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Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase—a potential target to reduce Fusarium head blight disease


Fusarium head blight (FHB) is one of the most destructive diseases of wheat and other cereals worldwide. During infection, the Fusarium fungi produce mycotoxins that represent a high risk to human and animal health. Developing small-molecule inhibitors to specifically reduce mycotoxin levels would be highly beneficial since current treatments unspecifically target the Fusarium pathogen. Culmorin possesses a well-known important synergistically virulence role among mycotoxins, and longiborneol synthase appears to be a key enzyme for its synthesis, thus making longiborneol synthase a particularly interesting target. This study aims to discover potent and less toxic agrochemicals against FHB. These compounds would hamper culmorin synthesis by inhibiting longiborneol synthase. In order to select starting molecules for further investigation, we have conducted a structure-based virtual screening investigation. A longiborneol synthase structural model is first built using homology modeling, followed by molecular dynamics simulations that provided the required input for a protein–ligand ensemble docking procedure. From this strategy, the three most interesting compounds (hits) were selected among the 25 top-ranked docked compounds from a library of 15,000 drug-like compounds. These putative inhibitors of longiborneol synthase provide a sound starting point for further studies involving molecular modeling coupled to biochemical experiments. This process could eventually lead to the development of novel approaches to reduce mycotoxin contamination in harvested grain.

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  1. 1.

    Driehuis F, Oude Elferink SJ (2000) The impact of the quality of silage on animal health and food safety: a review. Vet Q 22:212–216. doi:10.1080/01652176.2000.9695061

    CAS  Article  Google Scholar 

  2. 2.

    Boenisch MJ, Schäfer W (2011) Fusarium graminearum forms mycotoxin producing infection structures on wheat. BMC Plant Biol 11:110. doi:10.1186/1471-2229-11-110

    CAS  Article  Google Scholar 

  3. 3.

    Shimshoni JA, Cuneah O, Sulyok M et al (2013) Mycotoxins in corn and wheat silage in Israel. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 30:1614–1625. doi:10.1080/19440049.2013.802840

    CAS  Article  Google Scholar 

  4. 4.

    Becher R, Hettwer U, Karlovsky P et al (2010) Adaptation of Fusarium graminearum to tebuconazole yielded descendants diverging for levels of fitness, fungicide resistance, virulence, and mycotoxin production. Phytopathology 100:444–453. doi:10.1094/PHYTO-100-5-0444

    CAS  Article  Google Scholar 

  5. 5.

    Cools HJ, Hammond-Kosack KE (2013) Exploitation of genomics in fungicide research: current status and future perspectives. Mol Plant Pathol 14:197–210. doi:10.1111/mpp.12001

    Article  Google Scholar 

  6. 6.

    Sweeney MJ, Dobson AD (1998) Mycotoxin production by Aspergillus, Fusarium and Penicillium species. Int J Food Microbiol 43:141–158

    CAS  Article  Google Scholar 

  7. 7.

    Langseth W, Ghebremeskel M, Kosiak B et al (2001) Production of culmorin compounds and other secondary metabolites by Fusarium culmorum and F. graminearum strains isolated from Norwegian cereals. Mycopathologia 152:23–34. doi:10.1023/A:1011964306510

    CAS  Article  Google Scholar 

  8. 8.

    Pedersen PB, David Miller J (1999) The fungal metabolite culmorin and related compounds. Nat Toxins 7:305–309. doi:10.1002/1522-7189(199911/12)7:6<305::AID-NT72>3.0.CO;2-G

    CAS  Article  Google Scholar 

  9. 9.

    Scarpino V, Reyneri A, Sulyok M et al (2015) Effect of fungicide application to control Fusarium head blight and 20 Fusarium and Alternaria mycotoxins in winter wheat (Triticum aestivum L.). World Mycotoxin J 8:499–510. doi:10.3920/WMJ2014.1814

    Article  Google Scholar 

  10. 10.

    Ghebremeskel M, Langseth W (2001) The occurrence of culmorin and hydroxy-culmorins in cereals. Mycopathologia 152:103–108. doi:10.1023/A:1012479823193

    CAS  Article  Google Scholar 

  11. 11.

    McCormick SP (2014) Genetic control of Fusarium mycotoxins to enhance food safety, Research project #421046, annual report

  12. 12.

