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Ligand-based virtual screening, molecular docking, and molecular dynamics of eugenol analogs as potential acetylcholinesterase inhibitors with biological activity against Spodoptera frugiperda

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Abstract

The development of new, more selective, environmental-friendly insecticide alternatives is in high demand for the control of Spodoptera frugiperda (S. frugiperda). The major objective of this work was to search for new potential S. frugiperda acetylcholinesterase (AChE) inhibitors. A ligand-based virtual screening was initially carried out considering six scaffolds derived from eugenol and the ZINC15, PubChem, and MolPort databases. Subsequently, molecular docking analysis of the selected compounds on the active site and a second region (determined by blind molecular docking) of the AChE of S. frugiperda was performed. Molecular dynamics and Molecular Mechanics Poisson–Boltzmann Surface Area analyses were also applied to improve the docking results. Finally, three new eugenol analogs were evaluated in vitro against S. frugiperda larvae. The virtual screening identified 1609 compounds from the chemical libraries. Control compounds were selected from the interaction fingerprint by molecular docking. Only three new eugenol analogs (1, 3, and 4) were stable at 50 ns by molecular dynamics. Compounds 1 and 4 had the best biological activity by diet (LC50 = 0.042 mg/mL) and by topical route (LC50 = 0.027 mg/mL), respectively. At least three new eugenol derivatives possessed good-to-excellent insecticidal activity against S. frugiperda.

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References

  1. EPPO (2020) European and Mediterranean Plant Protection Organization. Spodoptera frugiperda. https://gd.eppo.int/taxon/LAPHFR. Accessed 15 Mar 2020

  2. FAO (2020) Food and Agriculture Organization of the United Nations. FAOSTAT, Data, Production, Crops. http://www.fao.org/faostat/en/#compare. Accessed 08 Nov 2020

  3. Blanco CA, Pellegaud JG, Nava-Camberos U, Lugo-Barrera D, Vega-Aquino P, Coello J et al (2014) Maize pests in Mexico and challenges for the adoption of integrated pest management programs. J Integr Pest Manag 5(4):E1–E9. https://doi.org/10.1603/IPM14006

    Article  Google Scholar 

  4. IRAC (2020) Lepidoptera Insecticide Mode of Action Classification: A key to effective insecticide resistance management. https://irac-online.org/lepidoptera/. Accessed 03 Sept 2020

  5. Sparks TC, Nauen R (2015) IRAC: mode of action classification and insecticide resistance management. Pest Biochem Physiol 121:122–128. https://doi.org/10.1016/j.pestbp.2014.11.014

    Article  CAS  Google Scholar 

  6. Prasanna B, Huesing J, Eddy R, Peschke V (2018) Fall armyworm in Africa: a guide for integrated pest management, 1st edn. CIMMYT, Mexico, CDMX

    Google Scholar 

  7. Dvir H, Silman I, Harel M, Rosenberry TL, Sussman JL (2010) Acetylcholinesterase: from 3D structure to function. Chem Biol Interact 187(1–3):10–22. https://doi.org/10.1016/j.cbi.2010.01.042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hernández-Carlos B, Gamboa-Angulo M (2019) Insecticidal and nematicidal contributions of Mexican Flora in the search for safer biopesticides. Molecules 24(5):897. https://doi.org/10.3390/molecules24050897

    Article  CAS  PubMed Central  Google Scholar 

  9. Cruz GS, Wanderley-Teixeira V, Oliveira JV, D’assunção CG, Cunha FM, Teixeira ÁA et al (2017) Effect of trans-anethole, limonene and your combination in nutritional components and their reflection on reproductive parameters and testicular apoptosis in Spodoptera frugiperda (Lepidoptera: Noctuidae). Chem Biol Interact 263:74–80. https://doi.org/10.1016/j.cbi.2016.12.013

    Article  CAS  PubMed  Google Scholar 

  10. Melani D, Himawan T, Afandhi A (2016) Bioactivity of sweet flag (Acorus calamus Linnaeus) essential oils against Spodoptera litura Fabricius (Lepidoptera: Noctuidae). J Trop Life Sci 6(2):86–90. https://doi.org/10.11594/jtls.06.02.04

    Article  Google Scholar 

  11. Menichini F, Tundis R, Loizzo MR, Bonesi M, Marrelli M, Statti GA et al (2009) Acetylcholinesterase and butyrylcholinesterase inhibition of ethanolic extract and monoterpenes from Pimpinella anisoides V Brig. (Apiaceae). Fitoterapia 80(5):297–300. https://doi.org/10.1016/j.fitote.2009.03.008

