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image of Boswellic Acid Derived Molecules as SARS-Cov-2 Spike Protein Inhibitors: A Comprehensive Virtual Screening, Triplicate Molecular Dynamic Simulation and Biochemical Validation

Abstract

Background

Coronavirus disease (COVID-19) is a highly infective disease caused by SARS-CoV-2. The SARS-CoV-2 spike protein binds with the human ACE2 receptor to facilitate viral entry into the host cell; therefore, spike protein serves as a potential target for drug development.

Objective

Keeping in view the significance of SARS-CoV-2 spike protein for viral replications, in the current study, we identified the potent inhibitors against SARS-CoV-2 spike protein in order to combat the viral infection.

Methods

In the current study, we screened an library of ~900 natural and synthesized compounds against the spike protein receptor binding domain (RBD) using a structure-based virtual approach, followed by an inhibition bioassay.

Results

Seven ( potent compounds were identified with docking scores ≥ −6.66 Kcal/mol; their drug-likeness, pharmacokinetic, and pharmacodynamic characteristics were excellent with no toxic effect. Those molecules were subjected to a triplicate simulation for 200 ns, which further confirmed their stable binding with RBD. This tight packing of complexes was reflected by calculated binding free energy, which disclosed higher binding free energy of , and than -, while predicted entropic energy demonstrates higher values for , and than the rest of the compounds, indicating more thermodynamic stability in protein due to conformational changes in spike protein induced by binding of , and . These computational analyses were later validated through bioassay. Remarkably, displayed significant inhibitory potential with >76 to 89% inhibition and , , and demonstrated the highest inhibition of RBD.

Conclusion

The current findings suggest that compounds and effectively disrupt the function of RBD of SARS-CoV-2 spike protein and can serve as potential drug candidates for spike protein.

