Advertisement

Journal of Computer-Aided Molecular Design

, Volume 28, Issue 12, pp 1205–1215 | Cite as

Structure–activity relationships of thiostrepton derivatives: implications for rational drug design

  • Antje Wolf
  • Sebastian Schoof
  • Sascha Baumann
  • Hans-Dieter Arndt
  • Karl N. KirschnerEmail author
Article

Abstract

The bacterial ribosome is a major target of naturally occurring thiopeptides antibiotics. Studying thiopeptide (e.g. thiostrepton) binding to the GAR’s 23S·L11 ribosomal subunit using docking methods is challenging. Regarding the target, the binding site is composed of a flexible protein–RNA nonbonded interface whose available crystal structure is of medium resolution. Regarding the ligands, the thiopeptides are chemically complex, flexible, and contain macrocycles. In this study we developed a combined MD–docking–MD workflow that allows us to study thiopeptide–23S·L11 binding. It is shown that docking thiostrepton-like ligands to an MD-refined receptor structure instead of the medium resolution crystal leads to better convergence to the native-like docking pose and a better reproduction of experimental binding affinities. By applying an energy decomposition analysis, we identify key structural binding elements within GAR’s rRNA–protein binding site and within the ligand structures.

Keywords

Thiostrepton GTPase-associated region Docking  MM-PBSA Antibiotics 

References

  1. 1.
    Projan SJ (2003) Why is big pharma getting out of antibacterial drug discovery? Curr Opin Microbiol 6:427–430CrossRefGoogle Scholar
  2. 2.
    Fabbretti A, Gualerzi CO, Brandi L (2011) How to cope with the quest for new antibiotics. FEBS Lett 585:1673–1681CrossRefGoogle Scholar
  3. 3.
    Moellering RC Jr (2011) Discovering new antimicrobial agents. Int J Antimicrob Agents 37:2–9CrossRefGoogle Scholar
  4. 4.
    Coates ARM, Hu Y (2007) Novel approaches to developing new antibiotics for bacterial infections. Br J Pharmacol 152:1147–1154CrossRefGoogle Scholar
  5. 5.
    Boucher HW, Talbot GH, Bradley JS, Edwards JE, Gilbert D et al (2009) Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin Infect Dis 48:1–12CrossRefGoogle Scholar
  6. 6.
    Gwynn MN, Portnoy A, Rittenhouse SF, Payne DJ (2010) Challenges of antibacterial discovery revisited. Ann N Y Acad Sci 1213:5–19CrossRefGoogle Scholar
  7. 7.
    Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL (2007) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Discov 6:29–40CrossRefGoogle Scholar
  8. 8.
    Jones D (2010) The antibacterial lead discovery challenge. Nat Rev Drug Discov 9:751–752CrossRefGoogle Scholar
  9. 9.
    Braine T (2011) Race against time to develop new antibiotics. Bull World Health Organ 89:88–89CrossRefGoogle Scholar
  10. 10.
    Fischbach MA, Walsh CT (2009) Antibiotics for emerging pathogens. Science 325:1089–1093CrossRefGoogle Scholar
  11. 11.
    Infectious Diseases Society of America (2010) The 10 × ’20 initiative: pursuing a global commitment to develop 10 new antibacterial drugs by 2020. Clin Infect Dis 50:1081–1083CrossRefGoogle Scholar
  12. 12.
    Wilson DN, Nierhaus KH (2004) Antibiotics and the inhibition of ribosome function. In: Nierhaus KH, Wilson DN (eds) Protein synthesis and ribosome structure. Wiley, Weinheim, pp 449–527Google Scholar
  13. 13.
    Bagley MC, Dale JW, Merritt EA, Xiong X (2005) Thiopeptide antibiotics. Chem Rev 105:685–714CrossRefGoogle Scholar
  14. 14.
    Harms JM, Wilson DN, Schlünzen F, Connell SR, Stachelhaus T et al (2008) Translational regulation via L11: molecular switches on the ribosome turned on and off by thiostrepton and micrococcin. Mol Cell 30:26–38CrossRefGoogle Scholar
  15. 15.
    Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303:1813–1818CrossRefGoogle Scholar
  16. 16.
    Taft CA, Da Silva VB, Da Silva CH (2008) Current topics in computer-aided drug design. J Pharm Sci 97:1089–1098CrossRefGoogle Scholar
  17. 17.
    Fischer E (1894) Einfluss der Configuration auf die Wirkung der enzyme. Berichte der deutschen chemischen Gesellschaft 27:2985–2993CrossRefGoogle Scholar
  18. 18.
    Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1:727–730CrossRefGoogle Scholar
  19. 19.
    Ma BY, Kumar S, Tsai CJ, Nussinov R (1999) Folding funnels and binding mechanisms. Protein Eng 12:713–720CrossRefGoogle Scholar
  20. 20.
    Tsai CJ, Ma B, Nussinov R (1999) Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci USA 96:9970–9972CrossRefGoogle Scholar
  21. 21.
    Tsai CJ, Kumar S, Ma B, Nussinov R (1999) Folding funnels, binding funnels, and protein function. Protein Sci 8:1181–1190CrossRefGoogle Scholar
  22. 22.
    Boehr DD, Nussinov R, Wright PE (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 5:789–796CrossRefGoogle Scholar
  23. 23.
    Bond CS, Shaw MP, Alphey MS, Hunter WN (2001) Structure of the macrocycle thiostrepton solved using the anomalous dispersion contribution of sulfur. Acta Crystallogr D 57:755–758CrossRefGoogle Scholar
  24. 24.
    Schoof S, Baumann S, Ellinger B, Arndt HD (2009) A fluorescent probe for the 70 S-ribosomal GTPase-associated center. ChemBioChem 10:242–245CrossRefGoogle Scholar
  25. 25.
    Myers CL, Hang PC, Ng G, Yuen J, Honek JF (2010) Semi-synthetic analogues of thiostrepton delimit the critical nature of tail region modifications in the control of protein biosynthesis and antibacterial activity. Bioorg Med Chem 18:4231–4237CrossRefGoogle Scholar
  26. 26.
    Nicolaou KC, Zak M, Rahimipour S, Estrada AA, Lee SH et al (2005) Discovery of a biologically active thiostrepton fragment. J Am Chem Soc 127:15042–15044CrossRefGoogle Scholar
  27. 27.
    Starosta AL, Qin H, Mikolajka A, Leung GYC, Schwinghammer K et al (2009) Identification of distinct thiopeptide-antibiotic precursor lead compounds using translation machinery assays. Chem Biol 16:1087–1096CrossRefGoogle Scholar
  28. 28.
    Li W, Sengupta J, Rath BK, Frank J (2006) Functional conformations of the L11-ribosomal RNA complex revealed by correlative analysis of cryo-EM and molecular dynamics simulations. RNA 12:1240–1253CrossRefGoogle Scholar
  29. 29.
    Lee D, Walsh JD, Yu P, Markus MA, Choli-Papadopoulou T et al (2007) The structure of free L11 and functional dynamics of L11 in free, L11-rRNA(58 nt) binary and L11-rRNA(58 nt)-thiostrepton ternary complexes. J Mol Biol 367:1007–1022CrossRefGoogle Scholar
  30. 30.
    Jonker HRA, Ilin S, Grimm SK, Wöhnert J, Schwalbe H (2007) L11 domain rearrangement upon binding to RNA and thiostrepton studied by NMR spectroscopy. Nucleic Acids Res 35:441–454CrossRefGoogle Scholar
  31. 31.
    Jonker HRA, Baumann S, Wolf A, Schoof S, Hiller F et al (2011) NMR structures of thiostrepton derivatives for characterization of the ribosomal binding site. Angew Chem Int Ed Engl 50:3308–3312CrossRefGoogle Scholar
  32. 32.
    CORINA. Molecular Networks GmbH. http://www.molecular-networks.com/products/corina
  33. 33.
    Wolf A, Reith D, Kirschner KN (2011) Thiopeptide antibiotics and the ribosomal 23S–L11 subunit: a challenging use case for semi-automatic force-field development. In: Carloni P, Hansmann UH, Lippert T, Meinke JH, Mohanty S et al (eds) From computational biophysics to systems biology (CBSB11). Jülich, Germany, pp 65–69Google Scholar
  34. 34.
    Gohlke H, Kiel C, Case DA (2003) Insights into protein–protein binding by binding free energy calculation and free energy decomposition for the Ras–Raf and Ras–RalGDS complexes. J Mol Biol 330:891–913CrossRefGoogle Scholar
  35. 35.
    Wolf A, Baumann S, Arndt HD, Kirschner KN (2012) Influence of thiostrepton binding on the ribosomal GTPase associated region characterized by molecular dynamics simulation. Bioorg Med Chem 20:7194–7205CrossRefGoogle Scholar
  36. 36.
    Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation and use of the MM-PBSA approach for drug discovery. J Med Chem 48:4040–4048CrossRefGoogle Scholar
  37. 37.
    Li Y, Liu Z, Wang R (2010) Test MM-PB/SA on true conformational ensembles of protein–ligand complexes. J Chem Inf Model 50:1682–1692CrossRefGoogle Scholar
  38. 38.
    Cameron DM, Thompson J, Gregory ST, March PE, Dahlberg AE (2004) Thiostrepton-resistant mutants of Thermus thermophilus. Nucleic Acids Res 32:3220–3227CrossRefGoogle Scholar
  39. 39.
    Rosendahl G, Douthwaite S (1994) The antibiotics micrococcin and thiostrepton interact directly with 23S rRNA nucleotides 1067A and 1095A. Nucleic Acids Res 22:357–363CrossRefGoogle Scholar
  40. 40.
    Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132CrossRefGoogle Scholar
  41. 41.
    Genheden S, Ryde U (2012) Will molecular dynamics simulations of proteins ever reach equilibrium? Phys Chem Chem Phys 14:8662–8677CrossRefGoogle Scholar
  42. 42.
    Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82CrossRefGoogle Scholar
  43. 43.
    Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28:1145–1152CrossRefGoogle Scholar
  44. 44.
    Page CS, Bates PA (2006) Can MM-PBSA calculations predict the specificities of protein kinase inhibitors? J Comput Chem 27:1990–2007CrossRefGoogle Scholar
  45. 45.
    Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J Comput Chem 32:866–877CrossRefGoogle Scholar
  46. 46.
    Baumann S, Schoof S, Bolten M, Haering C, Takagi M et al (2010) Molecular determinants of microbial resistance to thiopeptide antibiotics. J Am Chem Soc 132:6973–6981CrossRefGoogle Scholar
  47. 47.
    Case DA, Cheatham TE, Darden T, Gohlke H, Luo R et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688CrossRefGoogle Scholar
  48. 48.
    Ryckaert JP, Ciccotti G, Berendsen H (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  49. 49.
    Essmann U, Perera L, Berkowitz ML, Darden T, Lee H et al (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593CrossRefGoogle Scholar
  50. 50.
    Hornak V, Abel R, Okur A, Strockbine B, Roitberg A et al (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725CrossRefGoogle Scholar
  51. 51.
    Pérez A, Marchán I, Svozil D, Sponer J, Cheatham TE et al (2007) Refinement of the AMBER force field for nucleic acids: improving the description of alpha/gamma conformers. Biophys J 92:3817–3829CrossRefGoogle Scholar
  52. 52.
    Morris GM, Goodsell DS, Huey R, Hart WE, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662CrossRefGoogle Scholar
  53. 53.
    Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55:383–394CrossRefGoogle Scholar
  54. 54.
    Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20:217–230CrossRefGoogle Scholar
  55. 55.
    Pearlman DA, Charifson PS (2001) Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system. J Med Chem 44:3417–3423CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Antje Wolf
    • 1
    • 2
  • Sebastian Schoof
    • 3
  • Sascha Baumann
    • 4
  • Hans-Dieter Arndt
    • 5
  • Karl N. Kirschner
    • 6
    • 7
    Email author
  1. 1.Department BioinformaticsFraunhofer-Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
  2. 2.Department of Life Science InformaticsBonn-Aachen International Center for Information Technology (B-IT)BonnGermany
  3. 3.BASF SELudwigshafenGermany
  4. 4.Department of Microbial Natural Products, Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI)Saarland UniversitySaarbrückenGermany
  5. 5.Institute of Organic Chemistry and Macromolecular ChemistryFriedrich Schiller UniversityJenaGermany
  6. 6.Department of Simulation EngineeringFraunhofer-Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
  7. 7.Bonn-Rhein-Sieg University of Applied SciencesSankt AugustinGermany

Personalised recommendations