Journal of Computer-Aided Molecular Design

, Volume 32, Issue 5, pp 591–605 | Cite as

Investigation of the binding mode of a novel cruzain inhibitor by docking, molecular dynamics, ab initio and MM/PBSA calculations

  • Luan Carvalho Martins
  • Pedro Henrique Monteiro Torres
  • Renata Barbosa de Oliveira
  • Pedro Geraldo Pascutti
  • Elio A. Cino
  • Rafaela Salgado Ferreira


Chagas disease remains a major health problem in South America, and throughout the world. The two drugs clinically available for its treatment have limited efficacy and cause serious adverse effects. Cruzain is an established therapeutic target of Trypanosoma cruzi, the protozoan that causes Chagas disease. Our group recently identified a competitive cruzain inhibitor (compound 1) with an IC50 = 15 µM that is also more synthetically accessible than the previously reported lead, compound 2. Prior studies, however, did not propose a binding mode for compound 1, hindering understanding of the structure–activity relationship and optimization. Here, the cruzain binding mode of compound 1 was investigated using docking, molecular dynamics (MD) simulations with ab initio derived parameters, ab initio calculations, and MM/PBSA. Two ligand protonation states and four binding poses were evaluated. A careful ligand parameterization method was employed to derive more physically meaningful parameters than those obtained by automated tools. The poses of unprotonated 1 were unstable in MD, showing large conformational changes and diffusing away from the binding site, whereas the protonated form showed higher stability and interaction with negatively charged residues Asp161 and Cys25. MM/PBSA also suggested that these two residues contribute favorably to binding of compound 1. By combining results from MD, ab initio calculations, and MM/PBSA, a binding mode of 1 is proposed. The results also provide insights for further optimization of 1, an interesting lead compound for the development of new cruzain inhibitors.


Cruzain inhibitors Molecular dynamics Binding mode prediction Free energy calculations Ligand parameterization MM/PBSA 



The authors would like to thank Brazilian funding agencies Conselho Nacional do Desenvolvimento Científico e Tecnológico, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Fundação de Amparo à Pesquisa do Estado de Minas Gerais and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro. RSF, RBO and PGP hold CNPq research fellowships (Bolsa de Produtividade em Pesquisa). This research was enabled in part by support provided by Compute Canada (

