Skip to content
2000
image of Discovery of SARS-CoV-2 Main Protease Inhibitors from Natural Products via Machine Learning with Pharmacophore Modeling, Similarity Methods, and Molecular Dynamics

Abstract

Introduction

The SARS-CoV-2 main protease (Mpro) is a critical enzyme for viral replication, making it an essential target for COVID-19 therapeutic development. In this study, we conducted a comprehensive virtual screening campaign to identify natural product-derived Mpro inhibitors using both structure-based pharmacophore modeling and ligand-based similarity search.

Methods

Two optimized pharmacophore models were constructed from Mpro crystallographic structures (PDB codes 7QBB and 7TIA), validated through ROC analysis, optimized using Dynophores dynamic simulations, and used to screen two natural product libraries. The ligand-based screening was also performed using the co-crystallized ligands of these models, capturing compounds with high shape and atom-based similarity.

Results

Two rounds of molecular docking were performed to filter and refine the hits, leading to the identification of 17 promising compounds with favorable binding interactions and physicochemical profiles. Molecular dynamics simulations of top hits demonstrated stable binding within the Mpro active site, with binding energies supporting their potential as potent inhibitors.

Discussion

The integration of dynamic pharmacophore modeling (dynophore) represents a significant advancement over static models by accounting for protein-ligand interaction flexibility during molecular dynamics. This dynamic approach not only improves hit specificity but also reduces false positives, thereby enhancing the reliability of the virtual screening process. Furthermore, the identification of compound 10313 with high binding stability underscores the predictive value of combining pharmacophore filtering with MD simulations.

Conclusion

This study highlights the value of natural products as a reservoir for Mpro inhibitors, presenting novel candidates for further experimental validation in the fight against COVID-19.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cei/10.2174/0115734080371991250605114818
2025-07-04
2025-09-26

