SpringerLink
Log in

Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA

  • Original Paper
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H–Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (−6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (−9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (−9.8 kcal/mol/RMSD of 1.46 Å) and olysio (−9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Scheme 1
Scheme 2
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

Not applicable

References

  1. Guo, Y. R., Cao, Q. D., Hong, Z. S., Tan, Y. Y., Chen, S. D., **, H. J., Sen, T. K., Wang, D. Y., & Yan, Y. (2020). The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak- An update on the status. Military Medical Research, 7, 1–10. https://doi.org/10.1186/s40779-020-00240-0

    Article  CAS  Google Scholar 

  2. Martins-Filho, Ricardo P., Gois-Santos, Tavares V., Tavares, C. S. S., de Melo, E. G. M., Nascimento-Júnior, E. M., & do, Santos VS,. (2020). Recommendations for a safety dental care management during SARS-CoV-2 pandemic. Revista Panamericana de Salud Pública, 44, 1–3. https://doi.org/10.26633/rpsp.2020.51

    Article  Google Scholar 

  3. Fehr, A. R., & Perlman, S. (2015). Coronaviruses: An overview of their replication and pathogenesis. Coronaviruses: Methods and protocols (1st ed., pp. 1–23). Springer.

    Google Scholar 

  4. Rothan, H. A., & Byrareddy, S. N. (2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of Autoimmunity, 109, 1–4. https://doi.org/10.1016/j.jaut.2020.102433

    Article  CAS  Google Scholar 

  5. Bogoch, I. I., Watts, A., Thomas-Bachli, A., Huber, C., Kraemer, M. U. G., & Khan, K. (2020). Pneumonia of unknown aetiology in Wuhan, China: Potential for international spread via commercial air travel. Journal of Travel Medicine, 27, 1–3. https://doi.org/10.1093/jtm/taaa008

    Article  Google Scholar 

  6. Lu, H., Stratton, C. W., & Tang, Y. W. (2020). Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. Journal of Medical Virology, 92, 401–402. https://doi.org/10.1002/jmv.25678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhou, T., Liu, Q., Yang, Z., Liao, J., Yang, K., Bai, W., Lu, X., & Zhang, W. (2020). Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV. Journal of Evidence-Based Medicine, 13, 3–7. https://doi.org/10.1111/jebm.12376

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wu, D., Wu, T., Liu, Q., & Yang, Z. (2020). The SARS-CoV-2 outbreak: What we know. International Journal of Infectious Diseases, 94, 44–48. https://doi.org/10.1016/j.ijid.2020.03.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zumla, A., Hui, D. S., Azhar, E. I., Memish, Z. A., & Maeurer, M. (2020). Reducing mortality from 2019-nCoV: Host-directed therapies should be an option. The Lancet, 395, 22–28. https://doi.org/10.1016/S0140-6736(20)30305-6

    Article  Google Scholar 

  10. Wang, M., Cao, R., Zhang, L., Yang, X., Liu, J., Xu, M., Shi, Z., Hu, Z., Zhong, W., & **ao, G. (2020). Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Research, 30, 269–271. https://doi.org/10.1038/s41422-020-0282-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Falavigna, M., Colpani, V., Stein, C., Azevedo, L. C., Bagattini, A. M., & Brito, G. V. (2020). Diretrizes para o tratamento farmacológico da COVID-19. Revista Brasileira de Terapia Intensiva, 32, 166–196.

    PubMed  PubMed Central  Google Scholar 

  12. Mahdi, M. A., Yousefi, S. R., Jasim, L. S., & Salavati-Niasari, M. (2022). Green synthesis of DyBa2Fe3O7.988/DyFeO3 nanocomposites using almond extract with dual eco-friendly applications: Photocatalytic and antibacterial activities. International Journal of Hydrogen Energy, 47, 14319–14330. https://doi.org/10.1016/j.ijhydene.2022.02.175

    Article  CAS  Google Scholar 

  13. Yousefi, S. R., Ghanbari, M., Amiri, O., Marzhoseyni, Z., Mehdizadeh, P., Hajizadeh-Oghaz, M., & Salavati-Niasari, M. (2021). Dy2BaCuO5/Ba4DyCu3O9.09 S-scheme heterojunction nanocomposite with enhanced photocatalytic and antibacterial activities. Journal of the American Ceramic Society, 104, 2952–2965. https://doi.org/10.1111/jace.17696