    Article  CAS  PubMed  Google Scholar 

  12. Vargas-Méndez LY, Sanabria-Flórez PL, Saavedra-Reyes LM, Merchan-Arenas DR, Kouznetsov VV (2018) Bioactivity of semisynthetic eugenol derivatives against Spodoptera frugiperda (Lepidoptera: Noctuidae) larvae infesting maize in Colombia. Saudi J Biol Sci. https://doi.org/10.1016/j.sjbs.2018.09.010

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mukherjee PK, Kumar V, Mal M, Houghton PJ (2007) In vitro acetylcholinesterase inhibitory activity of the essential oil from Acorus calamus and its main constituents. Planta Med 73(03):283–285. https://doi.org/10.1055/s-2007-967114

    Article  CAS  PubMed  Google Scholar 

  14. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3(1):33. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tice CM (2001) Selecting the right compounds for screening: does Lipinski’s Rule of 5 for pharmaceuticals apply to agrochemicals? Pest Manag Sci Former Pest Sci 57(1):3–16. https://doi.org/10.1002/1526-4998(200101)57:1%3c3::AID-PS269%3e3.0.CO;2-6

    Article  CAS  Google Scholar 

  16. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303. https://doi.org/10.1093/nar/gky427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26(2):283–291. https://doi.org/10.1107/S0021889892009944

    Article  CAS  Google Scholar 

  18. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612. https://doi.org/10.1002/jcc.20084

    Article  CAS  PubMed  Google Scholar 

  19. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ (2016) Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc 11(5):905–919. https://doi.org/10.1038/nprot.2016.051

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. ChemAxon (2020) Marvin. https://chemaxon.com/products/marvin. Accessed 03 Feb 2020

  22. Seeliger D, de Groot BL (2010) Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des 24(5):417–422. https://doi.org/10.1007/s10822-010-9352-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wójcikowski M, Zielenkiewicz P, Siedlecki P (2015) Open drug discovery toolkit (ODDT): a new open-source player in the drug discovery field. J Cheminform 7(1):1–6. https://doi.org/10.1186/s13321-015-0078-2

    Article  Google Scholar 

  24. Chupakhin V, Marcou G, Gaspar H, Varnek A (2014) Simple ligand-receptor interaction descriptor (SILIRID) for alignment-free binding site comparison. Comput Struct Biotechnol J 10(16):33–37. https://doi.org/10.1016/j.csbj.2014.05.004

    Article  PubMed  PubMed Central  Google Scholar 

  25. Baptista LPR, Sinatti VV, Da Silva JH, Dardenne LE, Guimarães AC (2019) Computational evaluation of natural compounds as potential inhibitors of human PEPCK-M: an alternative for lung cancer therapy. Adv Appl Bioinform Chem 12:15. https://doi.org/10.2147/AABC.S197119

    Article  PubMed  PubMed Central  Google Scholar 

  26. Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M (2015) PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res 43(W1):W443–W447. https://doi.org/10.1093/nar/gkv315

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  28. Dodda LS, Cabeza de Vaca I, Tirado-Rives J, Jorgensen WL (2017) LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res 45(W1):W331–W336. https://doi.org/10.1093/nar/gkx312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Polishchuk P, Kutlushina A, Bashirova D, Mokshyna O, Madzhidov T (2019) Virtual screening using pharmacophore models retrieved from molecular dynamic simulations. Int J Mol Sci 20(23):5834. https://doi.org/10.3390/ijms20235834

    Article  CAS  PubMed Central  Google Scholar 

  30. Lemkul J (2018) From proteins to perturbed Hamiltonians: a suite of tutorials for the GROMACS-2018 molecular simulation package [article v1. 0]. Living J Comput Mole Sci 1(1):5068. https://doi.org/10.33011/livecoms.1.1.5068

    Article  Google Scholar 

  31. Kumari R, Kumar R, Lynn A (2014) g_mmpbsa—A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54(7):1951–62. https://doi.org/10.1021/ci500020m

    Article  CAS  PubMed  Google Scholar 

  32. Liu J-Y, Chen X-E, Zhang Y-L (2015) Insights into the key interactions between human protein phosphatase 5 and cantharidin using molecular dynamics and site-directed mutagenesis bioassays. Sci Rep 5(1):1–11. https://doi.org/10.1038/srep12359

    Article  CAS  Google Scholar 

  33. Rosado-Solano DN, Sanabria-Florez PL, Barón-Rodríguez MA, Luna-Parada LK, Puerto Galvis CE, Zorro-González AF et al (2019) Synthesis, biological evaluation and in silico computational studies of 7-Chloro-4-(1H–1, 2, 3-triazol-1-yl) quinoline derivatives. Search for new controlling agents against Spodoptera frugiperda (Lepidoptera: Noctuidae) larvae. J Agric Food Chem. https://doi.org/10.1021/acs.jafc.9b01067

