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2000
Volume 21, Issue 10
  • ISSN: 1573-4064
  • E-ISSN: 1875-6638

Abstract

Background

We continue to struggle with the prevention and treatment of the influenza virus. The 2009 swine flu pandemic, caused by the H1N1 strain of influenza A, resulted in numerous fatalities. The threat of influenza remains a significant concern for global health, and the development of novel drugs targeting these viruses is highly desirable.

Objective

The objective of this study is to explore the inhibitory potential of terpenoid compounds against the Nucleoprotein (NP) of influenza A virus, which is a highly effective drug target due to its ability to facilitate the transcription and replication of viral RNA.

Methods

research was performed to identify potential inhibitors of NP. Molecular docking studies were conducted to assess the binding of terpenoid compounds to the active site residues of the target protein. The most promising hits were then subjected to molecular dynamics simulations to examine the stability of the protein-ligand complexes. Additionally, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) studies and Lipinski's rule of five were employed to evaluate the drug safety and druglikeness of the compounds.

Results

Docking studies revealed that the terpenoid compounds bind strongly to the active site residues of the NP protein. Molecular dynamics simulations demonstrated the stability of the protein-ligand complexes for the best-hit compounds. ADMET studies and Lipinski's filter indicated that the compounds exhibit desirable drug safety and drug-likeness profiles.

Conclusion

This work may contribute significantly to drug discovery and the development of therapeutic agents against the influenza A virus. The identification of terpenoid compounds that bind strongly to the NP protein and exhibit favorable drug-like properties through studies provides a promising foundation for further research and the development of potential inhibitors targeting this critical viral protein.

