Computational drug design targeting MYH7 for hypertrophic cardiomyopathy integrating molecular docking, Density Functional Theory, and Molecular Dynamics Simulations
DOI:
https://doi.org/10.71336/jabs.1477Keywords:
Hypertrophic Cardiomyopathy, Left Ventricular Hypertrophy, Myocardial Ischemia, MYH7 Gene, MYBPC3 Gene, Pharmacophore ModelingAbstract
Hypertrophic cardiomyopathy (HCM) is a genetic condition of the heart that is commonly associated with mutations of sarcomeric proteins such as MYH7. Currently available therapies are largely palliative and do not offer cure-focused treatment. In this work, we utilize a computational approach combining molecular docking, Density Functional Theory (DFT), and Molecular Dynamics (MD) simulations to search for therapeutic compounds interacting with the MYH7 protein. Predicted models of MYH7 by Alpha Fold provided strong prediction reliability since 91.4% of residues were in the favored Ramachandran region. Out of multiple bioactive candidates screened, thymoquinone showed the most negative free energy of binding (-6.7 kcal/mol) to MYH7. Pharmacophore modeling along with ADMET analysis also validated its druglike nature and declared safety. Analyzing electronic properties showed that thymoquinone possesses a low HOMO-LUMO band gap (~5.67 eV), thus showcasing strong bioreactivity and stability. These results provide a direction for the intervention of MYH7 protein dysfunction. Such progress would aim at changing the treatments from palliative strategies to curative approach which is particularly essential to mitigate the risk of sudden cardiac death, especially in children. However further in vitro and in vivo studies are required to validate the safety and efficiency of the thymoquinone.
References
1. Maron, B.A., Wang R.-S., Carnethon M.R., Rowin E.J., Loscalzo J., et al. (2022). What causes hypertrophic cardiomyopathy? The American journal of cardiology 179: 74-82. https://doi.org/10.1016/j.amjcard.2022.06.017
2. Torbey, A.F.M., Couto R.G.T., Grippa A., Maia E.C., Miranda S.A., et al. (2024). Cardiomyopathy in children and adolescents in the era of precision medicine. Arquivos Brasileiros de Cardiologia 121: e20230154. https://doi.org/10.36660/abc.20230154
3. Ottaviani, A., Mansour D., Molinari L.V., Galanti K., Mantini C., et al. (2023). Revisiting diagnosis and treatment of hypertrophic cardiomyopathy: Current practice and novel perspectives. Journal of Clinical Medicine 12 (17): 5710. https://doi.org/10.3390/jcm12175710
4. Sethi, Y., Patel N., Kaka N., Kaiwan O., Kar J., et al. (2023). Precision medicine and the future of cardiovascular diseases: a clinically oriented comprehensive review. Journal of Clinical Medicine 12: 1799. https://doi.org/10.3390/jcm12051799
5. Lee, S., Roest A.S.V., Blair C.A., Kao K., Bremner S.B., et al. (2023). Multi-scale models reveal hypertrophic cardiomyopathy myh7 g256e mutation drives hypercontractility and elevated mitochondrial respiration. bioRxiv 2023: 2023-06. https://doi.org/10.1101/2023.06.08.544276
6. Shi, P., Yang A., Zhao Q., Chen Z., Ren X. and Dai Q.J.F.i.p. (2021). A hypothesis of gender differences in self-reporting symptom of depression: Implications to solve under-diagnosis and under-treatment of depression in males. Frontiers in Psychiatry 12: 589687. doi: 10.3389/fpsyt.2021.589687
7. Ciarambino, T., Menna G., Sansone G. and Giordano M.J.I.j.o.m.s. (2021). Cardiomyopathies: An overview. International Journal of Molecular Sciences 22(14): 7722. https://doi.org/10.3390/ijms22147722
8. Zytnick, D., Heard D., Ahmad F., Cresci S., Owens A.T., et al. (2021). Exploring experiences of hypertrophic cardiomyopathy diagnosis, treatment, and impacts on quality of life among middle-aged and older adults: An interview study. Heart & Lung 50(6): 788–793. https://doi.org/10.1016/j.hrtlng.2021.06.004
9. Sivalokanathan, S.J.D. (2022). The role of cardiovascular magnetic resonance imaging in the evaluation of hypertrophic cardiomyopathy. Diagnostics 12(2): 314. https://doi.org/10.3390/diagnostics12020314
10. McInnes, G., Sharo A.G., Koleske M.L., Brown J.E., Norstad M., et al. (2021). Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. The American Journal of Human Genetics 108(4): 535–548. https://doi.org/10.1016/j.ajhg.2021.03.003
11. McKenna, W.J. and Judge D.P.J.N.R.C. (2021). Epidemiology of the inherited cardiomyopathies. Nature Reviews Cardiology 18(1): 22–36. https://doi.org/10.1038/s41569-020-0428-2
12. Sarohi, V., Srivastava S., Basak T.J.J.o.C.D. and Disease. (2022). A comprehensive outlook on dilated cardiomyopathy (dcm): State-of-the-art developments with special emphasis on omics-based approaches. Journal of Cardiovascular Development and Disease 9(6): 174. https://doi.org/10.3390/jcdd9060174
13. Maron, B.J., Desai M.Y., Nishimura R.A., Spirito P., Rakowski H., et al. (2022). Management of hypertrophic cardiomyopathy: Jacc state-of-the-art review. Journal of the American College of Cardiology 79(4): 390–414. 7l. https://doi.org/10.1016/j.jacc.2021.11.021
14. Ahluwalia, M. and Ho C.Y.J.H. (2021). Cardiovascular genetics: The role of genetic testing in diagnosis and management of patients with hypertrophic cardiomyopathy. Heart 107(3): 183–189. https://doi.org/10.1136/heartjnl-2020-316798
15. Melas, M., Beltsios E.T., Adamou A., Koumarelas K. and McBride K.L.J.J.o.c.m. (2022). Molecular diagnosis of hypertrophic cardiomyopathy (hcm): In the heart of cardiac disease. Journal of Clinical Medicine 12(1): 225. https://doi.org/10.3390/jcm12010225
16. Abbas, M.T., Baba Ali N., Farina J.M., Mahmoud A.K., Pereyra M., et al. (2024). Role of genetics in diagnosis and management of hypertrophic cardiomyopathy: A glimpse into the future. Biomedicines 12(3): 682. https://doi.org/10.3390/biomedicines12030682
17. Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G., Li, X., Wu, J. C., Yang, S. (2025): Artificial intelligence in drug development. Nature Medicine 31(1): 45–59. https://doi.org/10.1038/s41591-024-03434-4
18. Zhao, X., Yang V.B., Menta A.K., Blum J., Wahida A. and Subbiah V.J.A.i.P.O. (2024). Alphafold’s predictive revolution in precision oncology. AI in Precision Oncology 1(3): 160–167. https://doi.org/10.1089/aipo.2024.0010
19. O’Leary, N. A., Cox, E., Holmes, J. B., Anderson, W. R., Falk, R., Hem, V., Tsuchiya, M. T., Schuler, G. D., Zhang, X., Torcivia, J., Ketter, A. (2024): Exploring and retrieving sequence and metadata for species across the tree of life with NCBI Datasets. Scientific Data 11(1): 732. https://doi.org/10.1038/s41597-024-03571-y
20. Faizan, R., Naveed, M., Estevez, I. B., Hanif, N., Arshad, A., Aziz, T., Alamri, A. S., Alsanie, W. F., Alhomrani, M. (2025): Computational exploration of natural inhibitors against toxin-associated proteins in Naegleria fowleri Karachi strain. Pathology – Research and Practice 2025: 156184 https://doi.org/10.1016/j.prp.2025.156184.
21. Terwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., Adams, P. D. (2024): AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nature Methods 21(1): 110–116. https://doi.org/10.1038/s41592-023-02087-4
22. Naveed, M., Ali, N., Aziz, T., Hanif, N., Fatima, M., Ali, I., Alharbi, M., Alasmari, A. F., Albekairi, T. H. (2024): The natural breakthrough: phytochemicals as potent therapeutic agents against spinocerebellar ataxia type 3. Scientific Reports 14(1): 1529. https://doi.org/10.1038/s41598-024-51954-3
23. Naveed, M., Hussain, M., Aziz, T., Hanif, N., Kanwal, N., Arshad, A., Khan, A. A., Alshammari, A., Alharbi, M. (2024): Computational biology assisted exploration of phytochemicals derived natural inhibitors to block BZLF1 gene activation of Epstein–Barr virus in host. Scientific Reports 14(1): 31664. https://doi.org/10.1038/s41598-024-81037-2
24. 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
25. Aghahasani, R., Shiri, F., Kamaladiny, H., Haddadi, F., Pirhadi, S. (2024): Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery. BMC Chemistry 18(1): 73. https://doi.org/10.1186/s13065-024-01175-6
26. Wang, Y., Shao, X., Wang, P. (2025): Discovery of novel potential small-molecule inhibitors of MMP-9 based on a pharmacophore virtual screening strategy. Results in Chemistry 2025: 102293. https://doi.org/10.1016/j.rechem.2025.102293
27. Afridi, M. B., Sardar, H., Serdaroğlu, G., Shah, S. W., Alsharif, K. F., Khan, H. (2024): SwissADME studies and density functional theory (DFT) approaches of methyl substituted curcumin derivatives. Computational Biology and Chemistry 112: 108153. https://doi.org/10.1016/j.compbiolchem.2024.108153
28. Abo-Alwafa, E., Faraj, A. (2024): Vibrational normal modes investigation of 4-methyl triazole [4,5-c] pyridine using density function theory (DFT)-chemical quantum calculation: computer simulation program. Sebha University Conference Proceedings 3(1): 234–239. https://doi.org/10.51984/sucp.v3i1.3723
29. Islam, M. A., Hossain, N., Ahsan, Z., Rana, M., Rahman, M., Abdullah, M. (2025): DFT insights into the mechanical properties of NMs. Results in Surfaces and Interfaces 2025: 100417. https://doi.org/10.1016/j.rsurfi.2025.100417
30. Naveed, M., Din, M. S., Aziz, T., Rehman, H. M., Naveed, R., Hanif, N., Waseem, M., Naz, S., Alasmari, A. F., Alharbi, M., Albekairi, T. H. (2025): Molecular characterization and investigating the potential of Georgenia satyanarayanai as an effective agent in pesticide biodegradation pathways. Molecular Biotechnology 2025: 1–6. https://doi.org/10.1007/s12033-025-01463-z
31. Cui, M., Ji, X., Guan, F., Su, G., Du, L. (2024): Design of a Helicobacter pylori multi-epitope vaccine based on immunoinformatics. Frontiers in Immunology 15: 1432968. https://doi.org/10.3389/fimmu.2024.1432968
32. Fathollahi, M., Motamedi H., Hossainpour H., Abiri R., Shahlaei M., et al. (2024). Designing a novel multi-epitopes pan-vaccine against sars-cov-2 and seasonal influenza: In silico and immunoinformatics approach. Journal of Biomolecular Structure and Dynamics 42(20): 10761–10784. https://doi.org/10.1080/07391102.2023.2258420
33. Marian, A. J. (2021): Molecular genetic basis of hypertrophic cardiomyopathy. Circulation Research 128(10): 1533–1553. https://doi.org/10.1161/CIRCRESAHA.121.318346
34. Souidi, A., Nakamori M., Zmojdzian M., Jagla T., Renaud Y. and Jagla K.J.E.r. (2023). Deregulations of mir‐1 and its target multiplexin promote dilated cardiomyopathy associated with myotonic dystrophy type 1. EMBO Reports 24(4): e56616. https://doi.org/10.15252/embr.202256616
35. Sun, B. and Kekenes-Huskey P.M.J.Q.r.o.b. (2023). Myofilament-associated proteins with intrinsic disorder (mapids) and their resolution by computational modeling. Quarterly Reviews of Biophysics 56: e2. https://doi.org/10.1017/S003358352300001X
36. Naveed, M., Abid, A., Aziz, T., Saleem, A., Hanif, N., Ali, I., Alasmari, A. F. (2024): Comparative toxicity assessment of fisetin-aided artificial intelligence-assisted drug design targeting epibulbar dermoid through phytochemicals. Open Chemistry 22(1): 20230197. https://doi.org/10.1515/chem-2023-0197

Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Applied Biological Sciences

This work is licensed under a Creative Commons Attribution 4.0 International License.