Skip to main content
SpringerLink
Log in

Methods of Exploring Protein–Ligand Interactions to Guide Medicinal Chemistry Efforts

  • Protocol
  • First Online:
Computational Methods for GPCR Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1705))

Abstract

We present a number of techniques to analyze protein–ligand interactions in the context of medicinal chemistry: crystal Contract Preferences, Electrostatic Maps and pharmacophore screening using Hückel Theory. Contact Preferences is a statistical technique to predict hydrophobic and hydrophilic geometry in receptor active sites. Electrostatic Maps use the Poisson-Boltzmann Equation to model solvation effects and are particularly useful for predicting hydrophobic regions. Pharmacophore annotation with Hückel Theory provides finer detail of hydrogen bonding interactions, including CH..O interactions. Applications to AblK:Gleevec and CDK2 virtual screening are presented.

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 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Berstein FC, Koetzle TF, Williams GJB, Meyer EF Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The protein data bank: a computer-based archival file for macromolecular structures. J Mol Biol 112:535–542

    Article  Google Scholar 

  2. Connolly ML (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science 221:709–713

    Article  CAS  PubMed  Google Scholar 

  3. Groom CR, Bruno IJ, Lightfoot MP, Ward SC (2016) The Cambridge structural database. Acta Cryst B72:171–179

    Google Scholar 

  4. Laskowski RA, Thornton JM, Humblet C, Singh J (1998) X-SITE: use of empirically derived atomic packing preferences to identify Favorable interaction regions in the binding sites of proteins. J Mol Biol 259:175–201

    Article  Google Scholar 

  5. Nissink JWM, Verdonk ML, Klebe G (2000) Simple knowledge-based descriptors to predict protein-ligand interactions. Methodology and validation. J Comput Aided Mol Des 14:787–803

    Article  CAS  PubMed  Google Scholar 

  6. Labute P (2001) Contact preference maps. Molecular operating environment version 2001.01, Chemical Computing Group Inc., 1010 Sherbrooke St. W. #910, Montreal, QC, Canada H3A 2R7

    Google Scholar 

  7. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–856

    Article  CAS  PubMed  Google Scholar 

  8. Wade RC, Clark KJ, Goodford PJ (1993) Further development of hydrogen bond functions for use in determining energetically Favorable binding sites on molecules of known structure. 1. Ligand probe groups and the ability to form two hydrogen bonds. J Med Chem 36:140–147

    Article  CAS  PubMed  Google Scholar 

  9. Pastor M, Cruciani G, Clementi S (1997) Smart region definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships. J Med Chem 40:1455–1464

    Article  CAS  PubMed  Google Scholar 

  10. Melani F, Gratteri P, Adamo M, Bonaccini C (2001) FILO (field interaction ligand optimization): a simplex strategy for searching the optimal ligand interaction field in drug design. J Comput Aided Mol Des 15:57–66

    Article  CAS  PubMed  Google Scholar 

  11. Crivori P, Zamora I, Speed B, Orrenius C, Poggesi I (2004) Model based on GRID-derived descriptors for estimating CYP34A enzyme stability of potential drug candidates. J Comput Aided Mol Des 18:155–166

    Article  CAS  PubMed  Google Scholar 

  12. Gilson M, Sharp KA, Honig B (1987) Calculating electrostatic interactions in biomolecules: method and error assessment. J Comput Chem 9:327–335

    Article  Google Scholar 

  13. Sharp KA, Honig B (1990) Calculating Total electrostatic energies with the non-linear Poisson–Boltzmann equation. J Phys Chem 94:7684–7692

    Article  CAS  Google Scholar 

  14. Grant JA, Pickup BT, Nicholls A (2001) A smooth permittivity function for Poisson-Boltzmann solvation methods. J Comput Chem 22:608–640

    Article  CAS  Google Scholar 

  15. Labute P (2006) Electrostatic maps. Molecular operating environment version 2006.08, Chemical Computing Group Inc., 1010 Sherbrooke St. W. #910, Montreal, QC, Canada H3A 2R7

    Google Scholar 

  16. Clark SS, McLaughlin J, Timmons M, Pendergast AM, Ben-Neriah Y, Dow LW, Crist W, Rovera G, Smith SD, Witte ON (1988) Expression of a distinctive BCR-ABL oncogene in Ph1-positive acute lymphocytic Leukemia (ALL). Science 239:775–777

