Journal of Computer-Aided Molecular Design

, Volume 31, Issue 1, pp 47–60 | Cite as

On the fly estimation of host–guest binding free energies using the movable type method: participation in the SAMPL5 blind challenge

  • Nupur Bansal
  • Zheng Zheng
  • David S. Cerutti
  • Kenneth M. MerzEmail author


We review our performance in the SAMPL5 challenge for predicting host–guest binding affinities using the movable type (MT) method. The challenge included three hosts, acyclic Cucurbit[2]uril and two octa-acids with and without methylation at the entrance to their binding cavities. Each host was associated with 6–10 guest molecules. The MT method extrapolates local energy landscapes around particular molecular states and estimates the free energy by Monte Carlo integration over these landscapes. Two blind submissions pairing MT with variants of the KECSA potential function yielded mean unsigned errors of 1.26 and 1.53 kcal/mol for the non-methylated octa-acid, 2.83 and 3.06 kcal/mol for the methylated octa-acid, and 2.77 and 3.36 kcal/mol for Cucurbit[2]uril host. While our results are in reasonable agreement with experiment, we focused on particular cases in which our estimates gave incorrect results, particularly with regard to association between the octa-acids and an adamantane derivative. Working on the hypothesis that differential solvation effects play a role in effecting computed binding affinities for the parent octa-acid and the methylated octa-acid and that the ligands bind inside the pockets (rather than on the surface) we devised a new solvent accessible surface area term to better quantify solvation energy contributions in MT based studies. To further explore this issue a, molecular dynamics potential of mean force (PMF) study indicates that, as found by our docking calculations, the stable binding mode for this ligand is inside (rather than surface bound) the octa-acid cavity whether the entrance is methylated or not. The PMF studies also obtained the correct order for the methylation-induced change in binding affinities and associated the difference, to a large extent to differential solvation effects. Overall, the SAMPL5 challenge yielded in improvements our solvation modeling and also demonstrated the need for thorough validation of input data integrity prior to any computational analysis.


SAMPL5 Blind challenge Binding free energy Movable type Octa-acid Cucurbituril 



We would like to acknowledge the SAMPL5 organizers for providing the data and platform for the blind challenge and global communication. NB would like to acknowledge Mr. Dario Gioia for numerous discussions related to docking of host–guest systems.

Supplementary material

10822_2016_9980_MOESM1_ESM.docx (247 kb)
Supplementary material 1 (DOCX 247 kb)


