Journal of Molecular Modeling

, Volume 18, Issue 5, pp 2065–2078 | Cite as

Combination of docking, molecular dynamics and quantum mechanical calculations for metabolism prediction of 3,4-methylenedioxybenzoyl-2-thienylhydrazone

  • Rodolpho C. Braga
  • Vinícius M. Alves
  • Carlos A. M. Fraga
  • Eliezer J. Barreiro
  • Valéria de Oliveira
  • Carolina H. Andrade
Original Paper


In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In silico methods based on docking, molecular dynamics and quantum chemical calculations can bring us closer to understand drug metabolism and predict drug–drug interactions. We report herein on a combined methodology to explore the site of metabolism prediction of a new cardioactive drug prototype, LASSBio-294 (1), using MetaPrint2D to predict the most likely metabolites, combined with structure-based tools using docking, molecular dynamics and quantum mechanical calculations to predict the binding of the substrate to CYP2C9 enzyme, to estimate the binding free energy and to study the energy profiles for the oxidation of (1). Additionally, the computational study was correlated with a metabolic fingerprint profiling using LC-MS analysis. The results obtained using the computational methods gave valuable information about the probable metabolites of (1) (qualitatively) and also about the important interactions of this lead compound with the amino acid residues of the active site of CYP2C9. Moreover, using a combination of different levels of theory sheds light on the understanding of (1) metabolism by CYP2C9 and its mechanisms. The metabolic fingerprint profiling of (1) has shown that the metabolites founded in highest concentration in different species were metabolites M1, M2 and M3, whereas M8 was found to be a minor metabolite. Therefore, our computational study allowed a qualitative prediction for the metabolism of (1). The approach presented here has afforded new opportunities to improve metabolite identification strategies, mediated by not only CYP2C9 but also other CYP450 family enzymes.


Workflow for metabolic investigation proposed in our work. (a) Proposed computational methods to improve drug metabolism studies using different levels of theory, showing the energy changes from substrate binding to product formation in CYP450-catalyzed drug metabolism. (b) Metabolic fingerprint workflow for complex matrix analysis from different species. Stage I: the preparation of the samples to be analyzed by LC-MS; Stage II: analyze the samples into LC-MS and treat the data; and Stage III: identify and quantify all detectable metabolites produced in vitro by filamentous fungi and discover the similarity across the species using PCA analysis


Cytochrome P450 Docking Metabolism prediction Molecular dynamics QM calculations 



The authors would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG) for financial support and fellowships.


  1. 1.
    Jones BC, Middleton DS, Youdim K (2009) Cytochrome P450 metabolism and inhibition: analysis for drug discovery. Prog Med Chem 47:239–263. doi: 10.1016/S0079-6468(08)00206-3 CrossRefGoogle Scholar
  2. 2.
    Vistoli G, Pedretti A, Testa B (2008) Assessing drug-likeness–what are we missing? Drug Discov Today 13:285–294. doi: 10.1016/j.drudis.2007.11.007 CrossRefGoogle Scholar
  3. 3.
    Lewis DFV, Ito Y (2008) Human cytochromes P450 in the metabolism of drugs: new molecular models of enzyme-substrate interactions. Expert Opin Drug Metab Toxicol 4:1181–1186. doi: 10.1517/17425250802352412 CrossRefGoogle Scholar
  4. 4.
    de Montellano PRO (2010) Cytochrome P450: structure, mechanism, and biochemistry, 3rd edn. Springer, New YorkGoogle Scholar
  5. 5.
    Baranczewski P, Stanczak A, Sundberg K, Svensson R, Wallin A, Jansson J, Garberg P, Postlind H (2006) Introduction to in vitro estimation of metabolic stability and drug interactions of new chemical entities in drug discovery and development. Pharmacol Rep 58:453–472Google Scholar
  6. 6.
    de Graaf C, Pospisil P, Pos W, Folkers G, Vermeulen NPE (2005) Binding mode prediction of cytochrome p450 and thymidine kinase protein-ligand complexes by consideration of water and rescoring in automated docking. J Med Chem 48:2308–2318. doi: 10.1021/jm049650u CrossRefGoogle Scholar
  7. 7.
