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Virtual Fragment Preparation for Computational Fragment-Based Drug Design

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Fragment-Based Methods in Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1289))

Abstract

Fragment-based drug design (FBDD) has become an important component of the drug discovery process. The use of fragments can accelerate both the search for a hit molecule and the development of that hit into a lead molecule for clinical testing. In addition to experimental methodologies for FBDD such as NMR and X-ray Crystallography screens, computational techniques are playing an increasingly important role. The success of the computational simulations is due in large part to how the database of virtual fragments is prepared. In order to prepare the fragments appropriately it is necessary to understand how FBDD differs from other approaches and the issues inherent in building up molecules from smaller fragment pieces. The ultimate goal of these calculations is to link two or more simulated fragments into a molecule that has an experimental binding affinity consistent with the additive predicted binding affinities of the virtual fragments. Computationally predicting binding affinities is a complex process, with many opportunities for introducing error. Therefore, care should be taken with the fragment preparation procedure to avoid introducing additional inaccuracies.

This chapter is focused on the preparation process used to create a virtual fragment database. Several key issues of fragment preparation which affect the accuracy of binding affinity predictions are discussed. The first issue is the selection of the two-dimensional atomic structure of the virtual fragment. Although the particular usage of the fragment can affect this choice (i.e., whether the fragment will be used for calibration, binding site characterization, hit identification, or lead optimization), general factors such as synthetic accessibility, size, and flexibility are major considerations in selecting the 2D structure. Other aspects of preparing the virtual fragments for simulation are the generation of three-dimensional conformations and the assignment of the associated atomic point charges.

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References

  1. Jencks WP (1981) On the attribution and additivity of binding energies. Proc Natl Acad Sci U S A 78:4046–4050

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Böhm HJ (1995) Site-directed structure generation by fragment-joining. Perspect Drug Discov Design 3:21–33

    Article  Google Scholar 

  3. Shuker SB, Hajduk PJ, Meadows RP et al (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274:1531–1534

    Article  CAS  PubMed  Google Scholar 

  4. Nienaber VL, Richardson PL, Klighofer V et al (2000) Discovering novel ligands for macromolecules using X-ray crystallographic screening. Nat Biotechnol 18:1105–1108

    Article  CAS  PubMed  Google Scholar 

  5. Erlanson DA (2006) Fragment-based lead discovery: a chemical update. Curr Opin Biotechnol 17:643–652

    Article  CAS  PubMed  Google Scholar 

  6. Murray CW, Verdonk ML, Rees DC (2012) Experiences in fragment-based drug discovery. Trends Pharmacol Sci 33:224–232

    Article  CAS  PubMed  Google Scholar 

  7. Erlanson DA, McDowell RS, O'Brien T (2004) Fragment-based drug discovery. J Med Chem 47:3463–3482

    Article  CAS  PubMed  Google Scholar 

  8. Rees DC, Congreve M, Murray CW et al (2004) Fragment-based lead discovery. Nat Rev Drug Discov 3:660–672

    Article  CAS  PubMed  Google Scholar 

  9. Hajduk PJ, Greer J (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 6:211–219

    Article  CAS  PubMed  Google Scholar 

  10. Congreve M, Chessari G, Tisi D et al (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680

    Article  CAS  PubMed  Google Scholar 

  11. Gozalbes R, Carbajo RJ, Pineda-Lucena A (2010) Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery. Curr Med Chem 17:1769–1794

    Article  CAS  PubMed  Google Scholar 

  12. Konteatis ZD (2010) In silico fragment-based drug design. Expert Opin Drug Discov 5:1047–1065

    Article  CAS  PubMed  Google Scholar 

  13. Fink T, Bruggesser H, Reymond JL (2005) Virtual exploration of the small-molecule chemical universe below 160 daltons. Angew Chem Int Ed 44:1504–1508

    Article  CAS  Google Scholar 

  14. Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16:3–50

    Article  CAS  PubMed  Google Scholar 

  15. Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41:856–864

    Article  CAS  PubMed  Google Scholar 

  16. Schuffenhauer A, Ruedisser S, Marzinzik A et al (2005) Library design for fragment based screening. Curr Top Med Chem 751–762

    Google Scholar 

  17. Klon AE, Konteatis Z, Meshkat SN et al (2011) Fragment and protein simulation methods in fragment based drug design. Drug Dev Res 72:130–137

    Article  CAS  Google Scholar 

  18. Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48:2518–2525

    Article  CAS  PubMed  Google Scholar 

  19. Moffet K, Konteatis Z, Nguyen D et al (2011) Discovery of a novel class of non-ATP site DFG-out state p38 inhibitors utilizing computationally assisted virtual fragment-based drug design (vFBDD). Bioorg Med Chem Lett 21:7155–7165

    Article  Google Scholar 

  20. Guarnieri F, Mezei M (1996) Simulated annealing of chemical potential: a general procedure for locating bound waters. Application to the study of the differential hydration propensities of the major and minor grooves of DNA. J Am Chem Soc 118:8493–8494

    Article  CAS  Google Scholar 

  21. Moore WR (2005) Maximizing discovery efficiency with a computationally driven fragment approach. Curr Opin Drug Discov Devel 8:355–364

    CAS  PubMed  Google Scholar 

  22. Clark M, Guarnieri F, Shkurko I et al (2006) Grand canonical Monte Carlo simulation of ligand-protein binding. J Chem Inf Model 46:231–242

    Article  CAS  PubMed  Google Scholar 

  23. Clark M, Meshkat S, Wiseman J (2009) Grand canonical free-energy calculation of protein-ligand binding. J Chem Inf Model 49:934–943

    Article  CAS  PubMed  Google Scholar 

  24. Clark M, Meshkat S, Talbot GT et al (2009) Fragment-based computation of binding free energies by systematic sampling. J Chem Inf Model 49:1901–1913

    Article  CAS  PubMed  Google Scholar 

  25. 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

    Article  CAS  Google Scholar 

  26. Frisch MJ, Trucks GW, Schlegel HB et al (1998) Gaussian 98 (revision A9). Gaussian, Inc., Pittsburgh, PA

    Google Scholar 

  27. Clark M, Meshkat S, Talbot G et al (2009) Developing technologies in biodefense research: computational drug design. Drug Dev Res 70:279–287

    Article  CAS  Google Scholar 

  28. Congreve M, Carr R, Murray C et al (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877

    Article  PubMed  Google Scholar 

  29. Lipinski CA, Lombardo F, Dominy BW et al (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  30. Jhoti H, Williams G, Rees DC et al (2013) The ‘rule of three’ for fragment-based drug discovery: where are we now? Nat Rev Drug Discov 12:644

    Article  CAS  PubMed  Google Scholar 

  31. Köster H, Craan T, Brass S et al (2011) A small nonrule of 3 compatible fragment library provides high hit rate of endothiapepsin crystal structures with various fragment chemotypes. J Med Chem 54:7784–7796

    Article  PubMed  Google Scholar 

  32. Konteatis ZD, Klon AE, Zou J et al (2011) Computational approach to de novo discovery of fragment binding for novel protein states. Methods Enzymol 493:357–380

    Article  CAS  PubMed  Google Scholar 

  33. Pargellis C, Tong L, Churchill L et al (2002) Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site. J Nat Struct Biol 9:268–272

    Article  CAS  Google Scholar 

  34. Ludington JL, Fujimoto TT, Hollinger FP (2004) Determining partial atomic charges for fragments used in de novo drug design. 228th ACS national meeting, Philadelphia, PA (Poster)

    Google Scholar 

  35. Mohamadi F, Richard NGJ, Guida WC et al (1990) Macromodel – an integrated software system for modeling organic and bioorganic molecules using molecular mechanics. J Comput Chem 11:440–467

    Article  CAS  Google Scholar 

  36. Weiner SJ, Kollman PA, Case DA et al (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. J Am Chem Soc 106:765–784

    Article  CAS  Google Scholar 

  37. 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 

  38. Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38:3098

    Article  CAS  PubMed  Google Scholar 

  39. Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785

    Article  CAS  Google Scholar 

  40. Becke AD (1993) Density‐functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652

    Article  CAS  Google Scholar 

  41. Hariharan PC, Pople JA (1973) The influence of polarization functions on molecular orbital hydrogenation energies. Theor Chim Acta 28:213–222

    Article  CAS  Google Scholar 

  42. Qiu D, Shenkin PS, Hollinger FP et al (1997) The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate Born radii. J Phys Chem A 101:3005–3014

    Google Scholar 

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Acknowledgements

The author thanks the following colleagues for their contributions to the Locus technology and this approach to virtual fragment preparation: F. Guarnieri, Z. Konteatis, T. Fujimoto, F. Hollinger, M. Clark, J. Wiseman, A. Klon, J. Zou, S. Meshkat, G. Talbot, K. Milligan, and W. Chiang. The author also thanks D. Ludington for editing assistance.

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Authors and Affiliations

  1. Locus Pharmaceuticals, Inc., Blue Bell, PA, USA

    Jennifer L. Ludington

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  1. Jennifer L. Ludington
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Corresponding author

Correspondence to Jennifer L. Ludington .

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Editors and Affiliations

  1. Pennsylvania Drug Discovery Institute, Doylestown, Pennsylvania, USA

    Anthony E. Klon

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Ludington, J.L. (2015). Virtual Fragment Preparation for Computational Fragment-Based Drug Design. In: Klon, A. (eds) Fragment-Based Methods in Drug Discovery. Methods in Molecular Biology, vol 1289. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2486-8_4

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  • DOI: https://doi.org/10.1007/978-1-4939-2486-8_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2485-1

  • Online ISBN: 978-1-4939-2486-8

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