Molecular Docking, Molecular Dynamic Simulation and ADME of Some Plant Extracts and their Effects on COVID-19 Patients
doi.org/10.26538/tjnpr/v6i8.13
Keywords:
Alliin,, Marrubin,, Molecular docking,, SARS-CoV2,, Thymoquinone,, MD simulationAbstract
The coronavirus disease 2019 (COVID-19) is caused by the recently discovered coronavirus and affects several countries worldwide. Some medications may alleviate or minimize some of the disease symptoms, but no drug have been proven to prevent or cure it. However, this study was aimed at investigating the role of some medicinal plants as potent inhibitors of COVID-19 main protease (MPro). More than 250 plant extracts with antiviral activity were exploited for their potential SARS-CoV2 medication using molecular docking. The conformational stability of the compounds extracted from the plants with MPro interactions was evaluated using molecular dynamics simulations. Then, the plant extracts with the highest binding energies were used for treatments by administering them to 50 COVID-19 patients, while the other 50 cases received only the drug without the plant extracts. The results of the theoretical analysis revealed high binding energies for seven compounds. Alliin stabilized COVID-19’s MPro while retaining critical connections and remained stable throughout the simulations. Marrubin and thymoquinone are also capable of protein stabilization over the simulated time. The test plants were observed to be effective against the virus in the COVID-19 patients, with a disease symptom improvement response rate of 78-86 and 60-72% for the first and second groups, respectively. Also, the percentage of oxygen increased from the second day after taking the extracts. Ground-glass opacity disappeared from the second group that received the plant extracts. The findings of this study suggest that these compounds have a great potential for therapeutic activity if isolated and administered alone.
References
Abebe EC, Dejenie TA, Shiferaw MY, Malik T. The newly emerged COVID-19 disease: A systemic review. Virol J Biomed Central. 2020; 17:96.
Lazzarotto-Figueiró J, Capelezzo AP, Schindler MSZ, Fossá JFC, Albeny-Simões D, Zanatta L, et al. Antioxidant activity, antibacterial and inhibitory effects of intestinal disaccharidases of extracts obtained from Eugenia uniflora l. seeds. Braz J Biol. 2021; 81(2):291–300.
Wang YJ, Wang L, Bao L. Exploring the active compounds of traditional Mongolian medicine in the intervention of novel coronavirus (COVID-19) based on molecular docking method. J Funct Foods. 2020; 1:71.
Arunkumar B, Fernandez A, Laila SP, Nair AS. Molecular docking study of acyclovir and its derivatives as potent inhibitors in novel COVID-19. Int J Pharm Sci Res. 2020; 11(9): 4700-4705.
Fischer A, Smieško M, Sellner M, Lill MA. Decision making in structure-based drug discovery: Visual inspection of docking results. J Med Chem. 2021; 64:2489–2500.
Mohammad T, Mathur Y, Hassan MI. InstaDock: A single- click graphical user interface for molecular docking-based virtual high-throughput screening. Brief Bioinform. 2021; 22(4): bbaa279. doi: 10.1093/bib/bbaa279. PMID: 33105480.
Thapa B and Raghavachari K. Energy decomposition analysis of protein-ligand interactions using the molecules- in-molecules fragmentation-based method. J Chem Inf Model. 2019; 59(8):3474–3484.
Riza YM, Parves MR, Tithi FA, Alam S. Quantum chemical calculation and binding modes of H1R; a combined study of molecular docking and DFT for suggesting therapeutically potent H1R antagonist. Silico Pharmacol. 2019; 7(1): 1. doi: 10.1007/s40203-019-0050-3. PMID: 30863716; PMCID: PMC6389732
Gamus A and Chodick G. Telemedicine after COVID-19: The Israeli perspective. Isr Med Assoc J. 2020; 22(8):467– 469.
Maia JDC, Urquiza-Carvalho GA, Mangueira CP, Santana SR, Cabral LAF, Rocha GB. GPU linear algebra libraries and GPGPU programming for accelerating MOPAC semiempirical quantum chemistry calculations. J Chem Theory Comput. 2012; 8(9):3072–3081.
Wilson JT, Borovitskiy V, Terenin A, Mostowsky P, Deisenroth MP. Pathwise conditioning of gaussian processes. J Mach Learn Res. 2021; 22: 7470–7480.
Ekins S, Mottin M, Ramos PR, Sousa BK, Neves BJ, Foil DH, Zorn K, Braga R, Coffee M, Southan C, Puhl A, Andrade C. Déjà vu: Stimulating open drug discovery for SARS-CoV-2. Drug Discov Today. 2020; 25:928-941.
Grime KH, Barton P, McGinnity DF. Application of in silico, in vitro and preclinical pharmacokinetic data for the effective and efficient prediction of human pharmacokinetics. Mol Pharm. 2013; 10(4):1191–1206.
Alegaon SG, Alagawadi KR, Sonkusare PV, Chaudhary SM, Dadwe DH, Shah AS. Novel imidazo [2, 1-b][1, 3, 4] thiadiazole carrying rhodanine-3-acetic acid as potential antitubercular agents. Bioorg Med Chem Lett. 2012; 22(5):1917–1921.
Amin NH, El-Saadi MT, Ibrahim AA, Abdel-Rahman HM. Design, synthesis and mechanistic study of new 1, 2, 4- triazole derivatives as antimicrobial agents. Bioorg Chem. 2021; 111:10484.
Yan Y, Shen X, Cao Y, Zhang J, Wang Y, Cheng Y. Discovery of anti-2019-nCoV agents from 38 Chinese patent drugs toward respiratory diseases via docking screening.Preprints2020,2020020254(doi: 10.20944/preprints202002.0254.v2).
Khaerunnisa S, Kurniawan H, Awaluddin R, Suhartati S, Soetjipto S. Potential inhibitor of COVID-19 main protease (Mpro) from several medicinal plant compounds by molecular docking study: Preprints 2020, 2020030226 (doi: 10.20944/preprints202003.0226.v1).
Sayed AM, Khattab AR, AboulMagd AM, Hassan HM, Rateb ME., Zaid H, Abdelmohsen UR. Nature as a treasure trove of potential anti-SARS-CoV drug leads: a structural/mechanistic rationale. RSC Adv. 2020; 10(34):19790–19802.
Mansour MA, Aboul-Magd AM, Abdel-Rahman HM. Quinazoline-Schiff base conjugates: in silico study and ADMET predictions as multi-target inhibitors of coronavirus (SARS-CoV-2) proteins. RSC Adv. 2020; 10(56):34033–34045.
Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis J, Kolossvary I, Moraes M, Sacerdoti F, Salmon J, Shan Y, Shaw D. Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC’06. 2006. 43–43 p.
Chow E, Rendleman CA, Bowers KJ, Dror RO, H D, Gullingsrud J, Sacerdoti F, Shaw D. Desmond performance on a cluster of multicore processors. Simulation. 2008; DE Shaw Research Technical Report DESRES/TR--2008-01.
Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the opls force field. J Chem Theory Comput. 2010; 6: 1509– 1519.
Jorgensen WL, Maxwell DS, Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc. 1996; 118:11225–11236.
Martyna GJ, Klein ML, Tuckerman M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J Chem Phys. 97:2635–2643.
Martyna GJ, Tobias DJ, Klein ML. Constant pressure molecular dynamics algorithms. J Chem Physics. 1994; 101:4177–4189.
Toukmaji AY and Board JA. Ewald summation techniques in perspective: a survey. Comp Physics Comm. 1996; 95:20-73-92
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