    Stjernschantz E, Vermeulen NPE, Oostenbrink C (2008) Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450. Expert Opin Drug Metab Toxicol 4:513–527. doi: 10.1517/17425255.4.5.513 CrossRefGoogle Scholar
  8. 8.
    Miners JO, Birkett DJ (1998) Cytochrome P4502C9: an enzyme of major importance in human drug metabolism. Br J Clin Pharmacol 45:525–538. doi: 10.1046/j.1365-2125.1998.00721.x CrossRefGoogle Scholar
  9. 9.
    Sykes MJ, McKinnon RA, Miners JO (2008) Prediction of metabolism by cytochrome P450 2C9: alignment and docking studies of a validated database of substrates. J Med Chem 51:780–791. doi: 10.1021/jm7009793 CrossRefGoogle Scholar
  10. 10.
    Wester MR, Yano JK, Schoch GA, Yang C, Griffin KJ, Stout CD, Johnson EF (2004) The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-A resolution. J Biol Chem 279:35630–35637. doi: 10.1074/jbc.M405427200 CrossRefGoogle Scholar
  11. 11.
    Usmani KA, Karoly ED, Hodgson E, Rose RL (2004) In vitro sulfoxidation of thioether compounds by human cytochrome P450 and flavin-containing monooxygenase isoforms with particular reference to the CYP2C subfamily. Drug Metab Dispos 32:333–339. doi: 10.1124/dmd.32.3.333 CrossRefGoogle Scholar
  12. 12.
    Khan MTH (2010) Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr Drug Metab 11(4):285–295. doi: 10.2174/138920010791514306 CrossRefGoogle Scholar
  13. 13.
    Sun H, Scott DO (2010) Structure-based drug metabolism predictions for drug design. Chem Biol Drug Des 75:3–17. doi: 10.1111/j.1747-0285.2009.00899.x CrossRefGoogle Scholar
  14. 14.
    Figueiredo JM, Camara CD, Amarante EG, Miranda ALP, Santos FM, Rodrigues CR, Fraga CAM, Barreiro EJ (2000) Design and synthesis of novel potent antinociceptive agents: methyl-imidazolyl N-acylhydrazone derivatives. Bioorg Med Chem 8:2243–2248. doi: 10.1016/S0968-0896(00)00152-8 CrossRefGoogle Scholar
  15. 15.
    Zapata-Sudo G, Sudo RT, Maronas PA, Silva GLM, Moreira OR, Aguiar MIS, Barreiro EJ (2003) Thienylhydrazone derivative increases sarcoplasmic reticulum Ca2+ release in mammalian skeletal muscle. Eur J Pharmacol 470:79–85. doi: 10.1016/S0014-2999(03)01757-6 CrossRefGoogle Scholar
  16. 16.
    Costa DG, da Silva JS, Kummerle AE, Sudo RT, Landgraf SS, Caruso-Neves C, Fraga CAM, de Lacerda Barreiro EJ, Zapata-Sudo G (2010) LASSBio-294, A compound with inotropic and lusitropic activity, decreases cardiac remodeling and improves Ca2(+) influx into sarcoplasmic reticulum after myocardial infarction. Am J Hypertens 23:1220–1227. doi: 10.1038/ajh.2010.157 CrossRefGoogle Scholar
  17. 17.
    Carlsson L, Spjuth O, Adams S, Glen RC, Boyer S (2010) Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse. BMC Bioinforma 11:362–362. doi: 10.1186/1471-2105-11-362 CrossRefGoogle Scholar
  18. 18.
    Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) The development and use of quantum-mechanical molecular-models.76. Am1 - a new general-purpose quantum-mechanical molecular-model. J Am Chem Soc 107:3902–3909. doi: 10.1021/ja00299a024 CrossRefGoogle Scholar
  19. 19.
    Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1996) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 118:2309–2309. doi: 10.1021/ja955032e CrossRefGoogle Scholar
  20. 20.
    Oda A, Yamaotsu N, Hirono S (2005) New AMBER force field parameters of heme iron for cytochrome P450s determined by quantum chemical calculations of simplified models. J Comput Chem 26:818–826. doi: 10.1002/jcc.20221 CrossRefGoogle Scholar
  21. 21.