  • From This Site

    /content/journals/cei/10.2174/0115734080371991250605114818
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Wang C. Horby P.W. Hayden F.G. Gao G.F. A novel coronavirus outbreak of global health concern. Lancet 2020 395 10223 470 473 10.1016/S0140‑6736(20)30185‑9
    [Google Scholar]
  2. Wu F. Zhao S. Yu B. A new coronavirus associated with human respiratory disease in China. Nature 2020 579 7798 265 269 10.1038/s41586‑020‑2008‑3
    [Google Scholar]
  3. Khanfar M.A. Saleh M.I. SARS-CoV-2 main protease inhibitors from natural product repository as therapeutic candidates for the treatment of coronaviridae infections. Curr. Med. Chem. 2023 32 4 688 719 10.2174/0109298673271674231109052709 38013440
    [Google Scholar]
  4. Chen Y. Liu Q. Guo D. Emerging coronaviruses: Genome structure, replication, and pathogenesis. J. Med. Virol. 2020 92 4 418 423 10.1002/jmv.25681
    [Google Scholar]
  5. Wang Y. Grunewald M. Perlman S. Coronaviruses: An updated overview of their replication and pathogenesis. Methods Mol. Biol. 2018 2203 1 29 10.1007/978‑1‑0716‑0900‑2_1 32833200
    [Google Scholar]
  6. Jin Z. Du X. Xu Y. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 2020 582 7811 289 293 10.1038/s41586‑020‑2223‑y 32272481
    [Google Scholar]
  7. Ullrich S. Nitsche C. The SARS-CoV-2 main protease as drug target. Bioorg. Med. Chem. Lett. 2020 30 17 127377 10.1016/j.bmcl.2020.127377
    [Google Scholar]
  8. Zagórska A. Czopek A. Fryc M. Jończyk J. Inhibitors of SARS-CoV-2 main protease (Mpro) as anti-coronavirus agents. Biomolecules 2024 14 7 797 10.3390/biom14070797 39062511
    [Google Scholar]
  9. Liu H. Ye F. Sun Q. Scutellaria baicalensis extract and baicalein inhibit replication of SARS-CoV-2 and its 3C-like protease in vitro. J. Enzyme Inhib. Med. Chem. 2021 36 1 497 503 10.1080/14756366.2021.1873977 33491508
    [Google Scholar]
  10. Bahun M Jukic M Oblak D Inhibition of the SARS-CoV-23CLpro main protease by plant polyphenols. Food Chem 2022 2022 373 (Pt B) 131594 10.1016/j.foodchem.2021.131594
    [Google Scholar]
  11. Hammond J. Leister-Tebbe H. Gardner A. Oral nirmatrelvir for high-risk, nonhospitalized adults with covid-19. N. Engl. J. Med. 2022 386 15 1397 1408 10.1056/NEJMoa2118542 35172054
    [Google Scholar]
  12. Robinson P. Toussi S.S. Aggarwal S. Safety, tolerability, and pharmacokinetics of single and multiple ascending intravenous infusions of pf-07304814 (lufotrelvir) in participants hospitalized with COVID-19. Open Forum Infect. Dis. 2023 10 8 ofad355 10.1093/ofid/ofad355 37559753
    [Google Scholar]
  13. Allerton C.M.N. Arcari J.T. Aschenbrenner L.M. A second-generation oral SARS-CoV-2 main protease inhibitor clinical candidate for the treatment of COVID-19. J. Med. Chem. 2024 67 16 13550 13571 10.1021/acs.jmedchem.3c02469 38687966
    [Google Scholar]
  14. Zhang H. Zhou J. Chen H. Phase I study, and dosing regimen selection for a pivotal COVID-19 trial of GST-HG171. Antimicrob. Agents Chemother. 2024 68 1 e01115 e01123 10.1128/aac.01115‑23 38099673
    [Google Scholar]
  15. Yang X.M. Yang Y. Yao B.F. A first-in-human phase 1 study of simnotrelvir, a 3CL-like protease inhibitor for treatment of COVID-19, in healthy adult subjects. Eur. J. Pharm. Sci. 2023 191 106598 10.1016/j.ejps.2023.106598 37783378
    [Google Scholar]
  16. Unoh Y. Uehara S. Nakahara K. Discovery of S-217622, a noncovalent oral SARS-CoV-2 3CL protease inhibitor clinical candidate for treating COVID-19. J. Med. Chem. 2022 65 9 6499 6512 10.1021/acs.jmedchem.2c00117 35352927
    [Google Scholar]