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References

  1. HsiehY.C. WuT.Z. LiuD.P. ShaoP.L. ChangL.Y. LuC.Y. LeeC.Y. HuangF.Y. HuangL.M. Influenza pandemics: Past, present and future.J. Formos. Med. Assoc.200610511610.1016/S0929‑6646(09)60102‑9 16440064
    [Google Scholar]
  2. NeumannG. NodaT. KawaokaY. Timeline of swine-origin H1N1 virus outbreak.Nature20094591893193910.1038/nature08157 19525932
    [Google Scholar]
  3. RahmanS. HasanM. AlamM.S. UddinK.M.M. MoniS. RahmanM. The evolutionary footprint of influenza A subtype H3N2 strains in Bangladesh: Implication of vaccine strain selection.Sci. Rep.20221211618610.1038/s41598‑022‑20179‑7 36171388
    [Google Scholar]
  4. PaleseP. Ohomyxoviridae: The viruses and their replication.Fields Virology200716471689
    [Google Scholar]
  5. WebsterR.G. MontoA.S. BracialeT.J. LambR.A. Textbook of influenza.John Wiley & Sons2014
    [Google Scholar]
  6. HuY. SneydH. DekantR. WangJ. Influenza A virus nucleoprotein: A highly conserved multi-functional viral protein as a hot antiviral drug target.Curr. Top. Med. Chem.2017172022712285 28240183
    [Google Scholar]
  7. Sasaki-TanakaR. ShibataT. MoriyamaM. OkamotoH. KogureH. KandaT. Amantadine and rimantadine inhibit hepatitis a virus replication through the induction of autophagy.J. Virol.20229618e00646e2210.1128/jvi.00646‑22 36040176
    [Google Scholar]
  8. McKimm-BreschkinJ.L. Influenza neuraminidase inhibitors: Antiviral action and mechanisms of resistance.Influenza Other Respir. Viruses20137s1253610.1111/irv.12047 23279894
    [Google Scholar]
  9. von ItzsteinM. The war against influenza: Discovery and development of sialidase inhibitors.Nat. Rev. Drug Discov.200761296797410.1038/nrd2400 18049471
    [Google Scholar]
  10. HaydenF.G. de JongM.D. Emerging influenza antiviral resistance threatsOxford University Press2011203610
    [Google Scholar]
  11. HurtA.C. The epidemiology and spread of drug resistant human influenza viruses.Curr. Opin. Virol.20148222910.1016/j.coviro.2014.04.009 24866471
    [Google Scholar]
  12. IsonM.G. Antivirals and resistance: Influenza virus.Curr. Opin. Virol.20111656357310.1016/j.coviro.2011.09.002 22440914
    [Google Scholar]
  13. HaydenF.G. Newer influenza antivirals, biotherapeutics and combinations.Influenza Other Respir. Viruses20137s1Suppl. 1637510.1111/irv.12045 23279899
    [Google Scholar]
  14. De ClercqE. Antiviral agents active against influenza A viruses.Nat. Rev. Drug Discov.20065121015102510.1038/nrd2175 17139286
    [Google Scholar]
  15. PintoL.H. LambR.A. The M2 proton channels of influenza A and B viruses.J. Biol. Chem.2006281148997900010.1074/jbc.R500020200 16407184
    [Google Scholar]
  16. HerzC. StavnezerE. KrugR.M. GurneyT.Jr. Influenza virus, an RNA virus, synthesizes its messenger RNA in the nucleus of infected cells.Cell198126339140010.1016/0092‑8674(81)90208‑7 7326745
    [Google Scholar]
  17. JacksonD.A. CatonA.J. McCreadyS.J. CookP.R. Influenza virus RNA is synthesized at fixed sites in the nucleus.Nature1982296585536636810.1038/296366a0 7063035
    [Google Scholar]
  18. ShiF. XieY. ShiL. XuW. Viral RNA polymerase: A promising antiviral target for influenza A virus.Curr. Med. Chem.201320313923393410.2174/09298673113209990208 23931274
    [Google Scholar]
  19. LeeS.M.Y. YenH.L. Targeting the host or the virus: Current and novel concepts for antiviral approaches against influenza virus infection.Antiviral Res.201296339140410.1016/j.antiviral.2012.09.013 23022351
    [Google Scholar]
  20. MathurS. HoskinsC. Drug development: Lessons from nature.Biomed. Rep.20176661261410.3892/br.2017.909 28584631
    [Google Scholar]
  21. HermanA. HermanA.P. Essential oils and their constituents as skin penetration enhancer for transdermal drug delivery: A review.J. Pharm. Pharmacol.201567447348510.1111/jphp.12334 25557808
    [Google Scholar]
  22. GuimarãesA.G. SerafiniM.R. Quintans-JúniorL.J. Terpenes and derivatives as a new perspective for pain treatment: A patent review.Expert Opin. Ther. Pat.201424324326510.1517/13543776.2014.870154 24387185
    [Google Scholar]
  23. AngehJ.E. HuangX. SwanG.E. MollmanU. SattlerI. EloffJ.N. Novel antibacterial triterpenoid from Combretum padoides.ARKIVOC2007ix113120
    [Google Scholar]
  24. del Carmen RecioM. GinerR. MáñezS. RíosJ. Structural requirements for the anti-inflammatory activity of natural triterpenoids.Planta Med.199561218218510.1055/s‑2006‑958045 7753929
    [Google Scholar]