    Article  CAS  Google Scholar 

  14. Yousefi, S. R., Sobhani, A., Alshamsi, H. A., & Salavati-Niasari, M. (2021). Green sonochemical synthesis of BaDy2NiO5/Dy2O3 and BaDy2NiO5/NiO nanocomposites in the presence of core almond as a cap** agent and their application as photocatalysts for the removal of organic dyes in water. RSC Advances, 11, 11500–11512. https://doi.org/10.1039/d0ra10288a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ledford, H. (2020). Dozens of coronavirus drugs are in development: What happens next? Nature, 581, 247–248. https://doi.org/10.1038/d41586-020-01367-9

    Article  CAS  PubMed  Google Scholar 

  16. Menezes, C. R., Sanches, C., & Chequer, F. M. D. (2020). Efetividade e toxicidade da cloroquina e da hidroxicloroquina associada (ou não) à azitromicina para tratamento da COVID-19. O que sabemos até o momento? Journal of Health & Biological Sciences, 8, 1–9. https://doi.org/10.12662/2317-3076jhbs.v8i1.3206.p1-9.2020

    Article  Google Scholar 

  17. Dourado, P., Porto, M., Dal Pizzol, T., Ramos, L., Serrate, S., Luiza, V., Leão, N., Rocha, M., Oliveira, M., Dâmaso, A., Da, T., Dal, S., Ii, P., Ramos, R., Serrate, S., Iv, M., Luiza, V. L., Urruth, N., Tavares, L., … Viii, B. (2016). Prevalence of self-medication in Brazil and associated factors. Revista de Saude Publica, 50, 1s–11s. https://doi.org/10.1590/S1518-8787.2016050006117

    Article  Google Scholar 

  18. de Correia, B., & C, Trindade JK, Almeida AB,. (2019). Fatores Correlacionados À Automedicação Entre Os Jovens E Adultos- Uma Revisão Integrativa Da Literatura. Rev Iniciação Científica e Extensão, 2, 57–61.

    Google Scholar 

  19. Hahn, K. L. (2011). Old drugs are new again. Pharmacy Times, 77, 159–166.

    Google Scholar 

  20. Aronson, J. K. (2007). Old drugs: New uses. British Journal of Clinical Pharmacology, 64, 563–565. https://doi.org/10.1111/j.1365-2125.2007.03058.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chong, C. R., & Sullivan, D. J. (2007). New uses for old drugs. Nature, 448, 645–646. https://doi.org/10.1038/448645a

    Article  CAS  PubMed  Google Scholar 

  22. Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. (2014). Computational methods in drug discovery. Pharmacological Reviews, 66, 334–395.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Aparoy, P., Kumar Reddy, K., & Reddanna, P. (2012). Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Current Medicinal Chemistry, 19, 3763–3778. https://doi.org/10.2174/092986712801661112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Guido, R. V. C., & Andricopulo, A. D. (2008). Modelagem Molecular de Fármacos. Revista Processos Químicos, 2, 24–36. https://doi.org/10.19142/rpq.v2i4.66

    Article  Google Scholar 

  25. Coutinho, J. P. (2013). Busca De Novos Fármacos Para O Tratamento Da Malária Humana Através De Diferentes Abordagens. Journal of Chemical Information and Modeling. https://doi.org/10.1017/CBO9781107415324.004

    Article  Google Scholar 

  26. Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31, 455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bai, Q., Tan, S., Xu, T., Liu, H., Huang, J., & Yao, X. (2021). MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Briefings in Bioinformatics, 22, 1–12. https://doi.org/10.1093/bib/bbaa161

    Article  CAS  Google Scholar 

  28. Vieira, T. F., & Sousa, S. F. (2019). Comparing AutoDock and Vina in ligand/decoy discrimination for virtual screening. Applied Sciences, 9, 1–18. https://doi.org/10.3390/app9214538