  17. Yotsuyanagi H. Ohmagari N. Doi Y. Efficacy and safety of 5-day oral ensitrelvir for patients with mild to moderate COVID-19. JAMA Netw. Open 2024 7 2 2354991 10.1001/jamanetworkopen.2023.54991 38335000
    [Google Scholar]
  18. Hou N. Shuai L. Zhang L. Development of highly potent noncovalent inhibitors of SARS-CoV-2 3CLpro. ACS Cent. Sci. 2023 9 2 217 227 10.1021/acscentsci.2c01359 36844503
    [Google Scholar]
  19. Mao L. Shaabani N. Zhang X. Olgotrelvir, a dual inhibitor of SARS-CoV-2 Mpro and cathepsin L, as a standalone antiviral oral intervention candidate for COVID-19. Med (N. Y.) 2024 5 1 42 61.e23 10.1016/j.medj.2023.12.004 38181791
    [Google Scholar]
  20. Sorrento announces phase 3 trial met primary endpoint and key secondary endpoint in mild or moderate covid-19 adult patients treated with ovydso (olgotrelvir), an oral mpro inhibitor as a standalone treatment for COVID-19. 2023 Available from: https://www.biospace.com/sorrento-announces-phase-3-trial-met-primary-endpoint-and-key-secondary-endpoint-in-mild-or-moderate-covid-19-adult-patients-treated-with-ovydso-olgotrelvir-an-oral-mpro-inhibitor-as-a-standalone-treatment-for-covid-19
  21. Yamamoto K.Z. Yasuo N. Sekijima M. Screening for inhibitors of main protease in SARS-CoV-2: In silico and in vitro approach avoiding peptidyl secondary amides. J. Chem. Inf. Model. 2022 62 2 350 358 10.1021/acs.jcim.1c01087 35015543
    [Google Scholar]
  22. Dong J. Varbanov M. Philippot S. Vreken F. Zeng W. Blay V. Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings. J. Enzyme Inhib. Med. Chem. 2023 38 1 24 35 10.1080/14756366.2022.2132486 36305272
    [Google Scholar]
  23. Khanfar M.A. Saleh M. Virtual screening identifies inhibitors of sars-cov-2 main protease through pharmacophore and similarity approaches. Curr. Pharm. Des. 2025 31 1 8 10.2174/0113816128358219241210101947 39757683
    [Google Scholar]
  24. Jing L. Zhao F. Zheng L. Optimization of SARS-CoV-2 Mpro inhibitors by a structure-based multilevel virtual screening method. Int. J. Mol. Sci. 2025 26 2 670 10.3390/ijms26020670 39859382
    [Google Scholar]
  25. de Prost N. Audureau E. Heming N. Clinical phenotypes and outcomes associated with SARS-CoV-2 variant Omicron in critically ill French patients with COVID-19. Nat. Commun. 2022 13 1 6025 10.1038/s41467‑022‑33801‑z 36224216
    [Google Scholar]
  26. Zatovkaňuková P. Veselý D. Slíva J. Evaluating drug interaction risks: Nirmatrelvir & ritonavir combination (PAXLOVID®) with concomitant medications in real-world clinical settings. Pathogens 2024 13 12 1055 10.3390/pathogens13121055 39770315
    [Google Scholar]
  27. Zhuang W. Xu J. Wu Y. Post‐marketing safety concerns with nirmatrelvir: A disproportionality analysis of spontaneous reports submitted to the FDA Adverse Event Reporting System. Br. J. Clin. Pharmacol. 2023 89 9 2830 2842 10.1111/bcp.15783 37170890
    [Google Scholar]
  28. Kitamura N. Sacco M.D. Ma C. Expedited approach toward the rational design of noncovalent SARS-CoV-2 main protease inhibitors. J. Med. Chem. 2022 65 4 2848 2865 10.1021/acs.jmedchem.1c00509 33891389
    [Google Scholar]
  29. Ghosh A.K. Raghavaiah J. Shahabi D. Indole chloropyridinyl ester-derived sars-cov-2 3clpro inhibitors: Enzyme inhibition, antiviral efficacy, structure-activity relationship, and x-ray structural studies. J. Med. Chem. 2021 64 19 14702 14714 10.1021/acs.jmedchem.1c01214
    [Google Scholar]
  30. Di Sarno V. Lauro G. Musella S. Identification of a dual acting SARS-CoV-2 proteases inhibitor through in silico design and step-by-step biological characterization. Eur. J. Med. Chem. 2021 226 113863 10.1016/j.ejmech.2021.113863 34571172
    [Google Scholar]