    Article  PubMed  Google Scholar 

  34. Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27(3):343–350. https://doi.org/10.1093/bioinformatics/btq662

    Article  CAS  PubMed  Google Scholar 

  35. Velankar S, Alhroub Y, Best C, Caboche S, Conroy MJ, Dana JM, et al (2012) PDBe: Protein Data Bank in Europe. Nucleic Acids Res. https://doi.org/10.1093/nar/gkr998

    Article  PubMed  PubMed Central  Google Scholar 

  36. Haddad Y, Adam V, Heger Z (2020) Ten quick tips for homology modeling of high-resolution protein 3D structures. PLoS Comput Biol 16(4):e1007449. https://doi.org/10.1371/journal.pcbi.1007449

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Xiang Z (2006) Advances in homology protein structure modeling. Current Protein Peptide Sci 7(3):217–227. https://doi.org/10.2174/138920306777452312

    Article  CAS  Google Scholar 

  38. Fortney K, Griesman J, Kotlyar M, Pastrello C, Angeli M, Sound-Tsao M et al (2015) Prioritizing therapeutics for lung cancer: an integrative meta-analysis of cancer gene signatures and chemogenomic data. PLoS Comput Biol 11(3):e1004068. https://doi.org/10.1371/journal.pcbi.1004068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nguyen NT, Nguyen TH, Pham TNH, Huy NT, Bay MV, Pham MQ et al (2019) Autodock vina adopts more accurate binding poses but autodock4 forms better binding affinity. J Chem Inf Model 60(1):204–211. https://doi.org/10.1021/acs.jcim.9b00778

    Article  CAS  Google Scholar 

  40. Bhowmik D, Jagadeesan R, Rai P, Nandi R, Gugan K, Kumar D (2020) Evaluation of potential drugs against leishmaniasis targeting catalytic subunit of Leishmania donovani nuclear DNA primase using ligand based virtual screening, docking and molecular dynamics approaches. J Biomole Struct Dyn. https://doi.org/10.1080/07391102.2020.1739557

    Article  Google Scholar 

  41. Kumari M, Subbarao N (2020) Virtual screening to identify novel potential inhibitors for Glutamine synthetase of Mycobacterium tuberculosis. J Biomole Struct Dyn 38(17):5062–5080. https://doi.org/10.1080/07391102.2019.1695670

    Article  CAS  Google Scholar 

  42. Liao KH, Chen K-B, Lee W-Y, Sun M-F, Lee C-C, Chen CY-C (2014) Ligand-based and structure-based investigation for Alzheimer’s disease from traditional Chinese medicine. Evidence-Based Complement Altern Med. https://doi.org/10.1155/2014/364819

    Article  Google Scholar 

  43. Rampogu S, Son M, Park C, Kim H-H, Suh J-K, Lee KW (2017) Sulfonanilide derivatives in identifying novel aromatase inhibitors by applying docking, virtual screening, and MD simulations studies. Biomed Res Int. https://doi.org/10.1155/2017/2105610

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhang X, Yan J, Wang H, Wang Y, Wang J, Zhao D (2020) Molecular docking, 3D-QSAR, and molecular dynamics simulations of thieno [3, 2-b] pyrrole derivatives against anticancer targets of KDM1A/LSD1. J Biomole Struct Dyn. https://doi.org/10.1080/07391102.2020.1726819

    Article  Google Scholar 

  45. Torres P, Avila JG, de Vivar AR, Garcia AM, Marin JC, Aranda E et al (2003) Antioxidant and insect growth regulatory activities of stilbenes and extracts from Yucca periculosa. Phytochemistry 64(2):463–73. https://doi.org/10.1016/S0031-9422(03)00348-0

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, 88710, Reynosa, Tamaulipas, México

    Domingo Méndez-Álvarez, Alfredo Juárez-Saldivar, Alma D. Paz-González, Eyra Ortiz-Pérez & Gildardo Rivera

  2. Departamento de Ingeniería Bioquímica, Unidad Académica Multidisciplinaria Mante, Universidad Autónoma de Tamaulipas, 89840, Mante, Tamaulipas, México

    Verónica Herrera-Mayorga

  3. Department of Chemistry and SEEMS, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA

    Debasish Bandyopadhyay

  4. Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio De Murcia (UCAM), 30107, Murcia, Spain

    Horacio Pérez-Sánchez

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  1. Domingo Méndez-Álvarez
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Méndez-Álvarez, D., Herrera-Mayorga, V., Juárez-Saldivar, A. et al. Ligand-based virtual screening, molecular docking, and molecular dynamics of eugenol analogs as potential acetylcholinesterase inhibitors with biological activity against Spodoptera frugiperda. Mol Divers 26, 2025–2037 (2022). https://doi.org/10.1007/s11030-021-10312-5

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