Binding Site Druggability Assessment in Fragment-Based Drug Design

  • Yu Zhou
  • Niu HuangEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1289)


Target druggability refers to the propensity that a particular target is amenable to bind high-affinity drug-like molecules. A robust yet accurate computational assessment of target druggability would greatly benefit the fields of chemical genomics and drug discovery. Here, we illustrate a structure-based computational protocol to quantitatively assess the target binding-site druggability via in silico screening a fragment-like compound library. In particular, we provide guidelines, suggestions, and critical thoughts on different aspects of this computational protocol, including: construction of fragment library, preparation of target structure, in silico fragment screening, and analysis of druggability.

Key words

Druggability assessment Fragment screening Molecular docking MM-GB/SA rescoring Hit rate 



The Chinese Ministry of Science and Technology “973” Grant 2011CB812402 (to N.H.) is acknowledged for financial support, Shoichet Lab at UCSF for the DOCK3.5.54 program and Jacobson Lab at UCSF for PLOP.


  1. 1.
    Fauman EB, Rai BK, Huang ES (2011) Structure-based druggability assessment—identifying suitable targets for small molecule therapeutics. Curr Opin Chem Biol 15:463–468CrossRefPubMedGoogle Scholar
  2. 2.
    Brady GP Jr, Stouten PF (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14:383–401CrossRefPubMedGoogle Scholar
  3. 3.
    Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15(359–363):389Google Scholar
  4. 4.
    Laskowski RA (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13(323–330):307–328Google Scholar
  5. 5.
    Liang J, Edelsbrunner H, Woodward C (1998) Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci 7:1884–1897CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    Soga S, Shirai H, Kobori M, Hirayama N (2007) Use of amino acid composition to predict ligand-binding sites. J Chem Inf Model 47:400–406CrossRefPubMedGoogle Scholar
  7. 7.
    Stuart AC, Ilyin VA, Sali A (2002) LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures. Bioinformatics 18:200–201CrossRefPubMedGoogle Scholar
  8. 8.
    An J, Totrov M, Abagyan R (2004) Comprehensive identification of “druggable” protein ligand binding sites. Genome Inform 15:31–41PubMedGoogle Scholar
  9. 9.
    Glaser F, Morris RJ, Najmanovich RJ, Laskowski RA, Thornton JM (2006) A method for localizing ligand binding pockets in protein structures. Proteins 62:479–488CrossRefPubMedGoogle Scholar
  10. 10.
    Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1:727–730CrossRefPubMedGoogle Scholar
  11. 11.
    Blundell TL, Sibanda BL, Montalvao RW, Brewerton S, Chelliah V, Worth CL, Harmer NJ, Davies O, Burke D (2006) Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery. Philos Trans R Soc Lond B Biol Sci 361:413–423CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Fauman EB, Hopkins AL, Groom CR (2003) Structural bioinformatics in drug discovery. Methods Biochem Anal 44:477–497PubMedGoogle Scholar
  13. 13.
    Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 25:71–75CrossRefPubMedGoogle Scholar
  14. 14.
    Sheridan RP, Maiorov VN, Holloway MK, Cornell WD, Gao YD (2010) Drug-like density: a method of quantifying the “bindability” of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank. J Chem Inf Model 50:2029–2040CrossRefPubMedGoogle Scholar
  15. 15.
    Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389CrossRefPubMedGoogle Scholar
  16. 16.
    Nayal M, Honig B (2006) On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins 63:892–906CrossRefPubMedGoogle Scholar
  17. 17.
    Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48:2518–2525CrossRefPubMedGoogle Scholar
  18. 18.
    Huang N, Jacobson MP (2010) Binding-site assessment by virtual fragment screening. PLoS One 5:e10109CrossRefPubMedCentralPubMedGoogle Scholar
  19. 19.
    Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Lorber DM, Shoichet BK (2005) Hierarchical docking of databases of multiple ligand conformations. Curr Top Med Chem 5:739–749CrossRefPubMedCentralPubMedGoogle Scholar