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2025-08-26
2025-10-15

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References

  1. Lefkowitz E.J. Dempsey D.M. Hendrickson R.C. Orton R.J. Siddell S.G. Smith D.B. Virus taxonomy: The database of the international committee on taxonomy of viruses (ICTV). Nucleic Acids Res. 2018 46 D1 D708 D717 10.1093/nar/gkx932 29040670
    [Google Scholar]
  2. Schoenmaker L. Witzigmann D. Kulkarni J.A. Verbeke R. Kersten G. Jiskoot W. Crommelin D.J.A. mRNA-lipid nanoparticle COVID-19 vaccines: Structure and stability. Int. J. Pharm. 2021 601 120586 10.1016/j.ijpharm.2021.120586 33839230
    [Google Scholar]
  3. Ullah A. Ullah S. Halim S.A. Waqas M. Ali B. Ataya F.S. El-Sabbagh N.M. Batiha G.E.S. Avula S.K. Csuk R. Khan A. Al-Harrasi A. Identification of new pharmacophore against SARS-CoV-2 spike protein by multi-fold computational and biochemical techniques. Sci. Rep. 2024 14 1 3590 10.1038/s41598‑024‑53911‑6 38351259
    [Google Scholar]
  4. Yadav R. Chaudhary J.K. Jain N. Chaudhary P.K. Khanra S. Dhamija P. Sharma A. Kumar A. Handu S. Role of structural and non-structural proteins and therapeutic targets of SARS-CoV-2 for COVID-19. Cells 2021 10 4 821 10.3390/cells10040821 33917481
    [Google Scholar]
  5. Ullah S. Ullah A. Waqas M. Halim S.A. Khan I. Ur Rehman S. Abdellattif M.H. Soomro S. Ibrar A. Kashtoh H. Khan A. Al-Harrasi A. Exploring the therapeutic potential of coumarin-thiazolotriazole pharmacophores for SARS-CoV-2 spike protein through in vitro and in silico evaluation. Curr. Med. Chem. 2024 10.2174/0109298673323284240911052131
    [Google Scholar]
  6. Kim J.Y. Kim Y.I. Park S.J. Kim I.K. Choi Y.K. Kim S.H. Safe, high-throughput screening of natural compounds of MERS-CoV entry inhibitors using a pseudovirus expressing MERS-CoV spike protein. Int. J. Antimicrob. Agents 2018 52 5 730 732 10.1016/j.ijantimicag.2018.05.003 29772395
    [Google Scholar]
  7. Mousavizadeh L. Ghasemi S. Genotype and phenotype of COVID-19: Their roles in pathogenesis. J. Microbiol. Immunol. Infect. 2021 54 2 159 163 10.1016/j.jmii.2020.03.022 32265180
    [Google Scholar]
  8. Hussain M. Jabeen N. Amanullah A. Ashraf Baig A. Aziz B. Shabbir S. Raza F. Uddin N. Molecular docking between human TMPRSS2 and SARS-CoV-2 spike protein: Conformation and intermolecular interactions. AIMS Microbiol. 2020 6 3 350 360 10.3934/microbiol.2020021 33029570
    [Google Scholar]
  9. Vankadari N. Ketavarapu V. Mitnala S. Vishnubotla R. Reddy D.N. Ghosal D. Structure of human TMPRSS2 in complex with SARS-CoV-2 spike glycoprotein and implications for potential therapeutics. J. Phys. Chem. Lett. 2022 13 23 5324 5333 10.1021/acs.jpclett.2c00967 35675654
    [Google Scholar]
  10. Ullah S. Ullah A. Waqas M. Halim S.A. Pasha A.R. Shafiq Z. Mali S.N. Jawarkar R.D. Khan A. Khalid A. Abdalla A.N. Kashtoh H. Al-Harrasi A. Structural, dynamic behaviour, in vitro and computational investigations of Schiff’s bases of 1,3-diphenyl urea derivatives against SARS-CoV-2 spike protein. Sci. Rep. 2024 14 1 12588 10.1038/s41598‑024‑63345‑9 38822113
    [Google Scholar]
  11. Chang C. Lin S.M. Satange R. Lin S.C. Sun S.C. Wu H.Y. Kehn-Hall K. Hou M.H. Targeting protein-protein interaction interfaces in COVID-19 drug discovery. Comput. Struct. Biotechnol. J. 2021 19 2246 2255 10.1016/j.csbj.2021.04.003 33936565
    [Google Scholar]
  12. Riad A. Pokorná A. Attia S. Klugarová J. Koščík M. Klugar M. Prevalence of COVID-19 vaccine side effects among healthcare workers in the Czech Republic. J. Clin. Med. 2021 10 7 1428 10.3390/jcm10071428 33916020
    [Google Scholar]
  13. Beatty A.L. Peyser N.D. Butcher X.E. Cocohoba J.M. Lin F. Olgin J.E. Pletcher M.J. Marcus G.M. Analysis of COVID-19 vaccine type and adverse effects following vaccination. JAMA Netw. Open 2021 4 12 e2140364 e2140364 10.1001/jamanetworkopen.2021.40364 34935921
    [Google Scholar]
  14. Morfin F. Thouvenot D. Herpes simplex virus resistance to antiviral drugs. J. Clin. Virol. 2003 26 1 29 37 10.1016/S1386‑6532(02)00263‑9 12589832
    [Google Scholar]
  15. van Dorp L. Acman M. Richard D. Shaw L.P. Ford C.E. Ormond L. Owen C.J. Pang J. Tan C.C.S. Boshier F.A.T. Ortiz A.T. Balloux F. Emergence of genomic diversity and recurrent mutations in SARS-CoV-2. Infect. Genet. Evol. 2020 83 104351 10.1016/j.meegid.2020.104351 32387564
    [Google Scholar]
  16. Li S. Chen C. Zhang H. Guo H. Wang H. Wang L. Zhang X. Hua S. Yu J. Xiao P. Li R.S. Tan X. Identification of natural compounds with antiviral activities against SARS-associated coronavirus. Antiviral Res. 2005 67 1 18 23 10.1016/j.antiviral.2005.02.007 15885816
    [Google Scholar]
  17. Smith C.J. Perfetti T.A. A comparison of the persistence, toxicity, and exposure to high-volume natural plant-derived and synthetic pesticides. Toxicol. Res. Appl. 2020 4 2397847320940561 10.1177/2397847320940561
    [Google Scholar]
  18. Bhuiyan F.R. Howlader S. Raihan T. Hasan M. Plants metabolites: Possibility of natural therapeutics against the COVID-19 pandemic. Front. Med. 2020 7 444 10.3389/fmed.2020.00444 32850918
    [Google Scholar]
  19. Henkel T. Brunne R.M. Müller H. Reichel F. Statistical investigation into the structural complementarity of natural products and synthetic compounds. Angew. Chem. Int. Ed. 1999 38 5 643 647 10.1002/(SICI)1521‑3773(19990301)38:5<643:AID‑ANIE643>3.0.CO;2‑G 29711552
    [Google Scholar]
  20. Boozari M. Hosseinzadeh H. Natural products for COVID ‐19 prevention and treatment regarding to previous coronavirus infections and novel studies. Phytother. Res. 2021 35 2 864 876 10.1002/ptr.6873 32985017
    [Google Scholar]
  21. Halim S.A. Waqas M. Khan A. Al-Harrasi A. In silico prediction of novel inhibitors of SARS-CoV-2 main protease through structure-based virtual screening and molecular dynamic simulation. Pharmaceuticals 2021 14 9 896 10.3390/ph14090896 34577596
    [Google Scholar]
  22. Yepes-Pérez A.F. Herrera-Calderon O. Sánchez-Aparicio J.E. Tiessler-Sala L. Maréchal J.D. Cardona-G W. Investigating potential inhibitory effect of Uncaria tomentosa (Cat’s Claw) against the Main Protease 3CLpro of SARS-CoV-2 by molecular modeling. Evid. Based Complement. Alternat. Med. 2020 2020 4932572 10.1155/2020/4932572
    [Google Scholar]
  23. Naik V.R. Munikumar M. Ramakrishna U. Srujana M. Goudar G. Naresh P. Kumar B.N. Hemalatha R. Remdesivir (GS-5734) as a therapeutic option of 2019-nCOV main protease – in silico approach. J. Biomol. Struct. Dyn. 2021 39 13 4701 4714 10.1080/07391102.2020.1781694 32568620
    [Google Scholar]
  24. Delgado Blanco J. Hernandez-Alias X. Cianferoni D. Serrano L. In silico mutagenesis of human ACE2 with S protein and translational efficiency explain SARS-CoV-2 infectivity in different species. PLOS Comput. Biol. 2020 16 12 e1008450 10.1371/journal.pcbi.1008450 33284795
    [Google Scholar]
  25. Rose P.W. The RCSB protein data bank: Integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 2016 gkw1000
    [Google Scholar]
  26. Ullah A. Waqas M. Halim S.A. Daud M. Jan A. Khan A. Al-Harrasi A. Sirtuin 1 inhibition: A promising avenue to suppress cancer progression through small inhibitors design. J. Biomol. Struct. Dyn. 2024 42 19 9825 9841 10.1080/07391102.2023.2252898 37661778
    [Google Scholar]
  27. Ono S. Naylor M.R. Townsend C.E. Okumura C. Okada O. Lee H.W. Lokey R.S. Cyclosporin A. Conformational complexity and chameleonicity. J. Chem. Inf. Model. 2021 61 11 5601 5613 10.1021/acs.jcim.1c00771 34672629
    [Google Scholar]
  28. Taha M. Ismail N.H. Imran S. Mohamad M.H. Wadood A. Rahim F. Saad S.M. Rehman A. Khan K.M. Synthesis, α-glucosidase inhibitory, cytotoxicity and docking studies of 2-aryl-7-methylbenzimidazoles. Bioorg. Chem. 2016 65 100 109 10.1016/j.bioorg.2016.02.004 26894559
    [Google Scholar]
  29. Grisoni F. Merk D. Friedrich L. Schneider G. Design of natural‐product‐inspired multitarget ligands by machine learning. ChemMedChem 2019 14 12 1129 1134 10.1002/cmdc.201900097 30973672
    [Google Scholar]
  30. Hajiebrahimi A. Ghasemi Y. Sakhteman A. FLIP: An assisting software in structure based drug design using fingerprint of protein-ligand interaction profiles. J. Mol. Graph. Model. 2017 78 234 244 10.1016/j.jmgm.2017.10.021 29121561
    [Google Scholar]
  31. Cheng F. Li W. Zhou Y. Shen J. Wu Z. Liu G. Lee P.W. Tang Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012 52 11 3099 3105 10.1021/ci300367a
    [Google Scholar]
  32. Daina A. Michielin O. Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017 7 1 42717 10.1038/srep42717 28256516
    [Google Scholar]
  33. Verma R.P. Hansch C. Camptothecins: A SAR/QSAR Study. Chem. Rev. 2009 109 1 213 235 10.1021/cr0780210 19099450
    [Google Scholar]
  34. Banerjee P. Eckert A.O. Schrey A.K. Preissner R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018 46 W1 W257 W263 10.1093/nar/gky318 29718510
    [Google Scholar]
  35. Ullah A. Rehman N.U. Islam W.U. Khan F. Waqas M. Halim S.A. Jan A. Muhsinah A.B. Khan A. Al-Harrasi A. Identification of small molecular inhibitors of SIRT3 by computational and biochemical approaches a potential target of breast cancer. Sci. Rep. 2024 14 1 12475 10.1038/s41598‑024‑63177‑7 38816444
    [Google Scholar]
  36. Erkes D.A. Selvan S.R. Contact hypersensitivity, autoimmune reactions, and tumor regression: plausibility of mediating antitumor immunity. J. Immunol. Res. 2014 2014 175265 10.1155/2014/175265
    [Google Scholar]
  37. Dickson C.J. Walker R.C. Gould I.R. Lipid21: complex lipid membrane simulations with AMBER. J. Chem. Theory Comput. 2022 18 3 1726 1736 10.1021/acs.jctc.1c01217 35113553
    [Google Scholar]
  38. Mikhailovskii O. Izmailov S.A. Xue Y. Case D.A. Skrynnikov N.R. X-ray crystallography module in MD simulation program amber 2023. Refining the models of protein crystals. J. Chem. Inf. Model. 2024 64 1 18 25 10.1021/acs.jcim.3c01531 38147516
    [Google Scholar]
  39. Sengupta A. Li Z. Song L.F. Li P. Merz K.M. Jr Parameterization of monovalent ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB water models. J. Chem. Inf. Model. 2021 61 2 869 880 10.1021/acs.jcim.0c01390 33538599
    [Google Scholar]
  40. Darden T. York D. Pedersen L. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993 98 12 10089 10092 10.1063/1.464397
    [Google Scholar]
  41. Zielkiewicz J. Structural properties of water: Comparison of the SPC, SPCE, TIP4P, and TIP5P models of water. J. Chem. Phys. 2005 123 10 104501 10.1063/1.2018637 16178604
    [Google Scholar]
  42. Sindhikara D.J. Kim S. Voter A.F. Roitberg A.E. Bad seeds sprout perilous dynamics: Stochastic thermostat induced trajectory synchronization in biomolecules. J. Chem. Theory Comput. 2009 5 6 1624 1631 10.1021/ct800573m 26609854
    [Google Scholar]
  43. Roe D.R. Cheatham T.E. III PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 2013 9 7 3084 3095 10.1021/ct400341p 26583988
    [Google Scholar]
  44. Waqas M. Halim S.A. Ullah A. Ali A.A.M. Khalid A. Abdalla A.N. Khan A. Al-Harrasi A. Multi-fold computational analysis to discover novel putative inhibitors of isethionate sulfite-lyase (isla) from bilophila wadsworthia: Combating colorectal cancer and inflammatory bowel diseases. Cancers 2023 15 3 901 10.3390/cancers15030901 36765864
    [Google Scholar]