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Supplementary material 1 (DOCX 742 KB) (23 kb)
Supplementary material 2 (TOP 23 KB) (24 kb)
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  1. 1.
    World Health Organization (2015) Chagas disease in Latin America: an epidemiological update based on 2010 estimates. Wkly Epidemiol Rec 6:33–44. Google Scholar
  2. 2.
    World Health Organization (2013) Sustaining the drive to overcome the global impact of neglected tropical diseases. Second WHO report on neglected tropical diseases, vol 3, no 9. World Health Organization, Geneva, pp 67–71Google Scholar
  3. 3.
    Morillo CA, Marin-Neto JA, Avezum A et al (2015) Randomized trial of benznidazole for chronic Chagas’ cardiomyopathy. N Engl J Med 373:1295–1306. CrossRefGoogle Scholar
  4. 4.
    das Neves Pinto AY, da Costa Valente V, Coura JR et al (2013) Clinical follow-up of responses to treatment with benznidazol in amazon: a cohort study of acute Chagas disease. PLoS ONE 8:e64450. CrossRefGoogle Scholar
  5. 5.
    Guedes PM, Silva GK, Gutierrez FR, Silva JS (2011) Current status of Chagas disease chemotherapy. Expert Rev Anti Infect Ther 9:609–620. CrossRefGoogle Scholar
  6. 6.
    Carlos Pinto Dias J, Novaes Ramos A, Dias Gontijo E et al (2016) II Consenso Brasileiro em Doença de Chagas, 2015. Epidemiol e Serviços Saúde 25:1–10. CrossRefGoogle Scholar
  7. 7.
    Keenan M, Chaplin JH (2015) A new era for Chagas disease drug discovery? Progress in medicinal chemistry, 1st edn. Elsevier, Oxford, pp 185–230Google Scholar
  8. 8.
    Duschak VG, Ciaccio M, Nasser JR, Basombrío MA (2001) Enzymatic activity, protein expressionm, and gene sequence of cruzipain in virulent and attenuated Trypanosoma cruzi strains. J Parasitol 87:1016–1022CrossRefGoogle Scholar
  9. 9.
    McKerrow J, Doyle P, Engel J et al (2009) Two approaches to discovering and developing new drugs for Chagas disease. Mem Inst Oswaldo Cruz 104:263–269. CrossRefGoogle Scholar
  10. 10.
    Engel JC, Doyle PS, Hsieh I, McKerrow JH (1998) Cysteine protease inhibitors cure an experimental Trypanosoma cruzi infection. J Exp Med 188:725–734CrossRefGoogle Scholar
  11. 11.
    Ferreira RS, Simeonov A, Jadhav A et al (2010) Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors. J Med Chem 53:4891–4905. CrossRefGoogle Scholar
  12. 12.
    Braga SFP, Martins LC, da Silva EB et al (2017) Synthesis and biological evaluation of potential inhibitors of the cysteine proteases cruzain and rhodesain designed by molecular simplification. Bioorg Med Chem 25:1889–1900. CrossRefGoogle Scholar
  13. 13.
    Cino EA, Choy W-Y, Karttunen M (2013) Conformational biases of linear motifs. J Phys Chem B 117:15943–15957. CrossRefGoogle Scholar
  14. 14.
    Cino EA, Killoran RC, Karttunen M, Choy W-Y (2013) Binding of disordered proteins to a protein hub. Sci Rep 3:2305. CrossRefGoogle Scholar
  15. 15.
    Kuhn B, Tichý M, Wang L et al (2017) Prospective evaluation of free energy calculations for the prioritization of Cathepsin L inhibitors. J Med Chem 60:2485–2497. CrossRefGoogle Scholar
  16. 16.
    Matter H, Sotriffer C (2011) Applications and success stories in virtual screening. In: Sotriffer C (ed) Virtual screening: principles, challenges, and practical guidelines. Wiley, New York, pp 319–358CrossRefGoogle Scholar
  17. 17.
    De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061. CrossRefGoogle Scholar
  18. 18.
    Wang L, Wu Y, Deng Y et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703. CrossRefGoogle Scholar
  19. 19.
    Gordon MS, Schmidt MW (2005) Advances in electronic structure theory. In: Dykstra CE, Frenking G, Kim KS, Scuseria GE (eds) Theory and applications of computational chemistry. Elsevier, Amsterdam, pp 1167–1189CrossRefGoogle Scholar
  20. 20.
    Janssen CL, Nielsen IB, Leininger ML et al (2008) The massively parallel quantum chemistry program (MPQC). Sandia National Laboratories, LivermoreGoogle Scholar
  21. 21.
    Thapa B, Schlegel HB (2016) Density functional theory calculation of pKa’s of thiols in aqueous solution using explicit water molecules and the polarizable continuum model. J Phys Chem A 120:5726–5735. CrossRefGoogle Scholar
  22. 22.