    Article  CAS  Google Scholar 

  29. Foundation TAS. (2012). Apache Cordova

  30. Dias, L. C., Dessoy, M. A., Guido, R. V. C., Oliva, G., & Andricopulo, A. D. (2013). Doenças tropicais negligenciadas: Uma nova era de desafios e oportunidades. Quimica Nova, 36, 1552–1556. https://doi.org/10.1590/S0100-40422013001000011

    Article  CAS  Google Scholar 

  31. Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., **a, J., Yu, T., Zhang, X., & Zhang, L. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet, 395, 507–513. https://doi.org/10.1016/S0140-6736(20)30211-7

    Article  CAS  Google Scholar 

  32. Cui, J., Li, F., & Shi, Z. L. (2019). Origin and evolution of pathogenic coronaviruses. Nature Reviews Microbiology, 17, 181–192. https://doi.org/10.1038/s41579-018-0118-9

    Article  CAS  PubMed  Google Scholar 

  33. **, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., … Yang, H. (2020). Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature, 582, 289–293. https://doi.org/10.1038/s41586-020-2223-y

    Article  CAS  PubMed  Google Scholar 

  34. Ferreira, J. C., Fadl, S., Villanueva, A. J., & Rabeh, W. M. (2021). Catalytic Dyad residues His41 and Cys145 impact the catalytic activity and overall conformational fold of the main SARS-CoV-2 Protease 3-chymotrypsin-like protease. Frontiers in Chemistry, 9, 1–11. https://doi.org/10.3389/fchem.2021.692168

    Article  CAS  Google Scholar 

  35. de Almeida, J. O., de Oliveira, V. R. T., Avelar, J. L. D. S., Moita, B. S., & Lima, L. M. (2020). COVID-19: Physiopathology and targets for therapeutic intervention [COVID-19: Fisiopatologia e Alvos para Intervenção Terapêutica]. Revista Virtual De Quimica, 12, 1464–1497.

    Article  Google Scholar 

  36. Rester, U. (2008). From virtuality to reality—virtual screening in lead discovery and lead optimization: A medicinal chemistry perspective. Current Opinion in Drug Discovery & development, 11, 559–568.

    CAS  Google Scholar 

  37. Ashutosh, T., & Vytas, A. B. (2018). Molecular docking: From lock and key to combination lock. Journal of Molecular Medicine and Clinical Applications, 2, 1–9. https://doi.org/10.16966/2575-0305.106

    Article  Google Scholar 

  38. Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the rule of 5 and drugability. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2016.05.007

    Article  PubMed  PubMed Central  Google Scholar 

  39. Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., Hohmann, E., Chu, H. Y., Luetkemeyer, A., Kline, S., Lopez de Castilla, D., Finberg, R. W., Dierberg, K., Tapson, V., Hsieh, L., Patterson, T. F., Paredes, R., Sweeney, D. A., Short, W. R., … Lane, H. C. (2020). Remdesivir for the treatment of Covid-19—preliminary report. New England Journal of Medicine, 1, 1–12. https://doi.org/10.1056/nejmoa2007764

    Article  CAS  Google Scholar 

  40. de Ruyck, J., Brysbaert, G., Blossey, R., & Lensink, M. F. (2016). Molecular docking as a popular tool in drug design, an in silico travel. Advances and Applications in Bioinformatics and Chemistry, 9, 1–11. https://doi.org/10.2147/AABC.S105289

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ruba, S., Arooj, M., & Naz, D. G. (2014). In silico molecular docking studies and design of dengue virus inhibitors. Journal Of Pharmacy and Biologycal Science, 9, 15–23. https://doi.org/10.9790/3008-09211523

    Article  Google Scholar 

  42. Huang, H. J., Yu, H. W., Chen, C. Y., Hsu, C. H., Chen, H. Y., Lee, K. J., Tsai, F. J., & Chen, C. Y. C. (2010). Current developments of computer-aided drug design. Journal of the Taiwan Institute of Chemical Engineers, 41, 623–635. https://doi.org/10.1016/j.jtice.2010.03.017