  45. Gohlke H. Hendlich M. Klebe G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol. 2000 295 2 337 356 10.1006/jmbi.1999.3371 10623530
    [Google Scholar]
  46. Wang C. Nguyen P.H. Pham K. Huynh D. Le T.B.N. Wang H. Ren P. Luo R. Calculating protein–ligand binding affinities with MMPBSA: Method and error analysis. J. Comput. Chem. 2016 37 27 2436 2446 10.1002/jcc.24467 27510546
    [Google Scholar]
  47. Cong Y. Li M. Feng G. Li Y. Wang X. Duan L. Trypsin-Ligand binding affinities calculated using an effective interaction entropy method under polarized force field. Sci. Rep. 2017 7 1 17708 10.1038/s41598‑017‑17868‑z 29255159
    [Google Scholar]
  48. Du X. Li Y. Xia Y.L. Ai S.M. Liang J. Sang P. Ji X.L. Liu S.Q. Insights into protein–ligand interactions: Mechanisms, models, and methods. Int. J. Mol. Sci. 2016 17 2 144 10.3390/ijms17020144 26821017
    [Google Scholar]
  49. Muegge I. Martin Y.C. A general and fast scoring function for protein-ligand interactions: A simplified potential approach. J. Med. Chem. 1999 42 5 791 804 10.1021/jm980536j 10072678
    [Google Scholar]
  50. Onufriev A. Bashford D. Case D.A. Exploring protein native states and large‐scale conformational changes with a modified generalized born model. Proteins 2004 55 2 383 394 10.1002/prot.20033 15048829
    [Google Scholar]
  51. Moritsugu K. Njunda B.M. Smith J.C. Theory and normal-mode analysis of change in protein vibrational dynamics on ligand binding. J. Phys. Chem. B 2010 114 3 1479 1485 10.1021/jp909677p 20043649
    [Google Scholar]
  52. Chang C.A. Chen W. Gilson M.K. Ligand configurational entropy and protein binding. Proc. Natl. Acad. Sci. USA 2007 104 5 1534 1539 10.1073/pnas.0610494104 17242351
    [Google Scholar]
  53. Wang B. Li L. Hurley T.D. Meroueh S.O. Molecular recognition in a diverse set of protein-ligand interactions studied with molecular dynamics simulations and end-point free energy calculations. J. Chem. Inf. Model. 2013 53 10 2659 2670 10.1021/ci400312v 24032517
    [Google Scholar]
  54. Haddad Y. Adam V. Heger Z. Rotamer dynamics: Analysis of rotamers in molecular dynamics simulations of proteins. Biophys. J. 2019 116 11 2062 2072 10.1016/j.bpj.2019.04.017 31084902
    [Google Scholar]
  55. Müller W.E.G. Neufurth M. Schepler H. Wang S. Tolba E. Schröder H.C. Wang X. The biomaterial polyphosphate blocks stoichiometric binding of the SARS-CoV-2 S-protein to the cellular ACE2 receptor. Biomater. Sci. 2020 8 23 6603 6610 10.1039/D0BM01244K 33231598
    [Google Scholar]
  56. Gu C. Wu Y. Guo H. Zhu Y. Xu W. Wang Y. Zhou Y. Sun Z. Cai X. Li Y. Liu J. Huang Z. Yuan Z. Zhang R. Deng Q. Qu D. Xie Y. Protoporphyrin IX and verteporfin potently inhibit SARS-CoV-2 infection in vitro and in a mouse model expressing human ACE2. Sci. Bull. 2021 66 9 925 936 10.1016/j.scib.2020.12.005 33318880
    [Google Scholar]
  57. Zhang M.Q. Wilkinson B. Drug discovery beyond the ‘rule-of-five’. Curr. Opin. Biotechnol. 2007 18 6 478 488 10.1016/j.copbio.2007.10.005 18035532
    [Google Scholar]
  58. Ivanović V. Rančić M. Arsić B. Pavlović A. Lipinski’s rule of five, famous extensions and famous exceptions. Chemia Naissensis 2020 3 1 171 181 10.46793/ChemN3.1.171I
    [Google Scholar]
  59. Avdeef A. Kansy M. “Flexible-acceptor” general solubility equation for beyond rule of 5 drugs. Mol. Pharm. 2020 17 10 3930 3940 10.1021/acs.molpharmaceut.0c00689 32787270
    [Google Scholar]
  60. Roskoski R. Jr Rule of five violations among the FDA-approved small molecule protein kinase inhibitors. Pharmacol. Res. 2023 191 106774 10.1016/j.phrs.2023.106774 37075870
    [Google Scholar]
  61. Bhattacharyya J. Bellucci J.J. Weitzhandler I. McDaniel J.R. Spasojevic I. Li X. Lin C.C. Chi J.T.A. Chilkoti A. A paclitaxel-loaded recombinant polypeptide nanoparticle outperforms Abraxane in multiple murine cancer models. Nat. Commun. 2015 6 1 7939 10.1038/ncomms8939 26239362