    Clark AM, Labute P (2007) 2D depiction of protein-ligand complexes. J Chem Inf Model 47:1933–1944. doi: 10.1021/ci7001473 CrossRefGoogle Scholar
  22. 22.
    Labute P (2008) The generalized Born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area. J Comput Chem 29:1693–1698. doi: 10.1002/jcc.20933 CrossRefGoogle Scholar
  23. 23.
    young dc (2009) computational drug design: a guide for computational and medicinal chemists 1 Har/Cdr edn. Wiley-Interscience, Hoboken. N.JGoogle Scholar
  24. 24.
    Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330:341–346. doi: 10.1126/science.1187409 CrossRefGoogle Scholar
  25. 25.
    Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236. doi: 10.1021/ja9621760 CrossRefGoogle Scholar
  26. 26.
    Paterlini MG, Ferguson DM (1998) Constant temperature simulations using the Langevin equation with velocity Verlet integration. Chem Phys 236:243–252. doi: 10.1016/S0301-0104(98)00214-6 CrossRefGoogle Scholar
  27. 27.
    SZYBKI (2010). 1.5.1 edn. OpenEye Scientific Software Inc, Santa Fe, NMGoogle Scholar
  28. 28.
    Wlodek S, Skillman AG, Nicholls A (2010) Ligand entropy in Gas-Phase. Upon solvation and protein complexation. Fast estimation with Quasi-Newton Hessian. J Chem Theory Comput 6:2140–2152. doi: 10.1021/ct100095p CrossRefGoogle Scholar
  29. 29.
    Jaguar (2009) 7.6 edn. Schrodinger, LLC, New YorkGoogle Scholar
  30. 30.
    Braga RC, Tôrres ACB, Persiano CB, Alves RO, Fraga CAM, Barreiro EJ, de Oliveira V (2011) Determination of the cardioactive prototype LASSBio-294 and its metabolites in dog plasma by LC-MS/MS: Application for a pharmacokinetic study. J Pharm Biomed Anal 55:1024–1030. doi: 10.1016/j.jpba.2011.02.031 Google Scholar
  31. 31.
    Carneiro EO, Andrade CH, Braga RC, Torres ACB, Alves RO, Liao LM, Fraga CAM, Barreiro EJ, de Oliveira V (2010) Structure-based prediction and biosynthesis of the major mammalian metabolite of the cardioactive prototype LASSBio-294. Bioorg Med Chem Lett 20:3734–3736. doi: 10.1016/j.bmcl.2010.04.073 CrossRefGoogle Scholar
  32. 32.
    Ts P, Castillo S, Villar-Briones A, Oresic M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinforma 11:395. doi: 10.1186/1471-2105-11-395 CrossRefGoogle Scholar
  33. 33.
    Castro-Perez JM (2007) Current and future trends in the application of HPLC-MS to metabolite-identification studies. Drug Discov Today 12:249–256. doi: 10.1016/j.drudis.2007.01.007 CrossRefGoogle Scholar
  34. 34.
    Bathelt CM, Zurek J, Mulholland AJ, Harvey JN (2005) Electronic structure of compound I in human isoforms of cytochrome P450 from QM/MM modeling. J Am Chem Soc 127:12900–12908. doi: 10.1021/ja0520924 CrossRefGoogle Scholar
  35. 35.
    Czodrowski P, Kriegl JM, Scheuerer S, Fox T (2009) Computational approaches to predict drug metabolism. Expert Opin Drug Metab Toxicol 5:15–27. doi: 10.1517/17425250802568009 CrossRefGoogle Scholar
  36. 36.
    Boyer S, Arnby CH, Carlsson L, Smith J, Stein V, Glen RC (2007) Reaction site mapping of xenobiotic biotransformations. J Chem Inf Model 47:583–590CrossRefGoogle Scholar
  37. 37.
    Rydberg P, Vasanthanathan P, Oostenbrink C, Olsen L (2009) Fast prediction of cytochrome P450 mediated drug metabolism. ChemMedChem 4:2070–2079. doi: 10.1002/cmdc.200900363 CrossRefGoogle Scholar
  38. 38.
