://doi.org/10.3389/fchem.2024.1493165 DOI: https://doi.org/10.3389/fchem.2024.1493165
Crosara, K. T. B., Moffa, E. B., Xiao, Y., Siqueira, W. L. (2018). Merging in-silico and in vitro salivary protein complex partners using the STRING database: a tutorial. Journal of Proteomics 171:87–94. https://doi.org/10.1016/j.jprot.2017.08.002 DOI: https://doi.org/10.1016/j.jprot.2017.08.002
Zhang, Y., Qiao, S., Ji, S., Li, Y. (2020). DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding. International Journal of Machine Learning and Cybernetics 11:841–851. https://doi.org/10.1007/s13042-019-00990-x DOI: https://doi.org/10.1007/s13042-019-00990-x
Naveed, M., Hanif, N., Aziz, T., Waseem, M., Alharbi, M., Alshammari, A., Alasmari, A. F. (2025). Insight into molecular and mutational scrutiny of epilepsy associated gene Gabrg2 leading to novel computer-aided drug designing. Scientific Reports 15:6770. https://doi.org/10.1038/s41598-025-91505-y DOI: https://doi.org/10.1038/s41598-025-91505-y
Faizan, R., Naveed, M., Estevez, I. B., Hanif, N., Arshad, A., Aziz, T., Alhomrani, M. (2025). Computational exploration of natural inhibitors against toxin-associated proteins in Naegleria fowleri Karachi strain. Pathology-Research and Practice 221:156184. https://doi.org/10.1016/j.prp.2025.156184 DOI: https://doi.org/10.1016/j.prp.2025.156184
Liu, Y., Cao, Y. (2023). Protein–Ligand Blind Docking Using CB-Dock2. In: Computational Drug Discovery and Design. New York, NY: Springer US, pp. 113–125. https://doi.org/10.1007/978-1-0716-3441-7_6 DOI: https://doi.org/10.1007/978-1-0716-3441-7_6
Temml, V., Kaserer, T., Kutil, Z., Landa, P., Vanek, T., Schuster, D. (2014). Pharmacophore modeling for COX-1 and -2 inhibitors with LigandScout in comparison to Discovery Studio. Future Medicinal Chemistry 6:1869–1881. https://doi.org/10.4155/fmc.14.114 DOI: https://doi.org/10.4155/fmc.14.114
Rauf, A., Khan, H., Khan, M., Abusharha, A., Serdaroğlu, G., Daglia, M. (2023). In silico, SwissADME, and DFT studies of newly synthesized oxindole derivatives followed by antioxidant studies. Journal of Chemistry 2023:5553913. https://doi.org/10.1088/1755-1315/890/1/012021 DOI: https://doi.org/10.1155/2023/5553913
Banerjee, P., Ulker, O. C. (2022). Combinative ex vivo studies and in silico models ProTox-II for investigating the toxicity of chemicals used mainly in cosmetic products. Toxicology Mechanisms and Methods 32:542–548. https://doi.org/10.1080/15376516.2022.2053623 DOI: https://doi.org/10.1080/15376516.2022.2053623
McGowan, M. P., Hosseini Dehkordi, S. H., Moriarty, P. M., Duell, P. B. (2019). Diagnosis and treatment of heterozygous familial hypercholesterolemia. Journal of the American Heart Association 8:e013225. https://doi.org/10.1161/JAHA.119.01322 DOI: https://doi.org/10.1161/JAHA.119.013225
Vuorio, A., Watts, G. F., Schneider, W. J., Tsimikas, S., Kovanen, P. T. (2020). Familial hypercholesterolemia and elevated lipoprotein (a): double heritable risk and new therapeutic opportunities. Journal of Internal Medicine 287:2–18. https://doi.org/10.1111/joim.12981 DOI: https://doi.org/10.1111/joim.12981
Reiner, Ž. (2015). Management of patients with familial hypercholesterolaemia. Nature Reviews Cardiology 12:565–575. https://doi.org/10.1038/nrcardio.2015.92 DOI: https://doi.org/10.1038/nrcardio.2015.92
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of Applied Biological Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