    [Google Scholar]
  62. Forrest M.L. Won C.Y. Malick A.W. Kwon G.S. In vitro release of the mTOR inhibitor rapamycin from poly(ethylene glycol)-b-poly(ε-caprolactone) micelles. J. Control. Release 2006 110 2 370 377 10.1016/j.jconrel.2005.10.008 16298448
    [Google Scholar]
  63. Makino K. Nakajima T. Shikamura M. Ito F. Ando S. Kochi C. Inagawa H. Soma G.I. Terada H. Efficient intracellular delivery of rifampicin to alveolar macrophages using rifampicin-loaded PLGA microspheres: effects of molecular weight and composition of PLGA on release of rifampicin. Colloids Surf. B Biointerfaces 2004 36 1 35 42 10.1016/j.colsurfb.2004.03.018 15261021
    [Google Scholar]
  64. Veber D.F. Johnson S.R. Cheng H.Y. Smith B.R. Ward K.W. Kopple K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002 45 12 2615 2623 10.1021/jm020017n 12036371
    [Google Scholar]
  65. Muegge I. Heald S.L. Brittelli D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 2001 44 12 1841 1846 10.1021/jm015507e 11384230
    [Google Scholar]
  66. Salam S.S. Chetia P. Kardong D. In silico docking, ADMET and QSAR study of few antimalarial phytoconstituents as inhibitors of plasmepsin II of P. falciparum against malaria. Curr. Drug Ther. 2020 15 3 264 273 10.2174/1574885514666190923112738
    [Google Scholar]
  67. Ciotti M. Ciccozzi M. Terrinoni A. Jiang W.C. Wang C.B. Bernardini S. The COVID-19 pandemic. Crit. Rev. Clin. Lab. Sci. 2020 57 6 365 388 10.1080/10408363.2020.1783198 32645276
    [Google Scholar]
  68. Basu A. Sarkar A. Maulik U. Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2. Sci. Rep. 2020 10 1 17699 10.1038/s41598‑020‑74715‑4 33077836
    [Google Scholar]
  69. Kabi A.K. Pal M. Gujjarappa R. Malakar C.C. Roy M. Overview of hydroxychloroquine and remdesivir on severe acute respiratory syndrome coronavirus 2 (SARS‐COV-2). J. Heterocycl. Chem. 2023 60 2 165 182 10.1002/jhet.4541 35942205
    [Google Scholar]
  70. Han P. Receptor binding and complex structures of human ACE2 to spike RBD from omicron and delta SARS-CoV-2. Cell 2022 185 4 630 640 10.1016/j.cell.2022.03.013
    [Google Scholar]
  71. Jean S.S. Hsueh P.R. Old and re-purposed drugs for the treatment of COVID-19. Expert Rev. Anti Infect. Ther. 2020 18 9 843 847 10.1080/14787210.2020.1771181 32419524
    [Google Scholar]
  72. Olaleye O.A. Kaur M. Onyenaka C.C. Ambroxol hydrochloride inhibits the interaction between severe acute respiratory syndrome coronavirus 2 spike protein’s receptor binding domain and recombinant human ACE2. BioRxiv 2020 2020.09
    [Google Scholar]
  73. Ho T. Wu S. Chen J. Li C. Hsiang C. Emodin blocks the SARS coronavirus spike protein and angiotensin-converting enzyme 2 interaction. Antiviral Res. 2007 74 2 92 101 10.1016/j.antiviral.2006.04.014 16730806
    [Google Scholar]
  74. Wang C. Li W. Drabek D. Okba N.M.A. van Haperen R. Osterhaus A.D.M.E. van Kuppeveld F.J.M. Haagmans B.L. Grosveld F. Bosch B.J. A human monoclonal antibody blocking SARS-CoV-2 infection. Nat. Commun. 2020 11 1 2251 10.1038/s41467‑020‑16256‑y 32366817
    [Google Scholar]
  75. Du L. He Y. Zhou Y. Liu S. Zheng B.J. Jiang S. The spike protein of SARS-CoV — A target for vaccine and therapeutic development. Nat. Rev. Microbiol. 2009 7 3 226 236 10.1038/nrmicro2090 19198616
    [Google Scholar]
  76. Thanh Le T. Andreadakis Z. Kumar A. Gómez Román R. Tollefsen S. Saville M. Mayhew S. The COVID-19 vaccine development landscape. Nat. Rev. Drug Discov. 2020 19 5 305 306 10.1038/d41573‑020‑00073‑5 32273591
    [Google Scholar]
  77. Jeyanathan M. Afkhami S. Smaill F. Miller M.S. Lichty B.D. Xing Z. Immunological considerations for COVID-19 vaccine strategies. Nat. Rev. Immunol. 2020 20 10 615 632 10.1038/s41577‑020‑00434‑6 32887954