    Article  CAS  PubMed  Google Scholar 

  17. Cortes JE, Talpaz M, Beran M, O’Brien SM, Rios MB, Stass M, Kantarjian HM (1995) Philadelphia chromosome-negative chronic Myelogenous Leukemia with rearrangement of the breakpoint cluster region: long-term follow-up results. Cancer 75:464–470

    Article  CAS  PubMed  Google Scholar 

  18. Asaki T, Sugiyama Y, Hamamoto T, Higashioka M, Umehara M, Naito H, Niwa T (2006) Design and synthesis of 3-substituted Benzamide derivatives as bcr-abl kinase inhibitors. Bioorg Med Chem Lett 16:1421–1425

    Article  CAS  PubMed  Google Scholar 

  19. Halgren TA (1995) The Merck molecular force field. J Comput Chem 20:720–774

    Article  Google Scholar 

  20. Kimura S, Nalto H, Segawa H, Kuroda J, Yuasa T, Sato K, Yokota A, Kamitsuji Y, Kawata E, Ashihara E, Nakaya Y, Naruoka H, Wakayama T, Nasu K, Asaki T, Niwa T, Hirabayashi K, Maekawa T (2005) NS-187, a potent and selective dual Bcr-Abl/Lyn tyrosine kinase inhibitor is a novel agent for Imatinib-resistant Leukemia. Blood 106:3948–3954

    Article  CAS  PubMed  Google Scholar 

  21. Kovalenko A, Hirata F (1999) Self-consistent description of a metal-water Interface by the Kohn-sham density functional theory and the three dimensional reference interaction site model. J Chem Phys 110:10095–10112

    Article  CAS  Google Scholar 

  22. Gund P (1979) Pharmacophoric pattern searching and receptor mapping. Ann. Rep Med Chem 14:299–308

    Article  CAS  Google Scholar 

  23. Marshall GR, Barry CD, Bosshard HE, Dammkoehler RA, Dunn DA (1979) The conformational parameter in drug design: the active analog approach. In: Olson EC, Christoffersen RE (eds) Computer-assisted drug design. American Chemical Society, Columbus, OH, pp 205–226

    Chapter  Google Scholar 

  24. Martin YC (1992) 3D Database searching in drug design. J Med Chem 9:1649–1964

    Google Scholar 

  25. Böhm H-J, Brode S, Hesse U, Klebe G (1996) Oxygen and nitrogen in competitive situations: which is the hydrogen-bond acceptor? Chem Eur J 2:1509–1513

    Article  Google Scholar 

  26. Pierce AC, Sandretto KL, Bemis GW (2002) Kinase inhibitors and the case for CH…O hydrogen bonds in protein-ligand binding. Proteins 49:567–576

    Article  CAS  PubMed  Google Scholar 

  27. Gerber PR, Müller K (1995) MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry. J Comput Aided Mol Des 9:251–268

    Article  CAS  PubMed  Google Scholar 

  28. Gerber PR (1998) Charge distribution from a simple molecular orbital type calculation and non-bonding interaction terms in the force field MAB. J Comput Aided Mol Des 12:37–51

    Article  CAS  PubMed  Google Scholar 

  29. Labute P (2008) Protonate 3D: assignment of ionization states and hydrogen coordinates to macromolecular structures. Protein Struct Funct Bioinform 75:187–205

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chemical Computing Group Inc., 1010 Sherbrooke Street W, Suite 910, Montreal, QC, Canada, H3A 2R7

    Paul Labute

Authors
  1. Paul Labute
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Labute .

Editor information

Editors and Affiliations

  1. Evotec (UK) Ltd., Abingdon, Oxfordshire, United Kingdom

    Alexander Heifetz

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Labute, P. (2018). Methods of Exploring Protein–Ligand Interactions to Guide Medicinal Chemistry Efforts. In: Heifetz, A. (eds) Computational Methods for GPCR Drug Discovery. Methods in Molecular Biology, vol 1705. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7465-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7465-8_7

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7464-1

  • Online ISBN: 978-1-4939-7465-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation