A combined treatment of hydration and dynamical effects for the modeling of host–guest binding thermodynamics: the SAMPL5 blinded challenge


As part of the SAMPL5 blinded experiment, we computed the absolute binding free energies of 22 host–guest complexes employing a novel approach based on the BEDAM single-decoupling alchemical free energy protocol with parallel replica exchange conformational sampling and the AGBNP2 implicit solvation model specifically customized to treat the effect of water displacement as modeled by the Hydration Site Analysis method with explicit solvation. Initial predictions were affected by the lack of treatment of ionic charge screening, which is very significant for these highly charged hosts, and resulted in poor relative ranking of negatively versus positively charged guests. Binding free energies obtained with Debye–Hückel treatment of salt effects were in good agreement with experimental measurements. Water displacement effects contributed favorably and very significantly to the observed binding affinities; without it, the modeling predictions would have grossly underestimated binding. The work validates the implicit/explicit solvation approach employed here and it shows that comprehensive physical models can be effective at predicting binding affinities of molecular complexes requiring accurate treatment of conformational dynamics and hydration.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Baron R, McCammon JA (2013) Molecular recognition and ligand association. Ann Rev Phys Chem 64:151–175

    CAS  Article  Google Scholar 

  2. 2.

    de Beer S, Vermeulen NPE, Oostenbrink C (2010) The role of water molecules in computational drug design. Curr Top Med Chem 10(1):55–66

    Article  Google Scholar 

  3. 3.

    Hummer G (2010) Molecular binding: under water’s influence. Nat Chem 2(11):906

    CAS  Article  Google Scholar 

  4. 4.

    Li Z, Lazaridis T (2007) Water at biomolecular binding interfaces. Phys Chem Chem Phys 9:573–581

    CAS  Article  Google Scholar 

  5. 5.

    Mancera RL (2007) Molecular modeling of hydration in drug design. Curr Opin Drug Discov Dev 10(3):275–280

    CAS  Google Scholar 

  6. 6.

    Wong SE, Lightstone FC (2011) Accounting for water molecules in drug design. Expert Opin Drug Dis 6(1):65–74

    CAS  Article  Google Scholar 

  7. 7.

    Ball P (2008) Water as an active constituent in cell biology. Chem Rev 108(1):74–108

    CAS  Article  Google Scholar 

  8. 8.

    Ladbury JE (1996) Just add water! the effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem Biol 3(12):973–980

    CAS  Article  Google Scholar 

  9. 9.

    Levy Y, Onuchic JN (2006) Water mediation in protein folding and molecular recognition. Annu Rev Biophys Biomol Struct 35:389–415

    CAS  Article  Google Scholar 

  10. 10.

    Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding. Proc Natl Acad Sci USA 104:808–813

    CAS  Article  Google Scholar 

  11. 11.

    Bodnarchuk MS, Russell V, Michel J, Essex JW (2014) Strategies to calculate water binding free energies in protein-ligand complexes. J Chem Inf Model 54(6):1623–1633

    CAS  Article  Google Scholar 

  12. 12.

    Huggins DJ (2012) Application of inhomogeneous fluid solvation theory to model the distribution and thermodynamics of water molecules around biomolecules. Phys Chem Chem Phys 14(43):15106–15117

    CAS  Article  Google Scholar 

  13. 13.

    Ross GA, Morris GM, Biggin PC (2012) Rapid and accurate prediction and scoring of water molecules in protein binding sites. PLoS One 7(3):e32036

    CAS  Article  Google Scholar 

  14. 14.

    Sindhikara DJ, Hirata F (2013) Analysis of biomolecular solvation sites by 3D-RISM theory. J Phys Chem B 117(22):6718–6723

    CAS  Article  Google Scholar 

  15. 15.

    Ross GA, Bodnarchuk MS, Essex JW (2015) Water sites, networks, and free energies with grand canonical monte carlo. J Am Chem Soc 137(47):14930–14943

    CAS  Article  Google Scholar 

  16. 16.

    Biedermann F, Nau WM, Schneider H-J (2014) The hydrophobic effect revisited–studies with supramolecular complexes imply high-energy water as a noncovalent driving force. Angew Chem Int Ed 53(42):11158–11171

    CAS  Article  Google Scholar 

  17. 17.

    Biela A, Nasief NN, Betz M, Heine A, Hangauer D, Klebe G (2013) Dissecting the hydrophobic effect on the molecular level: the role of water, enthalpy, and entropy in ligand binding to thermolysin. Angew Chem Int Ed 52(6):1822–1828

    CAS  Article  Google Scholar 

  18. 18.

