Skip to main content
SpringerLink
Log in

A Computational Fragment-Based De Novo Design Protocol Guided by Ligand Efficiency Indices (LEI)

  • Protocol
  • First Online:
Fragment-Based Methods in Drug Discovery

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

Abstract

We present a new protocol aimed at the structure-based design of drug-like molecules using a fragment approach. It starts from a suitably placed and well-defined “base fragment” and then uses an incremental construction algorithm and a scoring function to grow the molecule into prioritized candidates. The selection of the most promising solutions for synthesis and validation is guided by the optimization of the calculated ligand efficiency indices known as binding efficiency index (BEI) and surface efficiency index (SEI), which allow the user to navigate proficiently in chemico-biological space. A test case for the protocol is exemplified here using published data for inhibitors of protein kinase B, aka AKT, a key enzyme in several signal transduction pathways. Our procedure was able to identify the main features responsible for the binding of inhibitors and guided the selection process towards molecules that included or resembled those shown as the most active in the original studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Murray CW, Rees DC (2009) The rise of fragment-based drug discovery. Nat Chem 1:187–192

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. Congreve M, Carr R, Murray C, Jhoti H (2003) A “rule of three” for fragment-based lead discovery? Drug Discov Today 8:876–877

    Article  PubMed  Google Scholar 

  4. Jhoti H, Williams G, Rees DC, Murray CW (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 

  5. Reynolds CH, Tounge BA, Bembenek SD (2008) Ligand binding efficiency: trends, physical basis, and implications. J Med Chem 51:2432–2438

    Article  CAS  PubMed  Google Scholar 

  6. Abad-Zapatero C (2013) Ligand efficiency indices for drug discovery. Academic Press London

    Google Scholar 

  7. Bembenek SD, Tounge BA, Reynolds CH (2009) Ligand efficiency and fragment-based drug discovery. Drug Discov Today 14:278–283

    Article  CAS  PubMed  Google Scholar 

  8. Abad-Zapatero C, Blasi D (2011) Ligand efficiency indices (LEIs): more than a simple efficiency yardstick. Mol Informatics 30:122–132

    Article  CAS  Google Scholar 

  9. Blasi D, Arsequell G, Valencia G, Nieto J, Planas A, Pinto M et al (2011) Retrospective mapping of SAR data for TTR protein in chemico-biological space using ligand efficiency indices as a guide to drug discovery strategies. Mol Informatics 30:161–167

    Article  CAS  Google Scholar 

  10. Tanaka D, Tsuda Y, Shiyama T, Nishimura T, Chiyo N, Tominaga Y et al (2010) A practical use of ligand efficiency indices out of the fragment-based approach: ligand efficiency-guided lead identification of soluble epoxide hydrolase inhibitors. J Med Chem 54:851–857

    Article  PubMed  Google Scholar 

  11. Schrodinger L. PyMOL molecular graphics system, version 1.5. 0.4. See http://pymol.org

  12. Saxty G, Woodhead SJ, Berdini V, Davies TG, Verdonk ML, Wyatt PG et al (2007) Identification of inhibitors of protein kinase B using fragment-based lead discovery. J Med Chem 50:2293–2296

    Article  CAS  PubMed  Google Scholar 

  13. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857

    Article  CAS  PubMed  Google Scholar 

  14. Klett J, Nuñez-Salgado A, Dos Santos HG, Cortés-Cabrera Á, Perona A, Gil-Redondo R et al (2012) MM-ISMSA: an ultrafast and accurate scoring function for protein-protein docking. J Chem Theor Comput 8:3395–3408

    Article  CAS  Google Scholar 

  15. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445

    Article  CAS  PubMed  Google Scholar 

  16. Schneider N, Lange G, Hindle S, Klein R, Rarey M (2013) A consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes: methods behind the HYDE scoring function. J Comput Aided Mol Des 27:15–29

    Article  CAS  PubMed  Google Scholar 

  17. Abad-Zapatero C (2007) Ligand efficiency indices for effective drug discovery. Expert Opin Drug Discov 2:469–488

    Article  CAS  PubMed  Google Scholar 

  18. Gill A, Cleasby A, Jhoti H (2005) The discovery of novel protein kinase inhibitors by using fragment-based high-throughput x-ray crystallography. Chembiochem 6:506–512