  25. NosratiM. BehbahaniM. Molecular docking study of HIV-1 protease with triterpenoides compounds from plants and mushroom. J. Arak.Uni. Med. Sci.20151836779
    [Google Scholar]
  26. TopcuG. ErtasA. KolakU. OzturkM. UlubelenA. Antioxidant activity tests on novel triterpenoids from Salvia macrochlamys.ARKIVOC20077195208
    [Google Scholar]
  27. AljarallahK.M. RahmanS. Identification of plant-based inhibitors by molecular docking against catalytic glucosyltransferase domains of TcdA and TcdB toxins from clostridium difficile.Int. J. Clin. Exp. Med.2021141226202630
    [Google Scholar]
  28. MoradiM. GolmohammadiR. NajafiA. Moosazadeh-MoghadamM. Fasihi-RamandiM. MirnejadR. A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis;Informatics in Medicine Unlocked2022100862
    [Google Scholar]
  29. WoutersO.J. McKeeM. LuytenJ. Estimated research and development investment needed to bring a new medicine to market, 2009-2018.JAMA2020323984485310.1001/jama.2020.1166 32125404
    [Google Scholar]
  30. WagnerK.H. ElmadfaI. Biological relevance of terpenoids. Overview focusing on mono-, di- and tetraterpenes.Ann. Nutr. Metab.2003473-49510610.1159/000070030 12743459
    [Google Scholar]
  31. WenC.C. KuoY.H. JanJ.T. LiangP.H. WangS.Y. LiuH.G. LeeC.K. ChangS.T. KuoC.J. LeeS.S. HouC.C. HsiaoP.W. ChienS.C. ShyurL.F. YangN.S. Specific plant terpenoids and lignoids possess potent antiviral activities against severe acute respiratory syndrome coronavirus.J. Med. Chem.200750174087409510.1021/jm070295s 17663539
    [Google Scholar]
  32. SinhaD. ChatterjeeM. ChoudhuryS. SealS. DasT. SharmaS. BanerjeeS. ChowdhuryS. Terpenes and terpenoids: Potent antiviral agents against SARS-CoV-2.In: Bioactive Compounds Against SARS-CoV-294110
    [Google Scholar]
  33. EswarN. WebbB. Marti‐RenomM.A. MadhusudhanM. EramianD. ShenM. PieperU. SaliA. Comparative protein structure modeling using Modeller.Curr. Protoc. Bioinformatics200615113010.1002/0471250953.bi0506s15
    [Google Scholar]
  34. HooftR.W.W. SanderC. VriendG. Objectively judging the quality of a protein structure from a Ramachandran plot.Bioinformatics199713442543010.1093/bioinformatics/13.4.425 9283757
    [Google Scholar]
  35. TianW. ChenC. LeiX. ZhaoJ. LiangJ. CASTp 3.0: Computed atlas of surface topography of proteins.Nucleic Acids Res.201846W1W363W36710.1093/nar/gky473 29860391
    [Google Scholar]
  36. WeakoJ. UbaA.I. KeskinÖ. GürsoyA. YelekçiK. Identification of potential inhibitors of human methionine aminopeptidase (type II) for cancer therapy: Structure-based virtual screening, ADMET prediction and molecular dynamics studies.Comput. Biol. Chem.20208610724410.1016/j.compbiolchem.2020.107244 32252002
    [Google Scholar]
  37. AkashS. BayılI. HossainM.S. IslamM.R. HosenM.E. MekonnenA.B. NafidiH.A. Bin JardanY.A. BourhiaM. Bin EmranT. Novel computational and drug design strategies for inhibition of human papillomavirus-associated cervical cancer and DNA polymerase theta receptor by Apigenin derivatives.Sci. Rep.20231311656510.1038/s41598‑023‑43175‑x 37783745
    [Google Scholar]
  38. AkashS. IslamM.R. RahmanM.M. HossainM.S. AzadM.A. SharmaR. Investigation of the new inhibitors by modified derivatives of pinocembrin for the treatment of monkeypox and marburg virus with different computational approaches.Biointerface Res. Appl. Chem.2023136534
    [Google Scholar]
  39. SharmaS. KumarP. ChandraR. Applications of BIOVIA materials studio, LAMMPS, and GROMACS in various fields of science and engineering. Molecular dynamics simulation of nanocomposites using BIOVIA materials studio.Lammps and Gromacs2019329341
    [Google Scholar]
  40. DallakyanS. OlsonA. J. Small-molecule library screening by docking with PyRx.Chem. Biol.: Methods Protoc.2015243250
    [Google Scholar]
  41. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.21334 19499576
    [Google Scholar]
  42. AbrahamM.J. MurtolaT. SchulzR. PállS. SmithJ.C. HessB. LindahlE. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  43. VanommeslaegheK. HatcherE. AcharyaC. KunduS. ZhongS. ShimJ. DarianE. GuvenchO. LopesP. VorobyovI. MackerellA.D.Jr CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields.J. Comput. Chem.201031467169010.1002/jcc.21367 19575467
    [Google Scholar]
  44. JiangX. LiS. ZhangH. WangL.L. Discovery of potentially biased agonists of mu-opioid receptor (MOR) through molecular docking, pharmacophore modeling, and MD simulation.Comput. Biol. Chem.20219010740510.1016/j.compbiolchem.2020.107405 33184004