    [Google Scholar]
  78. Raus K. Mortier E. Eeckloo K. Ethical reflections on Covid-19 vaccines. Acta Clin. Belg. 2022 77 3 600 605 10.1080/17843286.2021.1925027 34008482
    [Google Scholar]
  79. Țăran A.M. Mustea L. Vătavu S. Lobonț O.R. Luca M.M. Challenges and drawbacks of the EU medical system generated by the covid-19 pandemic in the field of health systems’ digitalization. Int. J. Environ. Res. Public Health 2022 19 9 4950 10.3390/ijerph19094950 35564345
    [Google Scholar]
  80. Sprent J. King C. COVID-19 vaccine side effects: The positives about feeling bad. Sci. Immunol. 2021 6 60 eabj9256 10.1126/sciimmunol.abj9256 34158390
    [Google Scholar]
  81. Haynes B.F. Corey L. Fernandes P. Gilbert P.B. Hotez P.J. Rao S. Santos M.R. Schuitemaker H. Watson M. Arvin A. Prospects for a safe COVID-19 vaccine. Sci. Transl. Med. 2020 12 568 eabe0948 10.1126/scitranslmed.abe0948 33077678
    [Google Scholar]
  82. Wise J. COVID-19: Two rare vaccine side effects detected in large global study. BMJ 2024 384 q488 10.1136/bmj.q488 38408769
    [Google Scholar]
  83. Brogi S. In silico methods for drug design and discovery; Lausanne. SA Frontiers Media 2020 10.3389/978‑2‑88966‑057‑5
    [Google Scholar]
  84. Ekins S. Mestres J. Testa B. In silico pharmacology for drug discovery: Applications to targets and beyond. Br. J. Pharmacol. 2007 152 1 21 37 10.1038/sj.bjp.0707306 17549046
    [Google Scholar]
  85. Jaiswal S. Kumar M. Mandeep; Sunita; Singh, Y.; Shukla, P. Systems biology approaches for therapeutics development against COVID-19. Front. Cell. Infect. Microbiol. 2020 10 560240 10.3389/fcimb.2020.560240 33194800
    [Google Scholar]
  86. Ali S. Alam M. Khatoon F. Fatima U. Elasbali A.M. Adnan M. Islam A. Hassan M.I. Snoussi M. De Feo V. Natural products can be used in therapeutic management of COVID-19: Probable mechanistic insights. Biomed. Pharmacother. 2022 147 112658 10.1016/j.biopha.2022.112658 35066300
    [Google Scholar]
  87. Vakilian S. Alam K. Al-Kindi J. Jamshidi-Adegani F. Rehman N.U. Tavakoli R. Al-Riyami K. Hasan A. Zadjali F. Csuk R. Al-Harrasi A. Al-Hashmi S. An engineered microfluidic blood‐brain barrier model to evaluate the anti‐metastatic activity of β‐boswellic acid. Biotechnol. J. 2021 16 10 2100044 10.1002/biot.202100044 34313388
    [Google Scholar]
  88. Qurishi Y. Potential role of natural molecules in health and disease: Importance of boswellic acid. J. Med. Plants Res. 2010 4 25 2778 2785
    [Google Scholar]
  89. Sethi V. Garg M. Herve M. Mobasheri A. Potential complementary and/or synergistic effects of curcumin and boswellic acids for management of osteoarthritis. Ther Adv Musculoskelet Dis. 2022 14 1759720X221124545 10.1177/1759720X221124545
    [Google Scholar]
  90. Gomaa A.A. Mohamed H.S. Abd-ellatief R.B. Gomaa M.A. Boswellic acids/Boswellia serrata extract as a potential COVID-19 therapeutic agent in the elderly. Inflammopharmacology 2021 29 4 1033 1048 10.1007/s10787‑021‑00841‑8 34224069
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673354901250402143533
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Boswellic Acid Derived Molecules as SARS-Cov-2 Spike Protein Inhibitors: A Comprehensive Virtual Screening, Triplicate Molecular Dynamic Simulation and Biochemical Validation
Bentham Science Publishers ; https://doi.org/10.2174/0109298673354901250402143533
/content/journals/cmc/10.2174/0109298673354901250402143533
/content/journals/cmc/10.2174/0109298673354901250402143533
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  • Article Type:     Research Article
Keywords: molecular docking ; pharmacokinetics ; viral infection ; MD simulation ; spike protein ; SARS-CoV-2

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