    Thapa B, Schlegel HB (2015) Calculations of pKa’s and redox potentials of nucleobases with explicit waters and polarizable continuum solvation. J Phys Chem A 119:5134–5144. CrossRefGoogle Scholar
  23. 23.
    Linstrom PJ, Mallard WG (2014) NIST chemistry webBook, NIST standard reference database number 69. National Institute of Standards and Technology 20899: doi: citeulike-article-id:3211271Google Scholar
  24. 24.
    Camaioni DM, Schwerdtfeger CA (2005) Comment on “Accurate experimental values for the free energies of hydration of H+, OH-, and H3O+”. J Phys Chem A 109:10795–10797. CrossRefGoogle Scholar
  25. 25.
    Welch BL (1947) The generalization of “Student”s’ problem when several different population variances are involved. Biometrika 34:28–35. Google Scholar
  26. 26.
    Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Springer, BostonCrossRefGoogle Scholar
  27. 27.
    Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60. CrossRefGoogle Scholar
  28. 28.
    Wilcoxon F (1947) Probability tables for individual comparisons by ranking methods. Biometrics 3:119. CrossRefGoogle Scholar
  29. 29.
    Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621. CrossRefGoogle Scholar
  30. 30.
    Vanquelef E, Simon S, Marquant G et al (2011) R.E.D. Server: a web service for deriving RESP and ESP charges and building force field libraries for new molecules and molecular fragments. Nucleic Acids Res 39:W511–W517. CrossRefGoogle Scholar
  31. 31.
    Sousa da Silva AW, Vranken WF (2012) ACPYPE—antechamber python parser interface. BMC Res Notes 5:367. CrossRefGoogle Scholar
  32. 32.
    Pappu RV, Hart RK, Ponder JW (1988) Tinker: a package for molecular dynamics simulation. J Phys Chem B 102:9725–9742CrossRefGoogle Scholar
  33. 33.
    Schlee D, Sneath PHA, Sokal RR, Freeman WH (1975) Numerical taxonomy. The principles and practice of numerical classification. Syst Zool 24:263. CrossRefGoogle Scholar
  34. 34.
    O’Boyle NM, Banck M, James C et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:33. CrossRefGoogle Scholar
  35. 35.
    Connolly ML (1983) Analytical molecular surface calculation. J Appl Crystallogr 16:548–558. CrossRefGoogle Scholar
  36. 36.
    Dupradeau F-Y, Pigache A, Zaffran T et al (2010) The R.E.D. tools: advances in RESP and ESP charge derivation and force field library building. Phys Chem Chem Phys 12:7821. CrossRefGoogle Scholar
  37. 37.
    Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. CrossRefGoogle Scholar
  38. 38.
    Pronk S, Páll S, Schulz R et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854. CrossRefGoogle Scholar
  39. 39.
    Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. CrossRefGoogle Scholar
  40. 40.
    Shevchuk R, Prada-Gracia D, Rao F (2012) Water structure-forming capabilities are temperature shifted for different models. J Phys Chem B 116:7538–7543. CrossRefGoogle Scholar
  41. 41.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092. CrossRefGoogle Scholar
  42. 42.
    Olsson MHM, Søndergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7:525–537. CrossRefGoogle Scholar
  43. 43.
    Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA (2004) PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res 32:W665–W667. CrossRefGoogle Scholar
  44. 44.
    Lindorff-Larsen K, Piana S, Palmo K et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins. Google Scholar
  45. 45.
    Kumari R, Kumar R, Lynn A (2014) g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54:1951–1962. CrossRefGoogle Scholar
  46. 46.
    Gangarapu S, Marcelis ATM, Zuilhof H (2013) Accurate pKa calculation of the conjugate acids of alkanolamines, alkaloids and nucleotide bases by quantum chemical methods. ChemPhysChem 14:990–995. CrossRefGoogle Scholar
  47. 47.
    Rebollar-Zepeda AM, Galano A (2012) First principles calculations of pK a values of amines in aqueous solution: application to neurotransmitters. Int J Quantum Chem 112:3449–3460. CrossRefGoogle Scholar
  48. 48.
    Casasnovas R, Fernández D, Ortega-Castro J et al (2011) Avoiding gas-phase calculations in theoretical pKa predictions. Theor Chem Acc 130:1–13. CrossRefGoogle Scholar
  49. 49.
    Albert A, Goldacre R, Phillips J (1948) 455. The strength of heterocyclic bases. J Chem Soc. Google Scholar
  50. 50.