    Article  CAS  Google Scholar 

  43. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30, 2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yan, J., Zhang, G., Pan, J., & Wang, Y. (2014). α-Glucosidase inhibition by luteolin: Kinetics, interaction and molecular docking. International Journal of Biological Macromolecules, 64, 213–223. https://doi.org/10.1016/j.ijbiomac.2013.12.007

    Article  CAS  PubMed  Google Scholar 

  45. Kumar, Y., Singhc, H., & Patel, C. N. (2020). In silico prediction of potential inhibitors for the main protease of SARS-CoV-2 using molecular. Journal of Infection and Public Health, 13, 1–14. https://doi.org/10.1016/j.jiph.2020.06.016

    Article  CAS  Google Scholar 

  46. Biovia. (2015). Dassault systemes BIOVIA, discovery studio modelling environment, release 4.5. Accelrys Softw. Inc.

  47. Marinho, E. M., de Andrade, Batista, Neto, J., Silva, J., Rocha da Silva, C., Cavalcanti, B. C., Marinho, E. S., & Nobre Júnior, H. V. (2020). Virtual screening based on molecular docking of possible inhibitors of Covid-19 main protease. Microbial Pathogenesis, 148, 104365. https://doi.org/10.1016/j.micpath.2020.104365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Shityakov, S., & Förster, C. (2014). In silico predictive model to determine vector-mediated transport properties for the blood-brain barrier choline transporter. Advances and Applications in Bioinformatics and Chemistry. https://doi.org/10.2147/AABC.S63749

    Article  PubMed  PubMed Central  Google Scholar 

  49. Imberty, A., Hardman, K. D., Carver, J. P., & Perez, S. (1991). Molecular modelling of protein-carbohydrate interactions. Docking of monosaccharides in the binding site of concanavalin A. Glycobiology, 1, 631–642. https://doi.org/10.1093/glycob/1.6.631

    Article  CAS  PubMed  Google Scholar 

  50. Schrödinger, L. (2010). Maestro, version 9.1. New York, NY

  51. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79, 926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  52. Ryckaert, J. P., Ciccotti, G., & Berendsen, H. J. C. (1977). Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Computational Physics, 23, 327–341. https://doi.org/10.1016/0021-9991(77)90098-5

    Article  CAS  Google Scholar 

  53. Cheatham, T. E., Miller, J. L., Fox, T., Darden, T. A., & Kollman, P. A. (1995). Molecular dynamics simulations on solvated biomolecular systems: The particle mesh Ewald method leads to stable trajectories of DNA, RNA, and proteins. Journal of the American Chemical Society, 117, 4193–4194.

    Article  CAS  Google Scholar 

  54. Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997). LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18, 1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12%3c1463::AID-JCC4%3e3.0.CO;2-H

    Article  CAS  Google Scholar 

  55. Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26, 1781–1802. https://doi.org/10.1002/jcc.20289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Turner, P. (2005). XMGRACE, Version 5.1. 19. Center for Coastal and Land-Margin Research, Oregon Graduate Institute of Science and Technology, Beaverton

  57. Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14, 33–38. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  CAS  PubMed  Google Scholar 

  58. Diez, M., Petuya, V., Martínez-Cruz, L. A., & Hernández, A. (2014). Insights into mechanism kinematics for protein motion simulation. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-15-184

    Article  PubMed  PubMed Central  Google Scholar 

  59. Arshia, A. H., Shadravan, S., Solhjoo, A., Sakhteman, A., & Sami, A. (2021). De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2021.104967

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gohlke, H., Kiel, C., & Case, D. A. (2003). Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. Journal of Molecular Biology, 330, 891–913. https://doi.org/10.1016/S0022-2836(03)00610-7

    Article  CAS  PubMed  Google Scholar 

  61. DasGupta, D., Mandalaparthy, V., & Jayaram, B. (2017). A component analysis of the free energies of folding of 35 proteins: A consensus view on the thermodynamics of folding at the molecular level. Journal of Computational Chemistry, 38, 2791–2801. https://doi.org/10.1002/jcc.25072

    Article  CAS  PubMed  Google Scholar 

  62. Genheden, S., & Ryde, U. L. F. (2010). How to obtain statistically converged MM/GBSA results. Journal of Computational Chemistry, 31, 837–846. https://doi.org/10.1002/jcc.21366