    Article  CAS  PubMed  Google Scholar 

  19. Boobbyer DN, Goodford PJ, McWhinnie PM, Wade RC (1989) New hydrogen-bond potentials for use in determining energetically favorable binding sites on molecules of known structure. J Med Chem 32:1083–1094

    Article  CAS  PubMed  Google Scholar 

  20. Treiber DK, Shah NP (2013) Ins and outs of kinase DFG motifs. Chem Biol 20:745–746

    Article  CAS  PubMed  Google Scholar 

  21. O'Boyle NM, Morley C, Hutchison GR (2008) Pybel: a python wrapper for the OpenBabel cheminformatics toolkit. Chem Cent J 2:5

    Article  PubMed Central  PubMed  Google Scholar 

  22. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  23. Sadowski J, Gasteiger J, Klebe G (1994) Comparison of automatic three-dimensional model builders using 639 X-ray structures. J Chem Inf Comput Sci 34:1000–1008

    Article  CAS  Google Scholar 

  24. Morreale A, Gil-Redondo R, Ortiz AR (2007) A new implicit solvent model for protein-ligand docking. Proteins Struct Funct Bioinf 67:606–616

    Article  CAS  Google Scholar 

  25. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  PubMed  Google Scholar 

  26. Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  PubMed  Google Scholar 

  27. Reulecke I, Lange G, Albrecht J, Klein R, Rarey M (2008) Towards an integrated description of hydrogen bonding and dehydration: decreasing false positives in virtual screening with the HYDE scoring function. ChemMedChem 3:885–897

    Article  CAS  PubMed  Google Scholar 

  28. Dolinsky TJ, Czodrowski P, Li H, Nielsen JE, Jensen JH, Klebe G et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:W522–W525

    Article  PubMed Central  PubMed  Google Scholar 

  29. Brenk R, Naerum L, Gradler U, Gerber HD, Garcia GA, Reuter K et al (2003) Virtual screening for submicromolar leads of tRNA-guanine transglycosylase based on a new unexpected binding mode detected by crystal structure analysis. J Med Chem 46:1133–1143

    Article  CAS  PubMed  Google Scholar 

  30. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y et al (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49:1455–1474

    Article  CAS  PubMed  Google Scholar 

  31. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminform 3:33

    Article  PubMed Central  PubMed  Google Scholar 

  32. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  PubMed  Google Scholar 

  33. Tanford C (1978) The hydrophobic effect and the organization of living matter. Science 200:1012–1018

    Article  CAS  PubMed  Google Scholar 

  34. Ruppert J, Welch W, Jain AN (1997) Automatic identification and representation of protein binding sites for molecular docking. Proc Natl Acad Sci U S A 6:524–533

    CAS  Google Scholar 

  35. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by grants from CICYT (SAF2009-13914-C02-02 to F.G. and SAF2012-39760-C02-02 to F.G. and A.M.) and Comunidad Autónoma de Madrid (S-BIO-0214-2006 [BIPEDD] and S2010-BMD-2457 [BIPEDD-2] to A.M. and F.G.). A.M. acknowledges financial support from Fundación Severo Ochoa through the AMAROUTO program. A.C.C. is the recipient of an FPU grant (ref. AP2009-0203) from the Spanish Ministerio de Educación.

Author information

Authors and Affiliations

  1. Unidad de Bioinformática, Centro de Biología Molecular Severo Ochoa (CSIC-UAM), Campus de Cantoblanco UAM, Madrid, Spain

    Álvaro Cortés-Cabrera & Antonio Morreale

  2. Área de Farmacología, Departamento de Ciencias Biomédicas, Unidad Asociada de I+D+I al CSIC, Universidad de Alcalá, Madrid, Spain

    Álvaro Cortés-Cabrera & Federico Gago

  3. Repsol Technology Center, Ctra. de Extremadura, A-5, km 18, Móstoles, Madrid, 28935, Spain

    Antonio Morreale

Authors
  1. Álvaro Cortés-Cabrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Federico Gago
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Morreale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonio Morreale .

Editor information

Editors and Affiliations

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

    Anthony E. Klon

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Cortés-Cabrera, Á., Gago, F., Morreale, A. (2015). A Computational Fragment-Based De Novo Design Protocol Guided by Ligand Efficiency Indices (LEI). 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_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2486-8_8

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2485-1

  • Online ISBN: 978-1-4939-2486-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation