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3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents

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Abstract

Tuberculosis (TB) is the primary cause of mortality among infectious diseases. Mycobacterium tuberculosis monophosphate kinase (TMPKmt) is essential to DNA replication. Thus, this enzyme represents a promising target for developing new drugs against TB. In the present study, the receptor-independent, RI, 4D-QSAR method has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 81 thymidine analogues, and two corresponding subsets, reported as inhibitors of TMPKmt. The resulting optimized models are not only statistically significant with r 2 ranging from 0.83 to 0.92 and q 2 from 0.78 to 0.88, but also are robustly predictive based on test set predictions. The most and the least potent inhibitors in their respective postulated active conformations, derived from each of the models, were docked in the active site of the TMPKmt crystal structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. Moreover, the QSAR models provide insights regarding a probable mechanism of action of the analogues.

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References

  1. Dye C, Floyd K, Uplekar M (2008) World Health Organization Document, WHO/HTM/TB/2008.393

  2. Aziz MA, Wright A, Laszlo A, De Muynck A, Portaels F, Van Deun A, Wells C, Nunn P, Blanc L, Raviglione M (2006) Lancet 368:2142–2154

    Article  Google Scholar 

  3. Dye C (2006) Lancet 367:938–940

    Article  Google Scholar 

  4. Andrade CH, Salum LB, Pasqualoto KFM, Ferreira EI, Andricopulo AD (2008) Lett Drug Des Discov 5:377–387

    Article  CAS  Google Scholar 

  5. Andrade CH, Pasqualoto KFM, Zaim MH, Ferreira EI (2008) Braz J Pharm Sci 44:167–179

    CAS  Google Scholar 

  6. Haouz A, Vanheusden V, Munier-Lehmann H, Froeyen M, Herdewijn P, Van Calenbergh S, Delarue M (2003) J Biol Chem 278:4963–4971

    Article  CAS  Google Scholar 

  7. Munier-Lehmann H, Chafotte A, Pochet S, Labesse G (2001) Protein Sci 10:1195–1205

    Article  CAS  Google Scholar 

  8. Li de la Sierra I, Munier-Lehmann H, Gilles AM, Bârzu O, Delarue M (2001) J Mol Biol 311:87–100

    Article  CAS  Google Scholar 

  9. Vanheusden V, Munier-Lehmann H, Froeyen M, Busson R, Rozenski J, Herdewijn P, Van Calenbergh S (2004) J Med Chem 47:6187–6194

    Article  CAS  Google Scholar 

  10. Vanheusden V, Munier-Lehmann H, Pochet S, Herdewijn P, Van Calenbergh S (2002) Bioorg Med Chem Lett 12:2695–2698

    Article  CAS  Google Scholar 

  11. Vanheusden V, Van Rompaey P, Munier-Lehmann H, Pochet S, Herdewijn P, Van Calenbergh S (2003) Bioorg Med Chem Lett 13:3045–3048

    Article  CAS  Google Scholar 

  12. Van Daele I, Munier-Lehmann H, Froeyen M, Balzarini J, Van Calenbergh S (2007) J Med Chem 50:5281–5292

    Article  Google Scholar 

  13. Gopalakrishnan B, Aparna V, Jeevan J, Ravi M, Desiraju GR (2005) J Chem Inf Model 45:1101–1108

    Article  CAS  Google Scholar 

  14. Aparna V, Jeevan J, Ravi M, Desiraju GR, Gopalakrishnan B (2006) Bioorg Med Chem Lett 16:1014–1020

    Article  CAS  Google Scholar 

  15. Andrade CH, Pasqualoto KFM, Ferreira EI, Hopfinger AJ (2009) J Chem Inf Model 49:1070–1078

    Article  CAS  Google Scholar 

  16. Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, Duraiswami C (1997) J Am Chem Soc 119:10509–10524

    Article  CAS  Google Scholar 

  17. Blondin C, Serina L, Wiesmuller L, Gilles AM, Barzu (1994) Anal Biochem 220:219–222

    Article  CAS  Google Scholar 

  18. HyperChem Program Release 7.05 for Windows (2005) Hybercube Inc. Gainesville, FL

  19. Dewar MJSE, Zoebisch G, Healy EF, Stewart JJP (1985) J Am Chem Soc 107:3902–3909

    Article  CAS  Google Scholar 

  20. 4D-QSAR Package version 2.0 (1997) The Chem21 Group Inc. Lake Forest, IL

  21. Pasqualoto KFM, Ferreira EI, Santos OAF, Hopfinger AJ (2004) J Med Chem 47:3755–3764

    Article  CAS  Google Scholar 

  22. Romeiro NC, Albuquerque MG, Alencastro RB, Ravi M, Hopfinger AJ (2005) J Comput Aided Mol Des 19:385–400

    Article  CAS  Google Scholar 

  23. Doherty DC (2001) MOLSIM Package version 3.2. The Chem21 Group Inc, Lake Forest, IL

    Google Scholar 

  24. Ghose AK, Pritchett A, Crippen GM (1988) J Comput Chem 9:80–90

    Article  CAS  Google Scholar 

  25. Glen WG, Dunn WJ III, Scott DR (1989) Tetrahedron Comput Methodol 2:349–354

    Article  Google Scholar 

  26. Rogers DG, Hopfinger AJ (1994) J Chem Inf Comput Sci 34:854–866

    CAS  Google Scholar 

  27. Dunn WJ III, Rogers D (1996) In: Devillers J (ed) Genetic algorithms in molecular modeling. Academic Press, London

    Google Scholar 

  28. Friedman JH (1991) Ann Stat 19:1–141

    Article  Google Scholar 

  29. Discovery Studio Visualizer version 2.0 (2007) Accelrys Software Inc. San Diego, CA. http://accelrys.com/

  30. DeLano WL (2004) The Pymol Molecular Graphics System version 1.0. Delano Scientific LLC: Palo Alto, CA. http://www.pymol.org/

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Acknowledgments

The authors are grateful to the CAPES Foundation, a federal scientific agency of Brazil, for scholarship support. This work was also funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant 1 R21 GM075775. Information on Novel Preclinical Tools for Predictive ADME-Toxicology can be found at http://grants.nih.gov/grants/guide/rfa-files/RFA-RM-04-023.html. Links to nine initiatives are found here http://nihroadmap. nih.gov/initiatives.asp. Resources of the Laboratory of Molecular Modeling and Design at UNM and The Chem21 Group, Inc. were used in performing this work.

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Correspondence to Carolina Horta Andrade.

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Andrade, C.H., Pasqualoto, K.F.M., Ferreira, E.I. et al. 3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents. J Comput Aided Mol Des 24, 157–172 (2010). https://doi.org/10.1007/s10822-010-9323-y

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  • DOI: https://doi.org/10.1007/s10822-010-9323-y

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