    Article  CAS  PubMed  Google Scholar 

  63. Fan, J., Fu, A., & Zhang, L. (2019). Progress in molecular docking. Quantitative Biology, 7, 83–89. https://doi.org/10.1007/s40484-019-0172-y

    Article  CAS  Google Scholar 

  64. Lee, J., Lee, Y., Jung, Y. M., Park, J. H., Yoo, H. S., & Park, J. (2022). Discovery of E3 ligase ligands for target protein degradation. Molecules, 27, 1–23. https://doi.org/10.3390/molecules27196515

    Article  CAS  Google Scholar 

  65. Pettersen, E. F., Goddard, T. D., Huang, C. C., Meng, E. C., Couch, G. S., Croll, T. I., Morris, J. H., & Ferrin, T. E. (2021). UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Science, 30, 70–82. https://doi.org/10.1002/pro.3943

    Article  CAS  PubMed  Google Scholar 

  66. **, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., … Yang, H. (2020). Structure of Mpro from COVID-19 virus and discovery of its inhibitors. bioRxiv, 582, 289–293.

    CAS  Google Scholar 

  67. Coutsias, E. A., & Wester, M. J. (2019). RMSD and symmetry. Journal of Computational Chemistry, 40, 1496–1508. https://doi.org/10.1002/jcc.25802

    Article  CAS  PubMed  Google Scholar 

  68. Hevener, K. E., Zhao, W., Ball, D. M., Babaoglu, K., Qi, J., White, S. W., & Lee, R. E. (2009). Validation of molecular docking programs for virtual screening against dihydropteroate synthase. Journal of Chemical Information and Modeling, 49, 444–460. https://doi.org/10.1021/ci800293n

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Aniszewski, T. (2015). Alkaloid chemistry. Alkaloids (pp. 99–193). Elsevier.

    Chapter  Google Scholar 

  70. Rule, A. M. (2004). American society of health-system pharmacists’ pain management network. Journal of Pain & Palliative Care Pharmacotherapy. https://doi.org/10.1300/J354v18n03_06

    Article  Google Scholar 

  71. Majumdar, A., Kitson, M. T., & Roberts, S. K. (2016). Systematic review: Current concepts and challenges for the direct-acting antiviral era in hepatitis C cirrhosis. Alimentary Pharmacology & Therapeutics, 43, 1276–1292. https://doi.org/10.1111/apt.13633

    Article  CAS  Google Scholar 

  72. Giannini, A. J., & Slaby, A. E. (1989). Drugs of abuse. Medical Economics Books.

    Google Scholar 

  73. Ernst, E. (2010). Index Nominum 2000. International drug director. Focus on Alternative and Complementary Therapies, 5, 233–233. https://doi.org/10.1111/j.2042-7166.2000.tb02559.x

    Article  Google Scholar 

  74. Si, B., & Song, E. (2018). Recent advances in the detection of neurotransmitters. Chemosensors, 6, 1–24. https://doi.org/10.3390/chemosensors6010001

    Article  CAS  Google Scholar 

  75. Diener, H. C., Jansen, J. P., Reches, A., Pascual, J., Pitei, D., & Steiner, T. J. (2002). Efficacy, tolerability and safety of oral eletriptan and ergotamine plus caffeine (Cafergot®) in the acute treatment of migraine: A multicentre, randomised, double-blind, placebo-controlled comparison. European Neurology, 47, 99–107. https://doi.org/10.1159/000047960

    Article  PubMed  Google Scholar 

  76. Sayfan, J. (2002). Ergotamine-induced anorectal strictures: Report of five cases. Diseases of the Colon and Rectum, 45, 271–272. https://doi.org/10.1007/s10350-004-6160-x

    Article  PubMed  Google Scholar 

  77. Kanfer, I., & Shargel, L. (2020). Approved drug products with therapeutic equivalence evaluations. Generic drug product development (pp. 36–51). The Orange Book.

