A Molecular Dynamics Ensemble-Based Approach for the Mapping of Druggable Binding Sites

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 819)

Abstract

An expanding repertoire of “allosteric” drugs is revealing that structure-based drug design (SBDD) is not restricted to the “active site” of the target protein. Such compounds have been shown to bind distant regions of the protein topography, potentially providing higher levels of target specificity, reduced toxicity and access to new regions of chemical space. Unfortunately, the location of such allosteric pockets is not obvious in the absence of a bound crystal structure and the ability to predict their presence would be useful in the discovery of novel therapies. Here, we describe a method for the prediction of “druggable” binding sites that takes protein flexibility into account through the use of molecular dynamics (MD) simulation. By using a dynamic representation of the target, we are able to sample multiple protein conformations that may expose new drug-binding surfaces. We perform a fragment-based mapping analysis of individual structures in the MD ensemble using the FTMAP algorithm and then rank the most prolific probe-binding protein residues to determine potential “hot-spots” for further examination. This approach has recently been applied to a pair of human G-protein-coupled receptors (GPCRs), resulting in the detection of five potential allosteric sites.

Key words

Allosteric Molecular dynamics simulation Docking Binding site Drug design 

Notes

Acknowledgments

This work has been supported in part by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), the National Biomedical Computation Resource (NBCR), the Center for Theoretical Biological Physics (CTBP), San Diego Supercomputer Center (SDSC), and the NSF Supercomputer Centers.