    Haider K, Wickstrom L, Ramsey S, Gilson MK, Kurtzman T (2016) Enthalpic breakdown of water structure on protein active-site surfaces. J Phys Chem B. doi:10.1021/acs.jpcb.6b01094

    Google Scholar 

  19. 19.

    Setny P, Baron R, McCammon AJ (2010) How can hydrophobic association be enthalpy driven? J Chem Theory Comput 6:2866–2871

    CAS  Article  Google Scholar 

  20. 20.

    Nguyen CN, Cruz A, Gilson MK, Kurtzman T (2014) Thermodynamics of water in an enzyme active site: grid-based hydration analysis of coagulation factor Xa. J Chem Theory Comput 10(7):2769–2780

    CAS  Article  Google Scholar 

  21. 21.

    Lazaridis T (1998) Inhomogeneous fluid approach to solvation thermodyanmics. I. Theory. J Phys Chem B 102:3531–3541

    CAS  Article  Google Scholar 

  22. 22.

    Gallicchio E, Lapelosa M, Levy RM (2010) Binding energy distribution analysis method (BEDAM) for estimation of protein-ligand binding affinities. J Chem Theory Comput 6:2961–2977

    CAS  Article  Google Scholar 

  23. 23.

    Gallicchio E, Deng N, He P, Perryman AL, Santiago DN, Forli S, Olson AJ, Levy RM (2014) Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge. J Comp Aided Mol Des 28:475–490

    CAS  Article  Google Scholar 

  24. 24.

    Wickstrom L, Deng N, He P, Mentes A, Nguyen C, Gilson MK, Kurtzman T, Gallicchio E, Levy RM (2016) Parameterization of an effective potential for protein-ligand binding from host-guest affinity data. J Mol Recognit 29:10–21

    CAS  Article  Google Scholar 

  25. 25.

    Xia J, Flynn WF, Gallicchio E, Zhang BW, He P, Tan Z, Levy RM (2015) Large scale asynchronous and distributed multi-dimensional replica exchange molecular simulations and efficiency analysis. J Comp Chem 36:1772–1785

    CAS  Article  Google Scholar 

  26. 26.

    Gallicchio E, Xia J, Flynn WF, Zhang B, Samlalsingh S, Mentes A, Levy RM (2015) Asynchronous replica exchange software for grid and heterogeneous computing. Comp Phys Commun 196:236–246

    CAS  Article  Google Scholar 

  27. 27.

    Lazaridis T, Karplus M (2000) Effective energy functions for protein structure prediction. Curr Opin Struct Biol 10:139–145

    CAS  Article  Google Scholar 

  28. 28.

    Gallicchio E, Levy RM (2004) AGBNP: an analytic implicit solvent model suitable for molecular dynamics simulations and high-resolution modeling. J Comput Chem 25:479–499

    CAS  Article  Google Scholar 

  29. 29.

    Gallicchio E, Paris K, Levy RM (2009) The AGBNP2 implicit solvation model. J Chem Theory Comput 5:2544–2564

    CAS  Article  Google Scholar 

  30. 30.

    Deng Y, Roux B (2009) Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B 113:2234–2246

    CAS  Article  Google Scholar 

  31. 31.

    Mobley DL (2012) Let’s get honest about sampling. J Comput Aided Mol Des 26:93–95

    CAS  Article  Google Scholar 

  32. 32.

    Wickstrom L, He P, Gallicchio E, Levy RM (2013) Large scale affinity calculations of cyclodextrin host-guest complexes: understanding the role of reorganization in the molecular recognition process. J Chem Theory Comput 9:3136–3150

    CAS  Article  Google Scholar 

  33. 33.

    Gibb CL, Gibb BC (2014) Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J Comp Aided Mol Des 28(4):319–325

    CAS  Article  Google Scholar 

  34. 34.

    Gan H, Benjamin CJ, Gibb BC (2011) Nonmonotonic assembly of a deep-cavity cavitand. J Am Chem Soc 133(13):4770–4773

    CAS  Article  Google Scholar 

  35. 35.

    Gibb BC, Isaacs L et al (2016) Tbd. J Comp Aided Mol Des. doi:10.1007/s10822-016-9925-0

    Google Scholar 

  36. 36.

    Gallicchio E, Chen H, Chen H, Fitzgerald M, Gao Y, He P, Kalyanikar M, Kao C, Lu B, Niu Y, Pethe M, Zhu J, Levy RM (2015) BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge. J Comp Aided Mol Des 29(4):315–325

    CAS  Article  Google Scholar 

  37. 37.