    [Google Scholar]
  45. TurnerP. XMGRACE, Version 5.1. 19. Center for Coastal and Land-Margin Research.Beaverton, OROregon Graduate Institute of Science and Technology20052
    [Google Scholar]
  46. HameduhT. HaddadY. AdamV. HegerZ. Homology modeling in the time of collective and artificial intelligence.Comput. Struct. Biotechnol. J.2020183494350610.1016/j.csbj.2020.11.007 33304450
    [Google Scholar]
  47. BelzerM. MoralesM. JagadishB. MashE.A. WrightS.H. Substrate-dependent ligand inhibition of the human organic cation transporter OCT2.J. Pharmacol. Exp. Ther.2013346230031010.1124/jpet.113.203257 23709117
    [Google Scholar]
  48. GyebiG.A. AdegunloyeA.P. IbrahimI.M. OgunyemiO.M. AfolabiS.O. OgunroO.B. Prevention of SARS-CoV-2 cell entry: Insight from in silico interaction of drug-like alkaloids with spike glycoprotein, human ACE2, and TMPRSS2.J. Biomol. Struct. Dyn.20224052121214510.1080/07391102.2020.1835726 33089728
    [Google Scholar]
  49. AbdouA. MostafaH.M. Abdel-MawgoudA.M.M. Seven metal-based bi-dentate NO azocoumarine complexes: Synthesis, physicochemical properties, DFT calculations, drug-likeness, in vitro antimicrobial screening and molecular docking analysis.Inorg. Chim. Acta202253912104310.1016/j.ica.2022.121043
    [Google Scholar]
  50. LiT. GuoR. ZongQ. LingG. Application of molecular docking in elaborating molecular mechanisms and interactions of supramolecular cyclodextrin.Carbohydr. Polym.202227611864410.1016/j.carbpol.2021.118644 34823758
    [Google Scholar]
  51. HanssonT. OostenbrinkC. van GunsterenW. Molecular dynamics simulations.Curr. Opin. Struct. Biol.200212219019610.1016/S0959‑440X(02)00308‑1 11959496
    [Google Scholar]
  52. JainN. SkM.F. MishraA. KarP. KumarA. Identification of novel efflux pump inhibitors for Neisseria gonorrhoeae via multiple ligand-based pharmacophores, e-pharmacophore, molecular docking, density functional theory, and molecular dynamics approaches.Comput. Biol. Chem.20229810768210.1016/j.compbiolchem.2022.107682 35462198
    [Google Scholar]
  53. HeQ. HanC. LiG. GuoH. WangY. HuY. LinZ. WangY. In silico design novel (5-imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine derivatives as inhibitors for glycogen synthase kinase 3 based on 3D-QSAR, molecular docking and molecular dynamics simulation.Comput. Biol. Chem.20208810732810.1016/j.compbiolchem.2020.107328 32688011
    [Google Scholar]
  54. LiY. PengJ. LiP. DuH. LiY. LiuX. ZhangL. WangL.L. ZuoZ. Identification of potential AMPK activator by pharmacophore modeling, molecular docking and QSAR study.Comput. Biol. Chem.20197916517610.1016/j.compbiolchem.2019.02.007 30836318
    [Google Scholar]
  55. LeeG.R. ShinW.H. ParkH.B. ShinS.M. SeokC.O. Conformational sampling of flexible ligand-binding protein loops.Bull. Korean Chem. Soc.201233377077410.5012/bkcs.2012.33.3.770
    [Google Scholar]
  56. TeagueS.J. Implications of protein flexibility for drug discovery.Nat. Rev. Drug Discov.20032752754110.1038/nrd1129 12838268
    [Google Scholar]
  57. AliS. HassanM. IslamA. AhmadF. A review of methods available to estimate solvent-accessible surface areas of soluble proteins in the folded and unfolded states.Curr. Protein Pept. Sci.201415545647610.2174/1389203715666140327114232 24678666
    [Google Scholar]
  58. LobanovM.Y. BogatyrevaN.S. GalzitskayaO.V. Radius of gyration as an indicator of protein structure compactness.Mol. Biol.200842462362810.1134/S0026893308040195 18856071
    [Google Scholar]
  59. MenéndezC.A. AccordinoS.R. GerbinoD.C. AppignanesiG.A. Hydrogen bond dynamic propensity studies for protein binding and drug design.PLoS One20161110e016576710.1371/journal.pone.0165767 27792778
    [Google Scholar]
  60. PronkS. PállS. SchulzR. LarssonP. BjelkmarP. ApostolovR. ShirtsM.R. SmithJ.C. KassonP.M. van der SpoelD. HessB. LindahlE. GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit.Bioinformatics201329784585410.1093/bioinformatics/btt055 23407358
    [Google Scholar]
  61. SantosS.J.M. ValentiniA. In silico investigation of Komaroviquinone as a potential inhibitor of SARS-CoV-2 main protease (Mpro): Molecular docking, molecular dynamics, and QM/MM approaches.J. Mol. Graph. Model.202412610866210.1016/j.jmgm.2023.108662 37950976
    [Google Scholar]
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  • Article Type:
    Research Article
Keyword(s): ADMET; influenza A; MD simulations; molecular docking; nucleoprotein; Terpenoid
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