    Warhurst DC (2003) Hydroxychloroquine is much less active than chloroquine against chloroquine-resistant Plasmodium falciparum, in agreement with its physicochemical properties. J Antimicrob Chemother 52:188–193. CrossRefGoogle Scholar
  51. 51.
    Lundborg M, Lindahl E (2015) Automatic GROMACS topology generation and comparisons of force fields for solvation free energy calculations. J Phys Chem B 119:810–823. CrossRefGoogle Scholar
  52. 52.
    Karttunen M, Rottler J, Vattulainen I, Sagui C (2008) Chap. 2 Electrostatics in biomolecular simulations: where are we now and where are we heading? Current Topics in Membranes. Elsevier Inc, pp 49–89Google Scholar
  53. 53.
    Reynolds CA, Essex JW, Richards WG (1992) Atomic charges for variable molecular conformations. J Am Chem Soc 114:9075–9079. CrossRefGoogle Scholar
  54. 54.
    Cornell WD, Cieplak P, Bayly CI et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197. CrossRefGoogle Scholar
  55. 55.
    Basma M, Sundara S, Çalgan D et al (2001) Solvated ensemble averaging in the calculation of partial atomic charges. J Comput Chem 22:1125–1137. CrossRefGoogle Scholar
  56. 56.
    Gillmor S, Craik CS, Fletterick RJ (1997) Structural determinants of specificity in the cysteine protease cruzain. Protein Sci 6:1603–1611. CrossRefGoogle Scholar
  57. 57.
    Kerr ID, Lee JH, Farady CJ et al (2009) Vinyl sulfones as antiparasitic agents and a structural basis for drug design. J Biol Chem 284:25697–25703. CrossRefGoogle Scholar
  58. 58.
    Schrödinger LLC (2016) The PyMOL molecular graphics system. Schrödinger LLC version 1.
  59. 59.
    McGrath ME, Eakin AE, Engel JC et al (1995) The crystal structure of cruzain: a therapeutic target for Chagas’ disease. J Mol Biol 247:251–259. CrossRefGoogle Scholar
  60. 60.
    Chen YT, Brinen LS, Kerr ID et al (2010) In vitro and in vivo studies of the trypanocidal properties of WRR-483 against Trypanosoma cruzi. PLoS Negl Trop Dis 4:e825. CrossRefGoogle Scholar
  61. 61.
    Brinen LS, Hansell E, Cheng J et al (2000) A target within the target: probing cruzain’s P1′ site to define structural determinants for the Chagas’ disease protease. Structure 8:831–840. CrossRefGoogle Scholar
  62. 62.
    Gillmor SA (1998) Chapter 3: X-ray structures of complexes of cruzain with designed covalent inhibitors. Enzyme-ligand interactions, inhibition and specificity, pp 50–80Google Scholar
  63. 63.
    Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461. CrossRefGoogle Scholar
  64. 64.
    Thompson DC, Humblet C, Joseph-McCarthy D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48:1081–1091. CrossRefGoogle Scholar
  65. 65.
    Negri M, Recanatini M, Hartmann RW (2011) Computational investigation of the binding mode of bis(hydroxylphenyl)arenes in 17β-HSD1: molecular dynamics simulations, MM-PBSA free energy calculations, and molecular electrostatic potential maps. J Comput Aided Mol Des 25:795–811. CrossRefGoogle Scholar
  66. 66.
    Sun H, Li Y, Shen M et al (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys Chem Chem Phys 16:22035–22045. CrossRefGoogle Scholar
  67. 67.
    Lu M-C, Yuan Z-W, Jiang Y-L et al (2016) A systematic molecular dynamics approach to the study of peptide Keap1–Nrf2 protein–protein interaction inhibitors and its application to p62 peptides. Mol BioSyst 12:1378–1387. CrossRefGoogle Scholar
  68. 68.
    Aldeghi M, Bodkin MJ, Knapp S, Biggin PC (2017) Statistical analysis on the performance of molecular mechanics Poisson-Boltzmann surface area versus absolute binding free energy calculations: bromodomains as a case study. J Chem Inf Model 57:2203–2221. CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratório de Modelagem Molecular e Planejamento de Fármacos, Departamento de Bioquímica e ImunologiaUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Laboratório de Química Farmacêutica, Departamento de Produtos Farmacêuticos, Faculdade de FarmáciaUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  3. 3.Programa de Computação CientíficaFundação Oswaldo Cruz – FIOCRUZRio de JaneiroBrazil
  4. 4.Laboratório de Modelagem e Dinâmica Molecular, Instituto de BiofísicaUniversidade Federal do Rio de JaneiroRio de JaneiroBrazil
  5. 5.Departamento de Bioquímica e ImunologiaUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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