    Park J-Y, Harris D (2003) Construction and assessment of models of CYP2E1: predictions of metabolism from docking, molecular dynamics, and density functional theoretical calculations. J Med Chem 46:1645–1660. doi: 10.1021/jm020538a CrossRefGoogle Scholar
  39. 39.
    Lewis DFV (2000) On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics - Towards the prediction of human P450 substrate specificity and metabolism. Biochem Pharmacol 60:293–306. doi: 10.1016/S0006-2952(00)00335-X CrossRefGoogle Scholar
  40. 40.
    Fraga AGM, Fraga CAM, Barreiro EJ, Romeiro NC Perfil metabolico in silico de Prototipo N-Acilidrazonico Cardioativo. In: 30th Reunião Anual da Sociedade Brasileira de Química, Águas de Lindóia - SP, 2007. Abstracts of Papers. Sociedade Brasileira de Química, pp MD-053Google Scholar
  41. 41.
    Tarcsay A, Rb K, GrM K (2010) Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach. J Comput-Aided Mol Des 24:399–408. doi: 10.1007/s10822-010-9347-3 CrossRefGoogle Scholar
  42. 42.
    de Groot MJ, Alex AA, Jones BC (2002) Development of a combined protein and pharmacophore model for cytochrome P450 2C9. J Med Chem 45:1983–1993. doi: jm0110791[pii] CrossRefGoogle Scholar
  43. 43.
    Clodfelter KH, Waxman DJ, Vajda S (2006) Computational solvent mapping reveals the importance of local conformational changes for broad substrate specificity in mammalian cytochromes P450. Biochemistry 45:9393–9407. doi: 10.1021/bi060343v CrossRefGoogle Scholar
  44. 44.
    Polgar T, Menyhard DK, Keseru GM (2007) Effective virtual screening protocol for CYP2C9 ligands using a screening site constructed from flurbiprofen and S-warfarin pockets. J Comput-Aided Mol Des 21:539–548. doi: 10.1007/S10822-007-9137-8 CrossRefGoogle Scholar
  45. 45.
    Williams PA, Cosme J, Ward A, Angove HC, Matak Vinkoviƒá D, Jhoti H (2003) Crystal structure of human cytochrome P450 2C9 with bound warfarin. Nature 424:464–468. doi: 10.1038/nature01862 CrossRefGoogle Scholar
  46. 46.
    Rydberg P, Rod TH, Olsen L, Ryde U (2007) Dynamics of water molecules in the active-site cavity of human cytochromes P450. J Phys Chem B 111:5445–5457. doi: 10.1021/jp070390c CrossRefGoogle Scholar
  47. 47.
    Santos R, Hritz J, Oostenbrink C (2010) Role of water in molecular docking simulations of cytochrome P450 2D6. J Chem Inf Model 50:146–154. doi: 10.1021/ci900293e CrossRefGoogle Scholar
  48. 48.
    Mulholland AJ (2005) Modelling enzyme reaction mechanisms, specificity and catalysis. Drug Discov Today 10:1393–1402. doi: 10.1016/S1359-6446(05)03611-1 CrossRefGoogle Scholar
  49. 49.
    Shaik S, Cohen S, Wang Y, Chen H, Kumar D, Thiel W (2010) P450 enzymes: their structure, reactivity, and selectivity-modeled by QM/MM calculations. Chem Rev 110:949–1017. doi: 10.1021/cr900121s CrossRefGoogle Scholar
  50. 50.
    Shaik S, Milko P, Schyman P, Usharani D, Chen H (2011) Trends in aromatic oxidation reactions catalyzed by Cytochrome P450 Enzymes: a valence bond modeling. J Chem Theory Comput 7:327–339. doi: 10.1021/ct100554g CrossRefGoogle Scholar
  51. 51.
    Schoneboom JC, Lin H, Reuter N, Thiel W, Cohen S, Ogliaro F, Shaik S (2002) The elusive oxidant species of cytochrome P450 enzymes: characterization by combined quantum mechanical/molecular mechanical (QM/MM) calculations. J Am Chem Soc 124:8142–8151. doi: ja026279w[pii] CrossRefGoogle Scholar
  52. 52.