    Google Scholar 

  78. Schiff, P. L. (2006). Ergot and its alkaloids. American Journal of Pharmaceutical Education, 70, 1–10. https://doi.org/10.5688/aj700598

    Article  Google Scholar 

  79. Mevada, V., Dudhagara, P., Gandhi, H., Vaghamshi, Nilam Beladiya, & U, Patel R,. (2020). Drug repurposing of approved drugs Elbasvir, Ledipasvir, Paritaprevir, Velpatasvir, Antrafenine and Ergotamine for combating COVID19. ChemRxiv. https://doi.org/10.26434/chemrxiv.12115251.v2

    Article  Google Scholar 

  80. Arcon, J. P., Defelipe, L. A., Modenutti, C. P., López, E. D., Alvarez-Garcia, D., Barril, X., Turjanski, A. G., & Martí, M. A. (2017). Molecular dynamics in mixed solvents reveals protein-ligand interactions, improves docking, and allows accurate binding free energy predictions. Journal of Chemical Information and Modeling, 57, 846–863. https://doi.org/10.1021/acs.jcim.6b00678

    Article  CAS  PubMed  Google Scholar 

  81. Struchtrup, H. (2020). Entropy and the second law of thermodynamics-The nonequilibrium perspective. Entropy, 22, 1–61. https://doi.org/10.3390/e22070793

    Article  CAS  Google Scholar 

  82. Beretta, G. P. (2020). The fourth law of thermodynamics: Steepest entropy ascent. Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences, 378, 1–17. https://doi.org/10.1098/rsta.2019.0168

    Article  CAS  Google Scholar 

  83. Chinaka, T. W. (2021). Introducing the second law of thermodynamics using Legitimation Code Theory among first year chemistry students. Cypriot Journal of Educational Sciences, 16, 981–994.

    Article  Google Scholar 

  84. Du, X., Li, Y., **a, Y. L., Ai, S. M., Liang, J., Sang, P., Ji, X. L., & Liu, S. Q. (2016). Insights into protein–ligand interactions: Mechanisms, models, and methods. International Journal of Molecular Sciences, 17, 1–34. https://doi.org/10.3390/ijms17020144

    Article  CAS  Google Scholar 

  85. Byléhn, F., Menéndez, C. A., Perez-Lemus, G. R., Alvarado, W., & De Pablo, J. J. (2021). Modeling the binding mechanism of Remdesivir, Favilavir, and Ribavirin to SARS-CoV-2 RNA-dependent RNA polymerase. ACS Central Science, 7, 164–174. https://doi.org/10.1021/acscentsci.0c01242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Miyamoto, Y., Motohashi, H., Suyama, T., & Yokoyama, J. (2014). Langevin description of gauged scalar fields in a thermal bath. Physical Review D. https://doi.org/10.1103/PhysRevD.89.085037

    Article  Google Scholar 

  87. Qin, X., Zhong, J., & Wang, Y. (2021). A mutant T1 lipase homology modeling, and its molecular docking and molecular dynamics simulation with fatty acids. Journal of Biotechnology, 337, 24–34. https://doi.org/10.1016/j.jbiotec.2021.06.024

    Article  CAS  PubMed  Google Scholar 

  88. Roe, D. R., & Brooks, B. R. (2020). A protocol for preparing explicitly solvated systems for stable molecular dynamics simulations. The Journal of Chemical Physics, 153, 1–9. https://doi.org/10.1063/5.0013849

    Article  CAS  Google Scholar 

  89. Mascoli, V., Liguori, N., Cupellini, L., Elias, E., Mennucci, B., & Croce, R. (2021). Uncovering the interactions driving carotenoid binding in light-harvesting complexes. Chemical Science, 12, 5113–5122. https://doi.org/10.1039/d1sc00071c

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Ragunathan, A., Malathi, K., & Anbarasu, A. (2018). MurB as a target in an alternative approach to tackle the Vibrio cholerae resistance using molecular docking and simulation study. Journal of Cellular Biochemistry, 119, 1726–1732. https://doi.org/10.1002/jcb.26333

    Article  CAS  PubMed  Google Scholar 

  91. Mazola, Y., Guirola, O., Palomares, S., Chinea, G., Menéndez, C., Hernández, L., & Musacchio, A. (2015). A comparative molecular dynamics study of thermophilic and mesophilic β-fructosidase enzymes. Journal of Molecular Modeling, 21, 1–11. https://doi.org/10.1007/s00894-015-2772-4