  31. Xia Z. Sacco M. Hu Y. Rational design of hybrid SARS-COV-2 main protease inhibitors guided by the superimposed cocrystal structures with the peptidomimetic inhibitors gc-376, telaprevir, and boceprevir. ACS Pharmacol. Transl. Sci. 2021 4 4 1408 1421 10.1021/acsptsci.1c00099 34414360
    [Google Scholar]
  32. Dampalla C.S. Rathnayake A.D. Galasiti Kankanamalage A.C. Structure-guided design of potent spirocyclic inhibitors of severe acute respiratory syndrome coronavirus-2 3c-like protease. J. Med. Chem. 2022 65 11 7818 7832 10.1021/acs.jmedchem.2c00224 35638577
    [Google Scholar]
  33. Tan H. Hu Y. Jadhav P. Tan B. Wang J. Progress and challenges in targeting the SARS-COV-2 papain-like protease. J. Med. Chem. 2022 65 11 7561 7580 10.1021/acs.jmedchem.2c00303 35620927
    [Google Scholar]
  34. Han S.H. Goins C.M. Arya T. Structure-based optimization of ML300-derived, noncovalent inhibitors targeting the severe acute respiratory syndrome coronavirus 3CL protease (SARS-CoV-2 3CL pro). J. Med. Chem. 2022 65 4 2880 2904 10.1021/acs.jmedchem.1c00598 34347470
    [Google Scholar]
  35. Konno S. Kobayashi K. Senda M. 3CL protease inhibitors with an electrophilic arylketone moiety as anti-SARS-CoV-2 agents. J. Med. Chem. 2022 65 4 2926 2939 10.1021/acs.jmedchem.1c00665 34313428
    [Google Scholar]
  36. Bai B. Belovodskiy A. Hena M. Peptidomimetic α-acyloxymethylketone warheads with six-membered lactam p1 glutamine mimic: SARS-CoV-2 3CL protease inhibition, coronavirus antiviral activity, and in vitro biological stability. J. Med. Chem. 2022 65 4 2905 2925 10.1021/acs.jmedchem.1c00616 34242027
    [Google Scholar]
  37. Stille J.K. Tjutrins J. Wang G. Design, synthesis and in vitro evaluation of novel SARS-CoV-2 3CLpro covalent inhibitors. Eur. J. Med. Chem. 2022 229 114046 10.1016/j.ejmech.2021.114046 34995923
    [Google Scholar]
  38. Luttens A. Gullberg H. Abdurakhmanov E. Ultralarge virtual screening identifies SARS-COV-2 main protease inhibitors with broad-spectrum activity against coronaviruses. J. Am. Chem. Soc. 2022 144 7 2905 2920 10.1021/jacs.1c08402 35142215
    [Google Scholar]
  39. Chen W. Feng B. Han S. Discovery of highly potent SARS-CoV-2 Mpro inhibitors based on benzoisothiazolone scaffold. Bioorg. Med. Chem. Lett. 2022 58 128526 10.1016/j.bmcl.2022.128526 34998903
    [Google Scholar]
  40. Khanfar M.A. Salaas N. Abumostafa R. Discovery of natural‐derived M pro inhibitors as therapeutic candidates for COVID‐19: Structure‐based pharmacophore screening combined with QSAR analysis. Mol. Inform. 2023 42 4 2200198 10.1002/minf.202200198 36762567
    [Google Scholar]
  41. Khanfar M.A. Structure‐based pharmacophore screening coupled with qsar analysis identified potent natural‐product‐derived IRAK‐4 inhibitors. Mol. Inform. 2021 40 12 2100025 10.1002/minf.202100025 34427398
    [Google Scholar]
  42. Khanfar M.A. Alqtaishat S. Discovery of potent natural-product-derived sirt2 inhibitors using structure- based exploration of SIRT2 pharmacophoric space coupled with QSAR analyses. Anticancer. Agents Med. Chem. 2021 21 16 2278 2286 10.2174/1871520621666210112121523 33438557
    [Google Scholar]
  43. Jones G. Willett P. Glen R.C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 1995 245 1 43 53 10.1016/S0022‑2836(95)80037‑9 7823319
    [Google Scholar]
  44. Wolber G. Dornhofer A.A. Langer T. Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput. Aided Mol. Des. 2007 20 12 773 788 10.1007/s10822‑006‑9078‑7 17051340
    [Google Scholar]
  45. Khanfar M.A. Al-Qtaishat S. Habash M. Taha M.O. Discovery of potent adenosine A2a antagonists as potential anti-Parkinson disease agents. Non-linear QSAR analyses integrated with pharmacophore modeling. Chem. Biol. Interact. 2016 254 93 101 10.1016/j.cbi.2016.05.023 27216633