    Kellner DG, Hung SC, Weiss KE, Sligar SG (2002) Kinetic characterization of compound I formation in the thermostable cytochrome P450 CYP119. J Biol Chem 277:9641–9644. doi: 10.1074/jbc.C100745200 CrossRefGoogle Scholar
  53. 53.
    Crestoni ME, Fornarini S, Lanucara F (2009) Oxygen-atom transfer by a naked manganese(V)-Oxo-Porphyrin complex reveals axial ligand effect. Chem-Eur J 15:7863–7866. doi: 10.1002/Chem.200901361 CrossRefGoogle Scholar
  54. 54.
    Chiavarino B, Cipollini R, Crestoni ME, Fornarini S, Lanucara F, Lapi A (2008) Probing the Compound I-like reactivity of a bare high-valent oxo iron porphyrin complex: the oxidation of tertiary amines. J Am Chem Soc 130:3208–3217. doi: 10.1021/ja077286t CrossRefGoogle Scholar
  55. 55.
    Lonsdale R, Ranaghan KE, Mulholland AJ (2010) Computational enzymology. Chem Commun 46:2354–2372. doi: 10.1039/B925647d CrossRefGoogle Scholar
  56. 56.
    Rydberg P, Ryde U, Olsen L (2008) Sulfoxide, sulfur, and nitrogen oxidation and dealkylation by Cytochrome P450. J Chem Theor Comput 4:1369–1377. doi: 10.1021/ct800101v CrossRefGoogle Scholar
  57. 57.
    Watanabe Y (2001) Alternatives to the oxoferryl porphyrin cation radical as the proposed reactive intermediate of cytochrome P450: two-electron oxidized Fe(III) porphyrin derivatives. J Biol Inorg Chem 6:846–856. doi: 10.1007/s007750100278 CrossRefGoogle Scholar
  58. 58.
    Porro CS, Sutcliffe MJ, de Visser SP (2009) Quantum mechanics/molecular mechanics studies on the sulfoxidation of dimethyl sulfide by compound I and compound 0 of cytochrome P450: which is the better oxidant? J Phys Chem A 113:11635–11642. doi: 10.1021/jp9023926 CrossRefGoogle Scholar
  59. 59.
    Bathelt CM, Mulholland AJ, Harvey JN (2008) QM/MM modeling of benzene hydroxylation in human cytochrome P450 2C9. J Phys Chem A 112:13149–13156. doi: 10.1021/jp8016908 CrossRefGoogle Scholar
  60. 60.
    de Visser SP, Shaik S (2003) A proton-shuttle mechanism mediated by the porphyrin in benzene hydroxylation by cytochrome P450 enzymes. J Am Chem Soc 125:7413–7424. doi: 10.1021/Ja034142f CrossRefGoogle Scholar
  61. 61.
    Costa EMDB, Pimenta FC, Luz WC, de Oliveira V (2008) Selection of filamentous fungi of the Beauveria genus able to metabolize quercetin like mammalian cells. Braz J Microbiol 39:405–408. doi: 10.1590/S1517-83822008000200036 CrossRefGoogle Scholar
  62. 62.
    Pazini F, Menegatti R, Sabino JR, Andrade CH, Neves G, Rates SMK, Noel F, Fraga CAM, Barreiro EJ, de Oliveira V (2010) Design of new dopamine D2 receptor ligands: biosynthesis and pharmacological evaluation of the hydroxylated metabolite of LASSBio-581. Bioorg Med Chem Lett 20:2888–2891. doi: 10.1016/J.Bmcl.2010.03.034 CrossRefGoogle Scholar
  63. 63.
    Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27:747–751CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Rodolpho C. Braga
    • 1
  • Vinícius M. Alves
    • 1
  • Carlos A. M. Fraga
    • 2
  • Eliezer J. Barreiro
    • 2
  • Valéria de Oliveira
    • 1
  • Carolina H. Andrade
    • 1
  1. 1.Faculty of PharmacyFederal University of GoiásGoiâniaBrazil
  2. 2.LASSBio, Faculty of PharmacyFederal University of Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations