In Silico Evaluation of Natural Antiviral Compounds Targeting the RBM of SARSCoV-2 Spike Glycoprotein http://www.doi.org/10.26538/tjnpr/v7i10.23
Main Article Content
Abstract
Emerging in Wuhan in December 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has triggered a devastating global pandemic. In response to this crisis, considerable efforts have been dedicated to developing preventive and therapeutic approaches, including investigations into natural products that hold promise in combatting COVID-19. This study utilized computational methods to screen and identify six potential natural antiviral compounds with demonstrated efficacy against the SARS-CoV-2 spike glycoprotein. Molecular docking simulations were employed to predict and analyze the binding interactions between these selected natural compounds and the target protein. Factors such as binding affinity, interaction patterns, and structural compatibility within active sites were taken into account. The results revealed that some of the molecules exhibited positive binding, others didn’t bind at all, with possible interactions between them and the target protein. The computational evaluation obtained for these compounds call for further investigation to evaluate their potential as Spike glycoprotein inhibitors, presenting potential benefits in COVID-19 treatment. These findings contribute to the discovery of novel natural antiviral compounds for SARS-CoV-2, offering valuable leads for subsequent experimental validation and future drug design strategies in the ongoing battle against COVID-19.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Carabelli AM, Peacock TP, Thorne LG, Harvey WT, Hughes J, Peacock SJ, Barclay WS, De Silva TI, Towers GJ, Robertson DL. SARS-CoV-2 variant biology: immune escape, transmission and fitness. Nat Rev Microbiol. 2023; 21: 162-177.
Zatla I, Boublenza L, Hassaine H. The First Days and Months of the COVID-19 Pandemic. RRJoMV. 2022; 12(1): 7-13.
Zatla I, Boublenza L, Hassaine H. Infection and Viral Pathogenesis of SARS-CoV-2. A Review. RRJoMV. 2022; 12(2): 17-23.
Li G, Hilgenfeld R, Whitley R, De Clercg E. Therapeutic strategies for COVID-19: progress and lessons learned. Nat Rev Drug Discov. 2023; 22: 449-475.
Zatla I, Boublenza L, Hassaine H. Therapeutic and Preventive Approaches against COVID-19: A Review. RRJoMV. 2021; 11(3): 26-33.
Wang Z, Wang N, Yang L, Song X-q. Bioactive natural products in COVID-19 therapy. Front Pharmacol. 2022; 13: 926507.
Holst M, Nowak D, Hoch E. Cannabidiol as a Treatment for COVID-19 Symptoms? A Critical Review. Cannabis Cannabinoid Res. 2023; 487-494.
Alrasheid AA, Babiker MY, Awad TA. Evaluation of certain medicinal plants compounds as new potential inhibitors of novel corona virus (COVID-19) using molecular docking analysis. In Silico Pharmacol. 2021; 9: 10.
Takahashi JA, Barbosa BVR, Lima MTNS, Cardoso PG, Contigli C, Pimenta LPS. Antiviral fungal metabolites and some insights into their contribution to the current COVID-19 pandemic. Bioorg Med Chem. 2021; 46: 116366.
Carbone DA, Pellone P, Lubritto C, Ciniglia C. Evaluation of Microalgae Antiviral Activity and Their Bioactive Compounds. Antibiotics. 2021; 10(6): 746.
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. J Comput Chem. 2004; 13: 1605-12.
Lan J, Ge J, Yu J, Shan S, Zhou H, Fan S, Zhang Q, Shi X, Wang Q, Zhang L, Wang X. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature. 2020; 581: 215-220.
Goodsell DS, Olson AJ. Automated Docking of Substrates to Proteins by Simulated Annealing. Proteins: Struct. Funct. Genet. 1990; 8: 195-202.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. Autodock4 and AutoDockTools4: automated docking with selective receptor flexiblity. J. Comput. Chem. 2009; 16: 2785-91.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010; 31: 455-461
Schrödinger, E., Schrödinger. Centenary Celebra, 2016.
Schrödinger L., DE Shaw Research, New York, Maestro-Desmond Interoperability Tools, Schrödinger New York, NY, USA, 2020.
Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015; 20(7): 13384-421.
Hildebrand PW, Rose AS, Tiemann JKS. Bringing Molecular Dynamics Simulation Data into View. Trends Biochem Sci. 2019; 44(11): 902-913.
Rasheed MA, Iqbal MN, Saddick S, Ali I, Khan FS, Kanwal S, Ahmed D, Ibrahim M, Afzal U, Awais M. Identification of Lead Compounds against Scm (fms10) in Enterococcus faecium Using Computer Aided Drug Designing. Life (Basel). 2021; 11(2).
Shivakumar D, Williams J, Wu Y, Damm W, Shelly 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(5): 1509-1519.
Pollastri MP. Overview on the Rule of Five. Curr Protoc Pharmacol. 2010; 49.
Douglas EV, Pires TL, Blundell DB. Ascher pkCSM: predicting small-molecule pharmacokinetic properties using graph-based signatures. J Med Chem. 2015; 58(9): 4066-4072.
Aier I, Varadwaj PK, Raj U. Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep. 2016; 6(1): 1-10.