    McCormick SP, Alexander NJ, Harris LJ (2010) CLM1 of Fusarium graminearum encodes a longiborneol synthase required for culmorin production. Appl Environ Microbiol 76:136–141. doi:10.1128/AEM.02017-09

    CAS  Article  Google Scholar 

  13. 13.

    Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860. doi:10.2174/09298673113209990001

    CAS  Article  Google Scholar 

  14. 14.

    Ma XH, Zhu F, Liu X et al (2012) Virtual screening methods as tools for drug lead discovery from large chemical libraries. Curr Med Chem 19:5562–5571. doi:10.2174/092986712803833245

    CAS  Article  Google Scholar 

  15. 15.

    Korb O, Olsson TSG, Bowden SJ et al (2012) Potential and limitations of ensemble docking. J Chem Inf Model 52:1262–1274. doi:10.1021/ci2005934

    CAS  Article  Google Scholar 

  16. 16.

    Bernstein FC, Koetzle TF, Williams GJ et al (1977) The protein data bank. A computer-based archival file for macromolecular structures. Eur J Biochem 80:319–324. doi:10.1016/S0022-2836(77)80200-3

    CAS  Article  Google Scholar 

  17. 17.

    Fernandez-Fuentes N, Rai BK, Madrid-Aliste CJ et al (2007) Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments. Bioinformatics 23:2558–2565. doi:10.1093/bioinformatics/btm377

    CAS  Article  Google Scholar 

  18. 18.

    Meier A, Söding J (2015) Automatic prediction of protein 3D structures by probabilistic multi-template homology modeling. PLoS Comput Biol 11, e1004343. doi:10.1371/journal.pcbi.1004343

    Article  Google Scholar 

  19. 19.

    Dereeper A, Guignon V, Blanc G et al (2008) robust phylogenetic analysis for the non-specialist. Nucleic Acids Res 36:465–469. doi:10.1093/nar/gkn180

    Article  Google Scholar 

  20. 20.

    Dereeper A, Audic S, Claverie J-M, Blanc G (2010) BLAST-EXPLORER helps you building datasets for phylogenetic analysis. BMC Evol Biol 10:8. doi:10.1186/1471-2148-10-8

    Article  Google Scholar 

  21. 21.

    Edgar RC (2004) MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797. doi:10.1093/nar/gkh340

    CAS  Article  Google Scholar 

  22. 22.

    Guindon S, Dufayard J-F, Lefort V, Anisimova M (2010) New alogrithms and methods to estimate maximum- likelihoods phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59:307–321

    CAS  Article  Google Scholar 

  23. 23.

    Eswar N, Eramian D, Webb B et al (2008) Protein structure modeling with MODELLER. Methods Mol Biol 426:145–159. doi:10.1007/978-1-60327-058-8_8

    CAS  Article  Google Scholar 

  24. 24.


  25. 25.

    Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926. doi:10.1063/1.445869

    CAS  Article  Google Scholar 

  26. 26.

    MacKerell AD, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616. doi:10.1021/jp973084f

    CAS  Article  Google Scholar 

  27. 27.

    Phillips JC, Braun R, Wang W et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802. doi:10.1002/jcc.20289

    CAS  Article  Google Scholar 

  28. 28.

    Humphreys DD, Friesner RA, Berne BJ (1994) A multiple-time-step molecular dynamics algorithm for macromolecules. J Phys Chem 98:6885–6892. doi:10.1021/j100078a035

    CAS  Article  Google Scholar 

  29. 29.

    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98:10089. doi:10.1063/1.464397

    CAS  Article  Google Scholar 

  30. 30.

    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. doi:10.1016/0263-7855(96)00018-5

    CAS  Article  Google Scholar 

  31. 31.

    Beautrait A, Leroux V, Chavent M et al (2008) Multiple-step virtual screening using VSM-G: overview and validation of fast geometrical matching enrichment. J Mol Model 14:135–148. doi:10.1007/s00894-007-0257-9

    CAS  Article  Google Scholar 

  32. 32.

    Verdonk ML, Cole JC, Hartshorn MJ et al (2003) Improved protein-ligand docking using GOLD. Proteins Struct Funct Genet 52:609–623. doi:10.1002/prot.10465

    CAS  Article  Google Scholar 

  33. 33.