  22. 22.
    Wei BQ, Baase WA, Weaver LH, Matthews BW, Shoichet BK (2002) A model binding site for testing scoring functions in molecular docking. J Mol Biol 322:339–355CrossRefPubMedGoogle Scholar
  23. 23.
    Jacobson MP, Kaminski GA, Friesner RA, Rapp CS (2002) Force field validation using protein side chain prediction. J Phys Chem B 106:11673–11680CrossRefGoogle Scholar
  24. 24.
    Jacobson MP, Pincus DL, Rapp CS, Day TJ, Honig B, Shaw DE, Friesner RA (2004) A hierarchical approach to all-atom protein loop prediction. Proteins 55:351–367CrossRefPubMedGoogle Scholar
  25. 25.
    Zhu K, Shirts MR, Friesner RA, Jacobson MP (2007) Multiscale optimization of a truncated newton minimization algorithm and application to proteins and protein-ligand complexes. J Chem Theory Comput 3:640–648CrossRefGoogle Scholar
  26. 26.
    Ihlenfeldt WD, Takahashi Y, Abe S, Sasaki S (1994) Computation and management of chemical properties in CACTVS: an extensible networked approach toward modularity and flexibility. J Chem Inf Comput Sci 34:109–116CrossRefGoogle Scholar
  27. 27.
    Voigt JH, Bienfait B, Wang S, Nicklaus MC (2001) Comparison of the NCI open database with seven large chemical structural databases. J Chem Inf Comput Sci 41:702–712CrossRefPubMedGoogle Scholar
  28. 28.
    Huang N, Kalyanaraman C, Bernacki K, Jacobson MP (2006) Molecular mechanics methods for predicting protein-ligand binding. Phys Chem Chem Phys 8(44):5166–5177CrossRefPubMedGoogle Scholar
  29. 29.
    Huang N, Kalyanaraman C, Irwin JJ, Jacobson MP (2006) Physics-based scoring of protein-ligand complexes: enrichment of known inhibitors in large-scale virtual screening. J Chem Inf Model 46:243–253CrossRefPubMedGoogle Scholar
  30. 30.
    Connolly ML (1983) Solvent-accessible surfaces of proteins and nucleic acids. Science 221:709–713CrossRefPubMedGoogle Scholar
  31. 31.
    Ferrini TE, Huang CC, Jarvis LE, Roberts L (1988) The MIDAS display system. J Mol Graph 6:13–27CrossRefGoogle Scholar
  32. 32.
    Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288CrossRefPubMedGoogle Scholar
  33. 33.
    Meng EC, Shoichet BK, Kuntz ID (1992) Automated docking with grid-based energy evaluation. J Comput Chem 13:505–524CrossRefGoogle Scholar
  34. 34.
    Nicholls A, Honig B (1991) A raid finite-difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation. J Comput Chem 12:435–445CrossRefGoogle Scholar
  35. 35.
    Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50:1561–1573CrossRefPubMedGoogle Scholar
  36. 36.
    Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236CrossRefGoogle Scholar
  37. 37.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487CrossRefGoogle Scholar
  38. 38.
    Gallicchio E, Zhang LY, Levy RM (2002) The SGB/NP hydration free energy model based on the surface generalized born solvent reaction field and novel nonpolar hydration free energy estimators. J Comput Chem 23:517–529CrossRefPubMedGoogle Scholar
  39. 39.
    Ghosh A, Rapp CS, Friesner RA (1998) Generalized born model based on a surface integral formulation. J Phys Chem B 102:10983–10990CrossRefGoogle Scholar
  40. 40.
    Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) The maximal affinity of ligands. Proc Natl Acad Sci U S A 96:9997–10002CrossRefPubMedCentralPubMedGoogle Scholar
  41. 41.
    Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49:534–553CrossRefPubMedGoogle Scholar
  42. 42.
    Peng SM, Zhou Y, Huang N (2013) Improving the accuracy of pose prediction in molecular docking via structural filtering and conformational clustering. Chin Chem Lett 24:1001–1004CrossRefGoogle Scholar
  43. 43.
    Zhou Z, Madura JD (2004) CoMFA 3D-QSAR analysis of HIV-1 RT non nucleoside inhibitors, TIBO derivatives based on docking conformation and alignment. J Chem Inf Comput Sci 44:2167–2178CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Dr. Niu Huang’s LabNational Institute of Biological Sciences, BeijingBeijingPeople’s Republic of China

Personalised recommendations