References

  1. 1.
    Hardy JA, Wells JA. Searching for new allosteric sites in enzymes. Curr Opin Struct Biol 2004; 14: 706–15.PubMedCrossRefGoogle Scholar
  2. 2.
    Lewis JA, Lebois EP, Lindsley CW. Allosteric modulation of kinases and GPCRs: design principles and structural diversity. Curr Opin Chem Biol 2008; 12: 269–80.PubMedCrossRefGoogle Scholar
  3. 3.
    Christopoulos A. Allosteric binding sites on cell-surface receptors: novel targets for drug discovery. Nat Rev Drug Discov 2002; 1: 198–210.PubMedCrossRefGoogle Scholar
  4. 4.
    May LT, Leach K, Sexton PM, Christopoulos A. Allosteric modulation of G protein-coupled receptors. Annu Rev Pharmacol Toxicol 2007; 47: 1–51.PubMedCrossRefGoogle Scholar
  5. 5.
    Henrich S, Salo-Ahen OM, Huang B, Rippmann FF, Cruciani G, Wade RC. Computational approaches to identifying and characterizing protein binding sites for ligand design. J Mol Recognit 2009; 23: 209–19.Google Scholar
  6. 6.
    Perot S, Sperandio O, Miteva MA, Camproux AC, Villoutreix BO. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today 2010; 15: 656–67.PubMedCrossRefGoogle Scholar
  7. 7.
    Brenke R, Kozakov D, Chuang GY, Beglov D, Hall D, Landon MR, Mattos C, Vajda S. Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques. Bioinformatics 2009; 25: 621–7.PubMedCrossRefGoogle Scholar
  8. 8.
    Landon MR, Lancia DR, Jr., Yu J, Thiel SC, Vajda S. Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. J Med Chem 2007; 50: 1231–40.PubMedCrossRefGoogle Scholar
  9. 9.
    Landon MR, Lieberman RL, Hoang QQ, Ju S, Caaveiro JM, Orwig SD, Kozakov D, Brenke R, Chuang GY, Beglov D, Vajda S, Petsko GA, Ringe D. Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase. J Comput Aided Mol Des 2009; 23: 491–500.CrossRefGoogle Scholar
  10. 10.
    Carlson HA, McCammon JA. Accommodating protein flexibility in computational drug design. Mol Pharmacol 2000; 57: 213–8.PubMedGoogle Scholar
  11. 11.
    Teague SJ. Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2003; 2: 527–41.PubMedCrossRefGoogle Scholar
  12. 12.
    Forman-Kay JD. The ‘dynamics’ in the thermodynamics of binding. Nat Struct Biol 1999; 6: 1086–7.PubMedCrossRefGoogle Scholar
  13. 13.
    Verkhivker GM, Bouzida D, Gehlhaar DK, Rejto PA, Freer ST, Rose PW. Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective. Curr Opin Struct Biol 2002; 12: 197-203.PubMedCrossRefGoogle Scholar
  14. 14.
    Cozzini P, Kellogg GE, Spyrakis F, Abraham DJ, Costantino G, Emerson A, Fanelli F, Gohlke H, Kuhn LA, Morris GM, Orozco M, Pertinhez TA, Rizzi M, Sotriffer CA. Target flexibility: an emerging consideration in drug discovery and design. J Med Chem 2008; 51: 6237–55.PubMedCrossRefGoogle Scholar
  15. 15.
    Henzler AM, Rarey M. In Pursuit of Fully Flexible Protein-Ligand Docking: Modeling the Bilateral Mechanism of Binding. Molecular Informatics 2010; 29: 164–173.CrossRefGoogle Scholar
  16. 16.
    Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol 2002; 9: 646–52.PubMedCrossRefGoogle Scholar
  17. 17.
    Amaro RE, Li WW. Emerging methods for ensemble-based virtual screening. Curr Top Med Chem 2010; 10: 3–13.PubMedCrossRefGoogle Scholar
  18. 18.
    Ivetac A, McCammon JA. Mapping the druggable allosteric space of g-protein coupled receptors: a fragment-based molecular dynamics approach. Chem Biol Drug Des 2010; 76: 201–17.PubMedGoogle Scholar
  19. 19.
    Landon MR, Amaro RE, Baron R, Ngan CH, Ozonoff D, McCammon JA, Vajda S. Novel druggable hot spots in avian influenza neuraminidase H5N1 revealed by computational solvent mapping of a reduced and representative receptor ensemble. Chem Biol Drug Des 2008; 71: 106–16.PubMedCrossRefGoogle Scholar
  20. 20.
    Hansson T, Oostenbrink C, van Gunsteren W. Molecular dynamics simulations. Curr Opin Struct Biol 2002; 12: 190–6.PubMedCrossRefGoogle Scholar
  21. 21.
    Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ. GROMACS: fast, flexible, and free. J Comput Chem 2005; 26: 1701–18.CrossRefGoogle Scholar
  22. 22.
    Scott WRP, Hunenberger PH, Tironi IG, Mark AE, Billeter SR, Fennen J, Torda AE, Huber T, Kruger P, van Gunsteren WF. The GROMOS biomolecular simulation program package. Journal of Physical Chemistry A 1999; 103: 3596–3607.CrossRefGoogle Scholar
  23. 23.
    Caves LS, Evanseck JD, Karplus M. Locally accessible conformations of proteins: multiple molecular dynamics simulations of crambin. Protein Sci 1998; 7: 649–66.PubMedGoogle Scholar
  24. 24.
    Ivetac A, McCammon JA. Elucidating the inhibition mechanism of HIV-1 non-nucleoside reverse transcriptase inhibitors through multicopy molecular dynamics simulations. J Mol Biol 2009; 388: 644–58.PubMedCrossRefGoogle Scholar
  25. 25.
    Hamelberg D, Mongan J, McCammon JA. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 2004; 120: 11919–29.PubMedCrossRefGoogle Scholar
  26. 26.
    Grubmüller H. Predicting slow structural transitions in macromolecular systems: Conformational flooding. Physical Review E 1995; 52: 2893.CrossRefGoogle Scholar
  27. 27.
    Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters 1999; 314: 141–151.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Chemistry and Biochemistry, Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA
  2. 2.Howard Hughes Medical Institute, Departments of Chemistry and Biochemistry and Pharmacology, Center for Theoretical Biological PhysicsUniversity of California, San DiegoLa JollaUSA

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