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The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors

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Abstract

A new Receptor-Dependent LQTA-QSAR approach, RD-LQTA-QSAR, is proposed as a new 4D-QSAR method. It is an evolution of receptor independent LQTA-QSAR. This approach uses the free GROMACS package to carry out molecular dynamics simulations and generates a conformational ensemble profile for each compound. Such an ensemble is used to build molecular interaction field-based QSAR models, as in CoMFA. To show the potential of this methodology, a set of 38 phenothiazine derivatives that are specific competitive T. cruzi trypanothione reductase inhibitors, was chosen. Using a combination of molecular docking and molecular dynamics simulations, the binding mode of the phenotiazine derivatives was evaluated in a simulated induced fit approach. The ligands alignments were performed using both ligand and binding site atoms, enabling unbiased alignment. The models obtained were extensively validated by leave-N-out cross-validation and y-randomization techniques to test for their robustness and absence of chance correlation. The final model presented Q 2 LOO of 0.87 and R² of 0.92 and a suitable external prediction of \( Q_{ext}^{2} \)= 0.78. The adapted binding site obtained is useful to perform virtual screening and ligand structure-based design and the descriptors in the final model can aid in the design new inhibitors.

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References

  1. World Health Organization (2010) First WHO report on neglected tropical diseases: working to overcome the global impact of neglected tropical diseases. WHO Press 75–81

  2. Coura JR, Vinas PA (2010) Chagas disease: a new worldwide challenge. Nature 465:S6–S7

    Article  Google Scholar 

  3. Duschak VG, Couto A (2007) An insight on targets and patented drugs for chemotherapy of Chagas disease. Recent Pat Anti-Infect Drug Discov 2:19–51

    Article  CAS  Google Scholar 

  4. Schirmer RH, Joachim GM, Krauth-Siegel RL (1995) Disulfide-reductase inhibitors as chemotherapeutic agents: the design of drugs for trypanosomiasis and malaria. Angew Chem Int Edit 34:141–154

    Article  CAS  Google Scholar 

  5. Christina BL, Ilme S, Wolfgang K, Emil FP, Krauth-Siegel RL (1994) The structure of Trypanosoma cruzi trypanothione reductase in the oxidized and NADPH reduced state. Proteins 18:161–173

    Article  Google Scholar 

  6. Bond CS, Zhang Y, Berriman M, Cunningham ML, Fairlamb AH, Hunter WN (1999) Crystal structure of Trypanosoma cruzi trypanothione reductase in complex with trypanothione, and the structure-based discovery of new natural product inhibitors. Struct Fold Des 7:81–89

    Article  CAS  Google Scholar 

  7. Yihong Z, Charles SB, Susan B, Mark LC, Alan HF, William NH (1996) The crystal structure of trypanothione reductase from the human pathogen Trypanosoma cruzi at 2.3 Å resolution. Protein Sci 5:52–61

    Google Scholar 

  8. Bonse S, Santelli-Rouvier C, Barbe J, Krauth-Siegel RL (1999) Inhibition of trypanosoma cruzi trypanothione reductase by acridines: kinetic studies and structure-activity relationships. J Med Chem 42:5448–5454

    Article  CAS  Google Scholar 

  9. Krauth-Siegel RL, Inhoff O (2003) Parasite-specific trypanothione reductase as a drug target molecule. Parasit Res 90:S77–S85

    Article  Google Scholar 

  10. Benson TJ, McKie JH, Garforth J, Borges A, Fairlamb AH, Douglas KT (1992) Rationally designed selective inhibitors of trypanothione reductase. Phenothiazines and related tricyclics as lead structures. Biochem J 286:9–11

    CAS  Google Scholar 

  11. Faerman CH, Savvides SN, Strickland C, Breidenbach MA, Ponasik JA, Ganem B, Ripoll D, Luise Krauth-Siegel R, Andrew Karplus P (1996) Charge is the major discriminating factor for glutathione reductase versus trypanothione reductase inhibitors. Bioorg Med Chem 4:1247–1253

    Article  CAS  Google Scholar 

  12. Khan MOF, Austin SE, Chan C, Yin H, Marks D, Vaghjiani SN, Kendrick H, Yardley V, Croft SL, Douglas KT (2000) Use of an additional hydrophobic binding site, the Z site, in the rational drug design of a new class of stronger trypanothione reductase inhibitor, quaternary alkyl ammonium phenothiaziness. J Med Chem 43:3148–3156

    Article  CAS  Google Scholar 

  13. Iribarne F, Paulino M, Aguilera S, Tapia O (2009) Assaying phenothiazine derivatives as trypanothione reductase and glutathione reductase inhibitors by theoretical docking and molecular dynamics studies. J Mol Graph Model 28:371–381

    Article  CAS  Google Scholar 

  14. Galeazzi R (2009) Molecular dynamics as a tool in rational drug design: current status and some major applications. Curr Comput-Aided Drug Des 5:225–240

    Article  CAS  Google Scholar 

  15. Osvaldo AS-F, Anton JH (2002) The 4D-QSAR paradigm: application to a novel set of non-peptidic HIV protease inhibitors. Quant Struct-Act Relat 21:369–381

    Article  Google Scholar 

  16. Martins JPA, Barbosa EG, Pasqualoto KFM, Ferreira MMC (2009) LQTA-QSAR: a new 4D-QSAR methodology. J Chem Inf Model 49:1428–1436

    Article  CAS  Google Scholar 

  17. Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524

    Article  CAS  Google Scholar 

  18. Van Der David S, Erik L, Berk H, Gerrit G, Alan EM, Herman JCB (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718

    Article  Google Scholar 

  19. Chan C, Yin H, Garforth J, McKie JH, Jaouhari R, Speers P, Douglas KT, Rock PJ, Yardley V, Croft SL, Fairlamb AH (1998) Phenothiazine inhibitors of trypanothione reductase as potential antitrypanosomal and antileishmanial drugs. J Med Chem 41:148–156

    Article  CAS  Google Scholar 

  20. Parveen S, Khan MOF, Austin SE, Croft SL, Yardley V, Rock P, Douglas KT (2005) Antitrypanosomal, antileishmanial, and antimalarial activities of quaternary arylalkylammonium 2-amino-4-chlorophenyl phenyl sulfides, a new class of trypanothione reductase inhibitor, and of N-acyl derivatives of 2-amino-4-chlorophenyl phenyl sulfide. J Med Chem 48:8087–8097

    Article  CAS  Google Scholar 

  21. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  22. Saravanamuthu A, Vickers TJ, Bond CS, Peterson MR, Hunter WN, Fairlamb AH (2004) Two interacting binding sites for quinacrine derivatives in the active site of trypanothione reductase: a template for drug design. J Biol Chem 279:29493–29500

    Article  CAS  Google Scholar 

  23. Dennington IR, Keith T, Millam J, Eppinnett K, Hovell WL, Gilliland G (2003) GaussView. Version 3.09 ed. Semichem Inc., Shawnee Mission, KS

  24. Wodrich MD, Corminboeuf C, Schreiner PR, Fokin AA, PvR Schleyer (2007) How accurate are DFT treatments of organic energies? Org Lett 9:1851–1854

    Article  CAS  Google Scholar 

  25. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Jr. JAM, Vreven T, Kudin KN, Burant JC, Millam JM, Iyengar SS, Tomasi J, Barone V, Mennucci B, Cossi M, Scalmani G, Rega N, Petersson GA, Nakatsuji H, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Klene M, Li X, Knox JE, Hratchian HP, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Ayala PY, Morokuma K, Voth GA, Salvador P, Dannenberg JJ, Zakrzewski VG, Dapprich S, Daniels AD, Strain MC, Farkas O, Malick DK, Rabuck AD, Raghavachari K, Foresman JB, Ortiz JV, Cui Q, Baboul AG, Clifford S, Cioslowski J, Stefanov BB, Liu G, Liashenko A, Piskorz P, Komaromi I, Martin RL, Keith DJF, Al-Laham MA, Peng CY, Nanayakkara A, Challacombe M, Gill PMW, Johnson B, Chen W, Wong MW, Gonzalez C, Pople JA (2004) Gaussian 03, Revision C.02. Gaussian, Inc., Wallingford CT

  26. Breneman CM, Wiberg KB (1990) Determining atom-centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis. J Comput Chem 11:361–373

    Article  CAS  Google Scholar 

  27. Schüttelkopf AW, van Aalten DM (2004) PRODRG: a tool for high-throughput crystallography of proteinligand complexes. Acta Crystallogr D 60:1355–1363

    Article  Google Scholar 

  28. Delphine CB, David MR, Jan HJ (2008) Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins 73:765–783

    Article  Google Scholar 

  29. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723

    Article  CAS  Google Scholar 

  30. Garrett MM, David SG, Robert SH, Ruth H, William EH, Richard KB, Arthur JO (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662

    Article  Google Scholar 

  31. Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45:503–528

    Article  Google Scholar 

  32. Kubinyi H (1997) QSAR and 3D QSAR in drug design Part 1: methodology. Drug Discov Today 2:457–467

    Article  CAS  Google Scholar 

  33. Barbosa EG, Ferreira MMC (2012) Digital filters for molecular interaction field descriptors. Mol Inf 31:75–84

    Article  CAS  Google Scholar 

  34. Kiralj R, Ferreira MMC (2010) Is your QSAR/QSPR descriptor real or trash? J Chemom 24:681–693

    Article  CAS  Google Scholar 

  35. Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770–787

    Article  CAS  Google Scholar 

  36. Accelrys (2002) ViewerLite, 5.0th edn. San Diego, CA

    Google Scholar 

  37. Horvath D (1997) A virtual screening approach applied to the search for trypanothione reductase inhibitors. J Med Chem 40:2412–2423

    Article  CAS  Google Scholar 

  38. Teófilo RF, Martins JPA, Ferreira MMC (2009) Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression. J Chemometr 23:32–48

    Article  Google Scholar 

  39. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361–1375

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank the Brazilian scientific funding agencies, CAPES and FAPESP, for financial support and Prof. Dr. Carol H. Collins for English revision.

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Correspondence to Márcia M. C. Ferreira.

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Barbosa, E.G., Pasqualoto, K.F.M. & Ferreira, M.M.C. The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors. J Comput Aided Mol Des 26, 1055–1065 (2012). https://doi.org/10.1007/s10822-012-9598-2

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  • DOI: https://doi.org/10.1007/s10822-012-9598-2

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