Identifikasi Struktur Protein Spike Varian Baru SARS-CoV-2 secara Bioinformatika dalam Pengembangan Kandidat Terapi COVID-19


  • Taufik Muhammad Fakih Program Studi Farmasi, Fakultas Matematika dan Ilmu Pengetahuna Alam, Universitas Islam Bandung, Indonesia
  • Dwi Syah Fitra Ramadhan Program Studi Farmasi, Poltekkes Kemenkes Makassar, Makassar, Indonesia, Indonesia
  • Aulia Fikri Hidayat Program Studi Farmasi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Islam Bandung, Bandung, Indonesia, Indonesia
  • Budi Prabowo Soewondo Program Studi Farmasi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Islam Bandung, Bandung, Indonesia, Indonesia



COVID-19 disease, Variants of Concern (VOC), SARS-CoV-2 spike protein, COVID-19 therapy, bioinformatics


Despite the relatively slow evolutionary rate of SARS-CoV-2 in comparison to other RNA viruses, the extensive and rapid transmission during the COVID-19 pandemic has led to the emergence of significant genetic diversity since the virus first infected the human population. This has resulted in various variants, such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), among others. Of particular concern are the Delta Variant and newly recognized Variants of Concern (VOCs), including lineages of B.1.617.2, as well as other VOCs discovered through local transmission, such as Epsilon (B.1.427/29-US) and B1.1.7/E484K-UK. The interactions between these variants and the spike protein of SARS-CoV-2, as well as the Angiotensin-converting enzyme 2 (ACE2), have become a primary focus in understanding the infection and spread of the SARS-CoV-2 virus. This research aims to comprehensively identify, evaluate, and explore the structural characteristics of the macromolecular spike protein of SARS-CoV-2 in the Beta, Gamma, and Delta variants using bioinformatics approaches. The methods employed in this study include homology modeling, molecular docking simulations, and molecular dynamics simulations. The research findings indicate that the spike protein of SARS-CoV-2 in the Gamma variant exhibits a strong affinity for ACE2. Therefore, this study is expected to serve as a reference for designing effective vaccine or antiviral candidates targeting various SARS-CoV-2 variants in the treatment of COVID-19 infections.


Heymann, D. L., Data sharing and outbreaks: best practice exemplified. The Lancet, (2020). doi:10.1016/S0140-6736(20)30184-7

Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, (2020). doi:10.1016/S0140-6736(20)30183-5

Mittal, A., Manjunath, K., Ranjan, R. K., Kaushik, S., Kumar, S. & Verma, V., COVID-19 pandemic: Insights into structure, function, and hACE2 receptor recognition by SARS-CoV-2. PLoS pathogens, 16(8): (2020).

Walls, A. C., Park, Y. J., Tortorici, M. A., Wall, A., McGuire, A. T. & Veesler, D., Erratum: Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein (Cell (2020) 181(2) (281–292.e6), (S0092867420302622), (10.1016/j.cell.2020.02.058)). Cell, 183(6): (2020).

Lazarevic, I., Pravica, V., Miljanovic, D. & Cupic, M., Immune evasion of sars‐cov‐2 emerging variants: What have we learnt so far? Viruses, 13(7): (2021).

Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., et al., A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med., 382(8): (2020).

Villoutreix, B. O., Calvez, V., Marcelin, A. G. & Khatib, A. M., In silico investigation of the new UK (B.1.1.7) and South African (501y.v2) SARS-CoV-2 variants with a focus at the ace2–spike rbd interface. Int. J. Mol. Sci., 22(4): (2021).

Wan, Y., Shang, J., Graham, R., Baric, R. S. & Li, F., Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on Decade-Long Structural Studies of SARS Coronavirus. J. Virol., 94(7): (2020).

Lin, X. & Chen, S., Major Concerns on the Identification of Bat Coronavirus Strain RaTG13 and Quality of Related Nature Paper. Preprints, (2020).

Lam, T. T. Y., Jia, N., Zhang, Y. W., Shum, M. H. H., Jiang, J. F., Zhu, H. C., Tong, Y. G., et al., Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature, 583(7815): (2020).

Chen, J., Wang, R., Wang, M. & Wei, G. W., Mutations Strengthened SARS-CoV-2 Infectivity. J. Mol. Biol., 432(19): (2020).

Lou, F., Li, M., Pang, Z., Jiang, L., Guan, L., Tian, L., Hu, J., et al., Understanding the Secret of SARS-CoV-2 Variants of Concern/Interest and Immune Escape. Frontiers in Immunology, 12: (2021).

Sanyaolu, A., Okorie, C., Marinkovic, A., Haider, N., Abbasi, A. F., Jaferi, U., Prakash, S., et al., The emerging SARS-CoV-2 variants of concern. Therapeutic Advances in Infectious Disease, 8: (2021).

Choi, J. Y. & Smith, D. M., SARS-CoV-2 variants of concern. Yonsei Medical Journal, 62(11): (2021).

Gheblawi, M., Wang, K., Viveiros, A., Nguyen, Q., Zhong, J.-C., Turner, A. J., Raizada, M. K., et al., Angiotensin-Converting Enzyme 2: SARS-CoV-2 Receptor and Regulator of the Renin-Angiotensin System. Circ. Res., 126(10): (2020).

Bian, J. & Li, Z., Angiotensin-converting enzyme 2 (ACE2): SARS-CoV-2 receptor and RAS modulator. Acta Pharmaceutica Sinica B, 11(1): (2021).

Kadam, S. B., Sukhramani, G. S., Bishnoi, P., Pable, A. A. & Barvkar, V. T., SARS-CoV-2, the pandemic coronavirus: Molecular and structural insights. Journal of Basic Microbiology, 61(3): (2021).

Rasheed, M. A., Raza, S., Zohaib, A., Riaz, M. I., Amin, A., Awais, M., Khan, S. U., et al., Immunoinformatics based prediction of recombinant multi-epitope vaccine for the control and prevention of SARS-CoV-2. Alexandria Eng. J., 60(3): (2021).

Lan, J., Ge, J., Yu, J., Shan, S., Zhou, H., Fan, S., Zhang, Q., et al., Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature, 581(7807): (2020).

Kemmish, H., Fasnacht, M. & Yan, L., Fully automated antibody structure prediction using BIOVIA tools: Validation study. PLoS One, 12(5): (2017).

Makarewicz, T. & Kaźmierkiewicz, R., Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J. Chem. Inf. Model., (2013). doi:10.1021/ci400071x

Aruleba, R. T., Adekiya, T. A., Oyinloye, B. E. & Kappo, A. P., Structural Studies of Predicted Ligand Binding Sites and Molecular Docking Analysis of Slc2a4 as a Therapeutic Target for the Treatment of Cancer. Int. J. Mol. Sci., (2018). doi:10.3390/ijms19020386

Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E. & Berendsen, H. J. C., GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, (2005). doi:10.1002/jcc.20291

Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B. & Lindah, E., Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, (2015). doi:10.1016/j.softx.2015.06.001

Aragones, J. L., Noya, E. G., Valeriani, C. & Vega, C., Free energy calculations for molecular solids using GROMACS. J. Chem. Phys., (2013). doi:10.1063/1.4812362

Kumari, R., Kumar, R., Consortium, O. S. D. D. & Lynn, A., g _ mmpbsa - A GROMACS tool for MM-PBSA and its optimization for high-throughput binding energy calculations. J. Chem. Inf. Model., (2014). doi:10.1021/ci500020m

Ren, J., Yuan, X., Li, J., Lin, S., Yang, B., Chen, C., Zhao, J., et al., Assessing the performance of the g_mmpbsa tools to simulate the inhibition of oseltamivir to influenza virus neuraminidase by molecular mechanics Poisson–Boltzmann surface area methods. J. Chinese Chem. Soc., (2020). doi:10.1002/jccs.201900148

Ramadhan, D. S. F., Fakih, T. M. & Arfan, A., Activity Prediction of Bioactive Compounds Contained in Etlingera elatior Against the SARS-CoV-2 Main Protease: An In Silico Approach. Borneo J. Pharm., 3(4): (2020).

Fitriyani F, F., Fakih, T. M. & Tjahjono, D. H., In Silico Studies of Green Tea Catechins Against HER-2 Receptor in Breast Cancer. Curr. Trends Biotechnol. Pharm., 14(5): (2020).

Darusman, F. & Fakih, T. M., Identification of the molecular mechanism of christinin compounds from Arabian bidara leaves (Ziziphus spina-christi L.) on microorganisms that cause female genital problems through computational approaches. Pharmaciana, 10(3): (2020).

Bell, E. W. & Zhang, Y., DockRMSD: An open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism. J. Cheminform., 11(1): (2019).

Liu, Z., Wickramasinghe, S. R. & Qian, X., Ion-specificity in protein binding and recovery for the responsive hydrophobic poly(vinylcaprolactam) ligand. RSC Adv., (2017). doi:10.1039/c7ra06022j

Pitaloka, D. A. E., Ramadhan, D. S. F., Arfan., Chaidir, L. & Fakih, T. M., Docking-based virtual screening and molecular dynamics simulations of quercetin analogs as enoyl-acyl carrier protein reductase (Inha) inhibitors of mycobacterium tuberculosis. Sci. Pharm., 89(2): (2021).

Serafeim, A. P., Salamanos, G., Patapati, K. K. & Glykos, N. M., Sensitivity of Folding Molecular Dynamics Simulations to even Minor Force Field Changes. J. Chem. Inf. Model., (2016). doi:10.1021/acs.jcim.6b00493

Li, L., Spranger, L., Soll, D., Beer, F., Brachs, M., Spranger, J. & Mai, K., Metabolic impact of weight loss induced reduction of adipose ACE-2 – Potential implication in COVID-19 infections? Metabolism., 113: (2020).

Rotondi, M., Coperchini, F., Ricci, G., Denegri, M., Croce, L., Ngnitejeu, S. T., Villani, L., et al., Detection of SARS-COV-2 receptor ACE-2 mRNA in thyroid cells: a clue for COVID-19-related subacute thyroiditis. J. Endocrinol. Invest., 44(5): (2021).

Kurniawan, F., Miura, Y., Kartasasmita, R. E., Mutalib, A., Yoshioka, N. & Tjahjono, D. H., In silico study, synthesis, and cytotoxic activities of porphyrin derivatives. Pharmaceuticals, 11(1): (2018).

Fakih, T. M., Kurniawan, F., Yusuf, M., Mudasir, M. & Tjahjono, D. H., Molecular dynamics of cobalt protoporphyrin antagonism of the cancer suppressor REV-ERBβ. Molecules, 26(11): (2021).




How to Cite

Fakih, T. M., Ramadhan, D. S. F., Hidayat, A. F., & Soewondo, B. P. (2023). Identifikasi Struktur Protein Spike Varian Baru SARS-CoV-2 secara Bioinformatika dalam Pengembangan Kandidat Terapi COVID-19. Jurnal Riset Kimia, 14(2), 145–157.




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