    Liebeschuetz JW, Cole JC, Korb O (2012) Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J Comput Aided Mol Des 26:737–748. doi:10.1007/s10822-012-9551-4

    CAS  Article  Google Scholar 

  34. 34.

    Backman TWH, Cao Y, Girke T (2011) ChemMine tools: an online service for analyzing and clustering small molecules. Nucleic Acids Res 39:1–6. doi:10.1093/nar/gkr320

    Article  Google Scholar 

  35. 35.

    Akella LB, DeCaprio D (2010) Cheminformatics approaches to analyze diversity in compound screening libraries. Curr Opin Chem Biol 14:325–330. doi:10.1016/j.cbpa.2010.03.017

    CAS  Article  Google Scholar 

  36. 36.

    Monge A, Arrault A, Marot C, Morin-Allory L (2006) Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers. Mol Divers 10:389–403. doi:10.1007/s11030-006-9033-5

    CAS  Article  Google Scholar 

  37. 37.

    Le Guilloux V, Arrault A, Colliandre L et al (2012) Mining collections of compounds with screening assistant 2. J Cheminform 4:1–16. doi:10.1186/1758-2946-4-20

    Article  Google Scholar 

  38. 38.

    Abadio AKR, Kioshima ES, Leroux V et al (2015) Identification of New antifungal compounds targeting thioredoxin reductase of Paracoccidioides genus. PLoS One 10, e0142926. doi:10.1371/journal.pone.0142926

    Article  Google Scholar 

  39. 39.

    Otava Chemicals.

  40. 40.

    Gasteiger J, Rudolph C, Sadowski J (1990) Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput Methodol 3:537–547. doi:10.1016/0898-5529(90)90156-3

    CAS  Article  Google Scholar 

  41. 41.

    Huang B, Schroeder M (2006) LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct Biol 6:19. doi:10.1186/1472-6807-6-19

    Article  Google Scholar 

  42. 42.

    Zhang Z, Li Y, Lin B et al (2011) Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics 27:2083–2088. doi:10.1093/bioinformatics/btr331

    CAS  Article  Google Scholar 

  43. 43.

    Schmidtke P, Bidon-chanal A, Luque FJ, Barril X (2011) MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics 27:3276–3285. doi:10.1093/bioinformatics/btr550

    CAS  Article  Google Scholar 

  44. 44.

    Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719–2740. doi:10.1021/jm901137j

    CAS  Article  Google Scholar 

  45. 45.

    Drwal MN, Banerjee P, Dunkel M et al (2014) ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 42:W53–W58. doi:10.1093/nar/gku401

  46. 46.

    Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7:20. doi:10.1186/s13321-015-0069-3

    Article  Google Scholar 

  47. 47.

    O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33. doi:10.1186/1758-2946-3-33

    Article  Google Scholar 

  48. 48.

    Cappello F, Caron E, Dayde M, et al. (2005) Grid’5000: a large scale and highly reconfigurable Grid experimental testbed. In: Proceedings of IEEE/ACM International Workshop on Grid Compututing. pp 99–106

  49. 49.

    Maigret B, Ghemtio L (2010) Efficiency of a hierarchical protocol for high throughput structure-based virtual screening on GRID5000 cluster grid. Open Access Bioinforma 41. doi: 10.2147/OAB.S7272

  50. 50.

    Kelley LA, Mezulis S, Yates CM et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. doi:10.1038/nprot.2015.053

    CAS  Article  Google Scholar 

  51. 51.

    Biasini M, Bienert S, Waterhouse A et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42:252–258. doi:10.1093/nar/gku340

    Article  Google Scholar 

  52. 52.

    Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. doi:10.1093/nar/gkh468

    Google Scholar 

  53. 53.

    Yang J, Zhang Y (2015) I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 43:W174–W181. doi:10.1093/nar/gkv342

    Article  Google Scholar 

  54. 54.

    Wang C, Zhang H, Zheng W-M et al (2015) FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition. Bioinformatics. doi:10.1093/bioinformatics/btv581

    Google Scholar 

  55. 55.