    Article  CAS  Google Scholar 

  92. Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10, 449–461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Chen, F., Sun, H., Wang, J., Zhu, F., Liu, H., Wang, Z., Lei, T., Li, Y., & Hou, T. (2018). Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA, 24, 1183–1194. https://doi.org/10.1261/rna.065896.118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Wang, C., Greene, D., **ao, L., Qi, R., & Luo, R. (2018). Recent developments and applications of the MMPBSA method. Frontiers in Molecular Biosciences, 4, 1–18.

    Article  Google Scholar 

  95. Wright, D. W., Hall, B. A., Kenway, O. A., Jha, S., & Coveney, P. V. (2014). Computing clinically relevant binding free energies of HIV-1 protease inhibitors. Journal of Chemical Theory and Computation, 10, 1228–1241. https://doi.org/10.1021/ct4007037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Ben-Tal, N., Honig, B., Bagdassarian, C. K., & Ben-Shaul, A. (2000). Association entropy in adsorption processes. Biophysical Journal, 79, 1180–1187. https://doi.org/10.1016/S0006-3495(00)76372-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Šponer, J., Hobza, P., & Leszczynski, J. (1999). Chapter 3 Computational approaches to the studies of the interactions of nucleic acid bases. Theoretical and computational chemistry (pp. 85–117). Elsevier.

    Google Scholar 

  98. Emirik, M. (2022). Potential therapeutic effect of turmeric contents against SARS-CoV-2 compared with experimental COVID-19 therapies: In silico study. Journal of Biomolecular Structure & Dynamics, 40, 2024–2037. https://doi.org/10.1080/07391102.2020.1835719

    Article  CAS  Google Scholar 

Download references

Funding

The authors would like to thank the Brazilian Agencies: Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP). Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Ensino Superior (CAPES) for fellowships and financial supports. The authors would like to thank project Inova Fiocruz FUNCAP (Grant#:06481104-2020), CNPq (Grant 306008/2022-0).

Author information

Authors and Affiliations

  1. Mestrado Acadêmico em Sociobiodiversidades e Tecnologias Sustentáveis – MASTS, Instituto de Engenharias e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil

    Aluísio Marques da Fonseca

  2. Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil

    Bernardino Joaquim Caluaco, Junilson Martinho Canjanja Madureira, Sadrack Queque Cabongo, Faustino Djata & Regilany Paulo Colares

  3. Fundação Oswaldo Cruz – Fiocruz, R. São José, S/N - Precabura, Eusébio, Ceará, 61773-270, Brazil

    Eduardo Menezes Gaieta & Carla Freire Celedonio Fernandes

  4. Curso de Graduação Em Farmácia, Centro Universitário Fametro, Fortaleza, CE, Brazil

    Moises Maia Neto

  5. Faculdade de Filosofia, Dom Aureliano Matos - FAFIDAM, Universidade Estadual Do Ceará, Centro, Limoeiro Do Norte, CE, Brazil

    Gabrielle Silva Marinho & Emmanuel Silva Marinho

  6. Curso de Química, Universidade Estadual Vale do Acaraú, Sobral, CE, Brazil

    Hélcio Silva dos Santos

Authors
  1. Aluísio Marques da Fonseca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernardino Joaquim Caluaco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junilson Martinho Canjanja Madureira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sadrack Queque Cabongo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eduardo Menezes Gaieta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Faustino Djata
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Regilany Paulo Colares
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Moises Maia Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Carla Freire Celedonio Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gabrielle Silva Marinho
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hélcio Silva dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Emmanuel Silva Marinho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hélcio Silva dos Santos.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Ethical Approval

Not applicable.

Informed Consent

Not applicable.

Consent to Participate

Not applicable.

Consent for publication

The authors affirm that provided informed consent for publication.

Institutional Review Board

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

da Fonseca, A.M., Caluaco, B.J., Madureira, J.M.C. et al. Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA. Mol Biotechnol (2023). https://doi.org/10.1007/s12033-023-00831-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12033-023-00831-x

Keywords

Search

Navigation