    [Google Scholar]
  46. Khanfar M.A. Banat F. Alabed S. Alqtaishat S. Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses. Mol. Divers. 2017 21 1 187 200 10.1007/s11030‑016‑9696‑5 27599492
    [Google Scholar]
  47. Khanfar M.A. Alqtaishat S. Discovery of potent IRAK-4 inhibitors as potential anti-inflammatory and anticancer agents using structure-based exploration of IRAK-4 pharmacophoric space coupled with QSAR analyses. Comput. Biol. Chem. 2019 79 147 154 10.1016/j.compbiolchem.2019.02.005 30818109
    [Google Scholar]
  48. Mysinger M.M. Carchia M. Irwin J.J. Shoichet B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem. 2012 55 14 6582 6594 10.1021/jm300687e 22716043
    [Google Scholar]
  49. Bashatwah R.M. Khanfar M.A. Bardaweel S.K. Discovery of potent polyphosphate kinase 1 (PPK1) inhibitors using structure‐based exploration of PPK1Pharmacophoric space coupled with docking analyses. J. Mol. Recognit. 2018 31 10 2726 10.1002/jmr.2726 29740895
    [Google Scholar]
  50. Hawkins P.C.D. Skillman A.G. Nicholls A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem. 2007 50 1 74 82 10.1021/jm0603365 17201411
    [Google Scholar]
  51. Šribar D. Grabowski M. Murgueitio M.S. Bermudez M. Weindl G. Wolber G. Identification and characterization of a novel chemotype for human TLR8 inhibitors. Eur. J. Med. Chem. 2019 179 744 752 10.1016/j.ejmech.2019.06.084 31284084
    [Google Scholar]
  52. Alabed S.J. Khanfar M. Taha M.O. Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation. Future Med. Chem. 2016 8 15 1841 1869 10.4155/fmc‑2016‑0056 27643626
    [Google Scholar]
  53. Khanfar M.A. Youssef D.T.A. El Sayed K.A. 3D-QSAR studies of latrunculin-based actin polymerization inhibitors using CoMFA and CoMSIA approaches. Eur. J. Med. Chem. 2010 45 9 3662 3668 10.1016/j.ejmech.2010.05.012 20684858
    [Google Scholar]
  54. Janežič M. Valjavec K. Loboda K.B. Dynophore-based approach in virtual screening: A case of human DNA topoisomerase IIA. Int. J. Mol. Sci. 2021 22 24 13474 10.3390/ijms222413474 34948269
    [Google Scholar]
  55. Gossen J. Albani S. Hanke A. A blueprint for high affinity SARS-COV-2 mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics. ACS Pharmacol. Transl. Sci. 2021 4 3 1079 1095 10.1021/acsptsci.0c00215 34136757
    [Google Scholar]
  56. McGaughey G.B. Sheridan R.P. Bayly C.I. Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model. 2007 47 4 1504 1519 10.1021/ci700052x 17591764
    [Google Scholar]
  57. Su H. Yao S. Zhao W. Anti-SARS-CoV-2 activities in vitro of Shuanghuanglian preparations and bioactive ingredients. Acta Pharmacol. Sin. 2020 41 9 1167 1177 10.1038/s41401‑020‑0483‑6 32737471
    [Google Scholar]
  58. Umashankar V. Deshpande S.H. Hegde H.V. Singh I. Chattopadhyay D. Phytochemical moieties from indian traditional medicine for targeting dual hotspots on SARS-CoV-2 spike protein: An integrative in-silico approach. Front. Med. 2021 8 672629 10.3389/fmed.2021.672629 34026798
    [Google Scholar]
/content/journals/cei/10.2174/0115734080371991250605114818
Loading
Discovery of SARS-CoV-2 Main Protease Inhibitors from Natural Products via Machine Learning with Pharmacophore Modeling, Similarity Methods, and Molecular Dynamics
Bentham Science Publishers ; https://doi.org/10.2174/0115734080371991250605114818
/content/journals/cei/10.2174/0115734080371991250605114818
/content/journals/cei/10.2174/0115734080371991250605114818
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: dynophores ; Mpro ; covid-19 ; virtual screening ; docking ; pharmacophore

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test