    Hildebrand A, Remmert M, Biegert A, Söding J (2009) Fast and accurate automatic structure prediction with HHpred. Proteins Struct Funct Bioinforma 77:128–132. doi:10.1002/prot.22499

    CAS  Article  Google Scholar 

  56. 56.

    Källberg M, Wang H, Wang S et al (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7:1511–1522. doi:10.1038/nprot.2012.085

    Article  Google Scholar 

  57. 57.

    Vedula LS, Cane DE, Christianson DW (2005) Role of arginine-304 in the diphosphate-triggered active site closure mechanism of trichodiene synthase. Biochemistry 44:12719–12727. doi:10.1021/bi0510476

    CAS  Article  Google Scholar 

  58. 58.

    Vedula LS, Zhao Y, Coates RM et al (2007) Exploring biosynthetic diversity with trichodiene synthase. Arch Biochem Biophys 466:260–266. doi:10.1016/

    CAS  Article  Google Scholar 

  59. 59.

    Rynkiewicz MJ, Cane DE, Christianson DW (2001) Structure of trichodiene synthase from Fusarium sporotrichioides provides mechanistic inferences on the terpene cyclization cascade. Proc Natl Acad Sci USA 98:13543–13548. doi:10.1073/pnas.231313098

    CAS  Article  Google Scholar 

  60. 60.

    Miller DJ, Allemann RK (2012) Sesquiterpene synthases: passive catalysts or active players? Nat Prod Rep 29:60–71. doi:10.1039/c1np00060h

    CAS  Article  Google Scholar 

  61. 61.

    Gao Y, Honzatko RB, Peters RJ (2012) Terpenoid synthase structures: a so far incomplete view of complex catalysis. Nat Prod Rep 29:1153. doi:10.1039/c2np20059g

    CAS  Article  Google Scholar 

  62. 62.

    López-Gallego F, Wawrzyn GT, Schmidt-Dannert C (2010) Selectivity of fungal sesquiterpene synthases: Role of the active site’s H-1α loop in catalysis. Appl Environ Microbiol 76:7723–7733. doi:10.1128/AEM.01811-10

    Article  Google Scholar 

  63. 63.

    Ladner RD, Neamati N (2011) INHIBITORS OF dUTPase. US Patent 2011/0212467 A1

  64. 64.

    Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH (2016) PubChem substance and compound databases. Nucleic Acids Res 44(D1):D1202–D1213

  65. 65.

    Durrant JD, Cao R, Gorfe AA et al (2011) Non-bisphosphonate inhibitors of isoprenoid biosynthesis identified via computer-aided drug design. Chem Biol Drug Des 78:323–332. doi:10.1111/j.1747-0285.2011.01164.x

    CAS  Article  Google Scholar 

  66. 66.

    Pani G, Scherm B, Azara E et al (2014) Natural and natural-like phenolic inhibitors of type B trichothecene in vitro production by the wheat (Triticum sp.) pathogen Fusarium culmorum. J Agric Food Chem 62:4969–4978. doi:10.1021/jf500647h

    CAS  Article  Google Scholar 

  67. 67.

    Johnson ET, Evans KO, Dowd PF (2015) Antifungal activity of a synthetic cationic peptide against the plant pathogens Colletotrichum graminicola and three Fusarium species. Plant Pathol J 31:316–321. doi:10.5423/PPJ.NT.04.2015.0061

    Article  Google Scholar 

  68. 68.

    Crespo-Sempere A, Estiarte N, Marín S et al (2015) Targeting Fusarium graminearum control via polyamine enzyme inhibitors and polyamine analogs. Food Microbiol 49:95–103. doi:10.1016/

    CAS  Article  Google Scholar 

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This work is funded by a National Council for Scientific and Technological Development (CNPq) grant 400432/2012-9. E.B. is supported by a Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) postdoctoral fellowship (#51/2013). M.U. and K.H.K. receive support from Biotechnology and Biological Sciences Research Council, UK, Institute Strategy Grants 20:20® wheat (BB/J/00426X/1).

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Correspondence to N. F. Martins.

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Bresso, E., Leroux, V., Urban, M. et al. Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase—a potential target to reduce Fusarium head blight disease. J Mol Model 22, 163 (2016).

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  • Fusarium mycotoxins
  • Culmorin
  • Inhibitors
  • Homology modeling
  • Molecular dynamics
  • Ensemble docking