  1. 1.
    Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24(4):259CrossRefGoogle Scholar
  2. 2.
    Guthrie JP (2009) J Phys Chem B 113(14):4501CrossRefGoogle Scholar
  3. 3.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) J Comput Aided Mol Des 28(4):305CrossRefGoogle Scholar
  4. 4.
    Muddana HS, Varnado CD, Bielawski CW, Urbach AR, Isaacs L, Geballe MT, Gilson MK (2012) J Comput Aided Mol Des 26(5):475CrossRefGoogle Scholar
  5. 5.
    Skillman AG (2012) J Comput Aided Mol Des 26(5):473CrossRefGoogle Scholar
  6. 6.
    Benson ML, Faver JC, Ucisik MN, Dashti DS, Zheng Z, Merz KM (2012) J Comput Aided Mol Des 26(5):647CrossRefGoogle Scholar
  7. 7.
    Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK (2016) J Comput Aided Mol Des. doi: 10.1007/s10822-016-9974-4 Google Scholar
  8. 8.
    Chang CE, Gilson MK (2004) J Am Chem Soc 126(40):13156CrossRefGoogle Scholar
  9. 9.
    Chen W, Chang CE, Gilson MK (2004) Biophys J 87(5):3035CrossRefGoogle Scholar
  10. 10.
    Houk KN, Leach AG, Kim SP, Zhang XY (2003) Angew Chem Int Ed 42(40):4872CrossRefGoogle Scholar
  11. 11.
    Liu SM, Ruspic C, Mukhopadhyay P, Chakrabarti S, Zavalij PY, Isaacs L (2005) J Am Chem Soc 127(45):15959CrossRefGoogle Scholar
  12. 12.
    Gilberg L, Zhang B, Zavalij PY, Sindelar V, Isaacs L (2015) Org Biomol Chem 13(13):4041CrossRefGoogle Scholar
  13. 13.
    Zhang B, Isaacs L (2014) J Med Chem 57(22):9554CrossRefGoogle Scholar
  14. 14.
    Hettiarachchi G, Nguyen D, Wu J, Lucas D, Ma D, Isaacs L, Briken V (2010) PLoS One 5(5):e10514. doi: 10.1371/journal.pone.0010514 CrossRefGoogle Scholar
  15. 15.
    Lagona J, Mukhopadhyay P, Chakrabarti S, Isaacs L (2005) Angew Chem Int Ed 44(31):4844CrossRefGoogle Scholar
  16. 16.
    Rogers KE, Ortiz-Sanchez JM, Baron R, Fajer M, de Oliveira CAF, McCammon JA (2013) J Chem Theory Comput 9(1):46CrossRefGoogle Scholar
  17. 17.
    Choudhury R, Gupta S, Da Silva JP, Ramamurthy V (2013) J Org Chem 78(5):1824CrossRefGoogle Scholar
  18. 18.
    Porel M, Jayaraj N, Kaanumalle LS, Maddipatla MVSN, Parthasarathy A, Ramamurthy V (2009) Langmuir 25(6):3473CrossRefGoogle Scholar
  19. 19.
    Gibb CLD, Gibb BC (2014) J Comput Aided Mol Des 28(4):319CrossRefGoogle Scholar
  20. 20.
    Gibb CLD, Gibb BC (2004) J Am Chem Soc 126(37):11408CrossRefGoogle Scholar
  21. 21.
    Liu SM, Whisenhunt-Ioup SE, Gibb CLD, Gibb BC (2011) Supramol Chem 23(6):480CrossRefGoogle Scholar
  22. 22.
    Gan HY, Benjamin CJ, Gibb BC (2011) J Am Chem Soc 133(13):4770CrossRefGoogle Scholar
  23. 23.
    Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) J Comput Aided Mol Des 27(3):221CrossRefGoogle Scholar
  24. 24.
    Olsson MHM, Sondergaard CR, Rostkowski M, Jensen JH (2011) J Chem Theory Comput 7(2):525CrossRefGoogle Scholar
  25. 25.
    Rostkowski M, Olsson MHM, Sondergaard CR, Jensen JH (2011) BMC Struct Biol. doi: 10.1186/1472-6807-11-6
  26. 26.
    Jorgensen WL, Tiradorives J (1988) J Am Chem Soc 110(6):1657CrossRefGoogle Scholar
  27. 27.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) J Phys Chem B 105(28):6474CrossRefGoogle Scholar
  28. 28.
    LigPrep (2015) Version 3.6. Schrödinger, LLC, New YorkGoogle Scholar
  29. 29.
    Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) J Comput Aided Mol Des 21(12):681CrossRefGoogle Scholar
  30. 30.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) J Med Chem 47(7):1739CrossRefGoogle Scholar
  31. 31.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) J Med Chem 49(21):6177CrossRefGoogle Scholar
  32. 32.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) J Med Chem 47(7):1750CrossRefGoogle Scholar
  33. 33.
    Macromodel (2015) Schrödinger, LLC, New YorkGoogle Scholar
  34. 34.
    Mohamadi F, Richards NGJ, Guida WC, Liskamp R, Lipton M, Caufield C, Chang G, Hendrickson T, Still WC (1990) J Comput Chem 11(4):440CrossRefGoogle Scholar
  35. 35.
    Polak E, Ribiere G (1969) Rev Fr Inf Rech Oper 3(16):35Google Scholar
  36. 36.
    Kolossvary I, Guida WC (1996) J Am Chem Soc 118(21):5011CrossRefGoogle Scholar
  37. 37.
    Zheng Z, Merz KM (2013) J Chem Inf Model 53(5):1073CrossRefGoogle Scholar
  38. 38.
    Cleveland WS (1979) J Am Stat Assoc 74(368):829CrossRefGoogle Scholar
  39. 39.
    Cleveland WS (1981) Am Stat 35(1):54CrossRefGoogle Scholar
  40. 40.
    Kumar S, Bouzida D, Swendsen RH, Kollman PA, Rosenberg JM (1992) J Comput Chem 13(8):1011CrossRefGoogle Scholar
  41. 41.
    Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) J Comput Chem 25(9):1157CrossRefGoogle Scholar
  42. 42.
    Berendsen HJC, Grigera JR, Straatsma TP (1987) J Phys Chem 91(24):6269CrossRefGoogle Scholar
  43. 43.
    Zheng Z, Wang T, Li PF, Merz KM (2015) J Chem Theory Comput 11(2):667CrossRefGoogle Scholar
  44. 44.
    Maestro (2015) Schrödinger, LLC, New YorkGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nupur Bansal
    • 1
  • Zheng Zheng
    • 1
  • David S. Cerutti
    • 1
  • Kenneth M. Merz
    • 1
  1. 1.Department of ChemistryMichigan State UniversityEast LansingUSA

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