    Luzar A, Chandler D (1993) Structure and hydrogen bond dynamics of water-dimethyl sulfoxide mixtures by computer simulations. J Chem Phys 98(10):8160–8173

    CAS  Article  Google Scholar 

  38. 38.

    Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Developement and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225–11236

    CAS  Article  Google Scholar 

  39. 39.

    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparameterization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487

    CAS  Article  Google Scholar 

  40. 40.

    Still A, Tempczyk WC, Hawley RC, Hendrikson T (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112:6127–6129

    CAS  Article  Google Scholar 

  41. 41.

    Hawkins GD, Cramer CJ, Truhlar DG (1996) Parametrized models of aqueous free energies of solvation based on pairwise descreening of solute atomic charges from a dielectric medium. J Phys Chem 100:19824–19839

    CAS  Article  Google Scholar 

  42. 42.

    Srinivasan J, Trevathan MW, Beroza P, Case DA (1999) Application of a pairwise generalized born model to proteins and nucleic acids: inclusion of salt effects. Theor Chem Acc 101(6):426–434

    CAS  Article  Google Scholar 

  43. 43.

    Tan Z, Gallicchio E, Lapelosa M, Levy RM (2012) Theory of binless multi-state free energy estimation with applications to protein-ligand binding. J Chem Phys 136:144102

    Article  Google Scholar 

  44. 44.

    Gallicchio E, Levy RM (2011) Recent theoretical and computational advances for modeling protein-ligand binding affinities. Adv Prot Chem Struct Biol 85:27–80

    CAS  Article  Google Scholar 

  45. 45.

    Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151

    CAS  Article  Google Scholar 

  46. 46.

    Gallicchio E, Levy RM, Parashar M (2008) Asynchronous replica exchange for molecular simulations. J Comp Chem 29:788–794

    CAS  Article  Google Scholar 

  47. 47.

    Jorgensen WL, Chandrasekhar J, Madura JD (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935

    CAS  Article  Google Scholar 

  48. 48.

    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossváry I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the ACM/IEEE conference on supercomputing (SC06), Tampa, Florida. IEEE

  49. 49.

    Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21(12):681–691

    CAS  Article  Google Scholar 

  50. 50.

    Lapelosa M, Gallicchio E, Levy RM (2012) Conformational transitions and convergence of absolute binding free energy calculations. J Chem Theory Comput 8:47–60

    CAS  Article  Google Scholar 

  51. 51.

    Gallicchio E (2012) Role of ligand reorganization and conformational restraints on the binding free energies of DAPY non-nucleoside inhibitors to HIV reverse transcriptase. Mol Biosci 2:7–22

    Google Scholar 

  52. 52.

    Gilson MK, Given JA, Bush BL, McCammon JA (1997) The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys J 72:1047–1069

    CAS  Article  Google Scholar 

  53. 53.

    Mobley DL, Klimovich PV (2012) Perspective: Alchemical free energy calculations for drug discovery. J Chem Phys 137:230901

    Article  Google Scholar 

Download references


E.G. and R.K.P. acknowledge support from the National Science Foundation (SI2-SSE 1440665). R.M.L. acknowledges support from the National Institutes of Health (GM30580 and P50 GM103368). T.K. acknowledges support from the National Institutes of Health (1R01GM100946 and 5SC3GM095417). L.W. acknowledges support from PSC-CUNY (68457-00 46). REMD simulations were carried out on the Supermic cluster of XSEDE (supported by TG-MCB150001), and BOINC distributed networks at Temple University and Brooklyn College of the City University of New York. The authors acknowledge invaluable technical support from Gene Mayro, Jaykeen Holt, Zachary Hanson-Hart from the IT department at Temple University, and James Roman, and John Stephen at Brooklyn College.

Author information



Corresponding author

Correspondence to Emilio Gallicchio.

Additional information

The original version of this article was revised: Corrections done in the original article has been published in the erratum.

An erratum to this article is available at http://dx.doi.org/10.1007/s10822-016-9987-z.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pal, R.K., Haider, K., Kaur, D. et al. A combined treatment of hydration and dynamical effects for the modeling of host–guest binding thermodynamics: the SAMPL5 blinded challenge. J Comput Aided Mol Des 31, 29–44 (2017). https://doi.org/10.1007/s10822-016-9956-6

Download citation


  • SAMPL5
  • Hydration Site Analysis (HSA)
  • Debye–Hückel
  • Salt effects
  • AGBNP2