Journal of Computer-Aided Molecular Design

, Volume 32, Issue 10, pp 1059–1073 | Cite as

Prediction of CB[8] host–guest binding free energies in SAMPL6 using the double-decoupling method

  • Kyungreem HanEmail author
  • Phillip S. Hudson
  • Michael R. Jones
  • Naohiro Nishikawa
  • Florentina Tofoleanu
  • Bernard R. Brooks


This study reports the results of binding free energy calculations for CB[8] host–guest systems in the SAMPL6 blind challenge (receipt ID 3z83m). Force-field parameters were developed specific for each of host and guest molecules to improve configurational sampling. We used quantum mechanical (QM) implicit solvent calculations and QM force matching to determine non-bonded (partial atomic charges) and bonded terms, respectively. Free energy calculations were carried out using the double-decoupling method (DDM) combined with Hamiltonian replica exchange method (HREM) and Bennett acceptance ratio (BAR) method. The root mean square error (RMSE) of the predicted values using DDM with respect to the experimental results was 4.32 kcal/mol. The coefficient of determination (R2) and Kendall rank coefficient (τ) were 0.49 and 0.52, respectively, highest of all submissions. In addition, these were compared to the results obtained by umbrella sampling (US) and weighted histogram analysis method (WHAM). Overall, DDM achieved a higher prediction accuracy than the US method. Results are discussed in terms of parameterization and free energy simulations.


Binding free energy Double-decoupling Hamiltonian replica exchange Bennett acceptance ratio Umbrella sampling Weighted histogram analysis 



The authors would like to thank Gerhard König, Xiongwu Wu, Qiao Zheng and Daniel R. Roe for helpful advice and discussion. We extend our appreciation to Richard M. Venable, Andrew C. Simmonett, John Legato, Andrea Rizzi, Minkyung Baek and Chaok Seok for valuable comment and technical support. Kyungreem Han wishes to express his deepest gratitude to Richard W. Pastor. This work was partially supported by the intramural research program of the National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health and employed the high-performance computational capabilities of the LoBoS and Biowulf Linux clusters at the National Institutes of Health ( and Kyungreem Han’s research was partially supported by a Grant from the KRIBB Research Initiative Program (Korean Biomedical Scientist Fellowship Program), Korea Research Institute of Bioscience and Biotechnology, Republic of Korea.


  1. 1.
    Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303(5665):1813–1818. CrossRefPubMedGoogle Scholar
  2. 2.
    Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66(1):334–395. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Shirts MR (2012) Best practices in free energy calculations for drug design. In: Baron R (ed) Computational drug discovery and design. Springer, New York, pp 425–467. CrossRefGoogle Scholar
  4. 4.
    Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93(7):2395–2417. CrossRefGoogle Scholar
  5. 5.
    Chipot C, Pohorille A (2007) Free energy calculations: theory and applications in chemistry and biology. Springer, BerlinCrossRefGoogle Scholar
  6. 6.
    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) The SAMPL2 blind prediction challenge: introduction and overview. J Comput Aided Mol Des 24(4):259–279. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Muddana HS, Daniel Varnado C, Bielawski CW, Urbach AR, Isaacs L, Geballe MT, Gilson MK (2012) Blind prediction of host–guest binding affinities: a new SAMPL3 challenge. J Comput Aided Mol Des 26(5):475–487. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host–guest blind prediction challenge: an overview. J Comput Aided Mol Des 28(4):305–317. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK (2017) Overview of the SAMPL5 host–guest challenge: are we doing better? J Comput Aided Mol Des 31(1):1–19. CrossRefGoogle Scholar
  11. 11.
    Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL, Chodera JD (2018) Overview of the SAMPL6 host-guest binding affinity prediction challenge. J Comput-Aided Mol Des. CrossRefPubMedGoogle Scholar
  12. 12.
    Wang L, Berne BJ, Friesner RA (2012) On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities. Proc Natl Acad Sci 109(6):1937–1942. CrossRefPubMedGoogle Scholar
  13. 13.
    Mobley DL, Chodera JD, Dill KA (2007) Confine-and-release method: obtaining correct binding free energies in the presence of protein conformational change. J Chem Theory Comput 3(4):1231–1235. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Jiang W, Roux B (2010) Free energy perturbation hamiltonian replica-exchange molecular dynamics (FEP/H-REMD) for absolute ligand binding free energy calculations. J Chem Theory Comput 6(9):2559–2565. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Liu S, Ruspic C, Mukhopadhyay P, Chakrabarti S, Zavalij PY, Isaacs L (2005) The Cucurbit[n]uril family: prime components for self-sorting systems. J Am Chem Soc 127(45):15959–15967. CrossRefPubMedGoogle Scholar
  16. 16.
    Lagona J, Mukhopadhyay P, Chakrabarti S, Isaacs L (2005) The Cucurbit[n]uril Family. Angew Chem Int Ed 44(31):4844–4870. CrossRefGoogle Scholar
  17. 17.
    Steven Murkli JM, Lyle I (2018) Cucurbit[8]uril-guest complexes: blinded dataset for the SAMPL6 challenge. Supramol Chem (submitted)Google Scholar
  18. 18.
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217. CrossRefGoogle Scholar
  19. 19.
    Gilson MK, Given JA, Bush BL, McCammon JA (1997) The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys J 72(3):1047–1069CrossRefGoogle Scholar
  20. 20.
    Boresch S, Tettinger F, Leitgeb M, Karplus M (2003) Absolute binding free energies: a quantitative approach for their calculation. J Phys Chem B 107(35):9535–9551. CrossRefGoogle Scholar
  21. 21.
    Fukunishi H, Watanabe O, Takada S (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J Chem Phys 116(20):9058–9067. CrossRefGoogle Scholar
  22. 22.
    Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22(2):245–268. CrossRefGoogle Scholar
  23. 23.
    Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23(2):187–199. CrossRefGoogle Scholar
  24. 24.
    Kumar S, Rosenberg JM, Bouzida D, Swendsen RH, Kollman PA (1992) THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem 13(8):1011–1021. CrossRefGoogle Scholar
  25. 25.
    Grossfield A (2013) “WHAM: an implementation of the weighted histogram analysis method”,, version 2.0.9
  26. 26.
    D. MA (2004) Empirical force fields for biological macromolecules: overview and issues. J Comput Chem 25(13):1584–1604. CrossRefGoogle Scholar
  27. 27.
    MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiórkiewicz-Kuczera J, Yin D, Karplus M (1998) All-Atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616. CrossRefGoogle Scholar
  28. 28.
    Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, MacKerell AD (2010) CHARMM general force field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31(4):671–690. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    The NIST Reference on Constants, Units, and Uncertainty. US National Institute of Standards and Technology. June 2015. Accessed 25 Sept 2015. 2014 CODATA recommended valuesGoogle Scholar
  30. 30.
    Yao S, Plastaras JP, Marzilli LG (1994) A Molecular mechanics AMBER-type force field for modeling platinum complexes of guanine derivatives. Inorg Chem 33(26):6061–6077. CrossRefGoogle Scholar
  31. 31.
    Allen MP, Tildesley DJ (1987) Computer simulation of liquids. Clarendon Press, OxfordGoogle Scholar
  32. 32.
    Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98(7):5648–5652. CrossRefGoogle Scholar
  33. 33.
    Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37(2):785–789. CrossRefGoogle Scholar
  34. 34.
    Møller C, Plesset MS (1934) Note on an approximation treatment for many-electron systems. Phys Rev 46(7):618–622. CrossRefGoogle Scholar
  35. 35.
    Hariharan PC, Pople JA (1973) The influence of polarization functions on molecular orbital hydrogenation energies. Theor Chimica Acta 28(3):213–222. CrossRefGoogle Scholar
  36. 36.
    Francl MM, Pietro WJ, Hehre WJ, Binkley JS, Gordon MS, DeFrees DJ, Pople JA (1982) Self-consistent molecular orbital methods. XXIII. A polarization-type basis set for second-row elements. J Chem Phys 77(7):3654–3665. CrossRefGoogle Scholar
  37. 37.
    Bayly CI, Cieplak P, Cornell W, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97(40):10269–10280. CrossRefGoogle Scholar
  38. 38.
    Jr. THD (1989) Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J Chem Phys 90(2):1007–1023. CrossRefGoogle Scholar
  39. 39.
    Figgen D, Peterson KA, Dolg M, Stoll H (2009) Energy-consistent pseudopotentials and correlation consistent basis sets for the 5d elements Hf–Pt. J Chem Phys 130(16):164108. CrossRefPubMedGoogle Scholar
  40. 40.
    Marenich AV, Cramer CJ, Truhlar DG (2009) Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J Phys Chem B 113(18):6378–6396. CrossRefGoogle Scholar
  41. 41.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 16 Rev. B.01. Wallingford, CTGoogle Scholar
  42. 42.
    Wang J, Wang W, Kollmann P, Case D (2005) Antechamber, An Accessory Software PackageFor Molecular Mechanical Calculation. J Comput Chem 25:1157–1174. citeulike-article-id:10121022CrossRefGoogle Scholar
  43. 43.
    Rogers DM, Beck TL (2008) ForceSolve. Sourceforge, Chicago ILGoogle Scholar
  44. 44.
    Hudson PS, Boresch S, Rogers D, Woodcock HL (2018) Accelerating QM/MM free energy computations via intramolecular force matching. J Chem Theor Comput (in press)Google Scholar
  45. 45.
    Thiel W, Voityuk AA (1996) Extension of MNDO to d orbitals: parameters and results for the second-row elements and for the zinc group. J Phys Chem 100(2):616–626. CrossRefGoogle Scholar
  46. 46.
    Hay PJ, Wadt WR (1985) Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitals. J Chem Phys 82(1):299–310. CrossRefGoogle Scholar
  47. 47.
    Wadt WR, Hay PJ (1985) Ab initio effective core potentials for molecular calculations. Potentials for main group elements Na to Bi. J Chem Phys 82(1):284–298. CrossRefGoogle Scholar
  48. 48.
    Hay PJ, Wadt WR (1985) Ab initio effective core potentials for molecular calculations. Potentials for the transition metal atoms Sc to Hg. J Chem Phys 82(1):270–283. CrossRefGoogle Scholar
  49. 49.
    Shin W-H, Seok C (2012) GalaxyDock: protein–ligand docking with flexible protein side-chains. J Chem Inf Model 52(12):3225–3232. CrossRefPubMedGoogle Scholar
  50. 50.
    Shin WH, Kim JK, Kim DS, Seok C (2013) GalaxyDock2: protein–ligand docking using beta-complex and global optimization. J Comput Chem 34(30):2647–2656. CrossRefPubMedGoogle Scholar
  51. 51.
    Hoover WG (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31(3):1695–1697. CrossRefGoogle Scholar
  52. 52.
    Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327–341. CrossRefGoogle Scholar
  53. 53.
    Zhang Y, McCammon JA (2003) Studying the affinity and kinetics of molecular association with molecular-dynamics simulation. J Chem Phys 118(4):1821–1827. CrossRefGoogle Scholar
  54. 54.
    Hermans J, Wang L (1997) Inclusion of loss of translational and rotational freedom in theoretical estimates of free energies of binding. Application to a complex of benzene and mutant T4 Lysozyme. J Am Chem Soc 119 (11):2707–2714.; CrossRefGoogle Scholar
  55. 55.
    Northrup SH, Pear MR, Lee CY, McCammon JA, Karplus M (1982) Dynamical theory of activated processes in globular proteins. Proc Natl Acad Sci USA 79(13):4035–4039CrossRefGoogle Scholar
  56. 56.
    Jorgensen WL (1983) Theoretical studies of medium effects on conformational equilibria. J Phys Chem 87(26):5304–5314. CrossRefGoogle Scholar
  57. 57.
    Jorgensen WL (1989) Interactions between amides in solution and the thermodynamics of weak binding. J Am Chem Soc 111(10):3770–3771. CrossRefGoogle Scholar
  58. 58.
    Boczko EM, Brooks CL (1993) Constant-temperature free energy surfaces for physical and chemical processes. J Phys Chem 97(17):4509–4513. CrossRefGoogle Scholar
  59. 59.
    Boczko E, Brooks C (1995) First-principles calculation of the folding free energy of a three-helix bundle protein. Science 269(5222):393–396. CrossRefPubMedGoogle Scholar
  60. 60.
    Sugita Y, Kitao A (1998) Dependence of protein stability on the structure of the denatured state: free energy calculations of I56V mutation in human lysozyme. Biophys J 75(5):2178–2187CrossRefGoogle Scholar
  61. 61.
    Woo H-J, Roux B (2005) Calculation of absolute protein–ligand binding free energy from computer simulations. Proc Natl Acad Sci USA 102(19):6825–6830. CrossRefPubMedGoogle Scholar
  62. 62.
    Gumbart JC, Roux B, Chipot C (2013) Efficient determination of protein–protein standard binding free energies from first principles. J Chem Theory Comput 9(8):3789–3798. CrossRefGoogle Scholar
  63. 63.
    Heinzelmann G, Henriksen NM, Gilson MK (2017) Attach-pull-release calculations of ligand binding and conformational changes on the first BRD4 bromodomain. J Chem Theory Comput 13(7):3260–3275. CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Lee MS, Olson MA (2006) Calculation of Absolute protein-ligand binding affinity using path and endpoint approaches. Biophys J 90(3):864–877. CrossRefPubMedGoogle Scholar
  65. 65.
    Stigler SM (1989) Francis Galton’s account of the invention of correlation. Statist Sci 4(2):73–79. CrossRefGoogle Scholar
  66. 66.
    Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1–2):81–93. CrossRefGoogle Scholar
  67. 67.
    Laury ML, DeYonker NJ, Jiang W, Wilson AK (2011) A pseudopotential-based composite method: the relativistic pseudopotential correlation consistent composite approach for molecules containing 4d transition metals (Y–Cd). J Chem Phys 135(21):214103. CrossRefPubMedGoogle Scholar
  68. 68.
    Riojas AG, Wilson AK (2014) Solv-ccCA: implicit solvation and the correlation consistent composite approach for the determination of pKa. J Chem Theory Comput 10(4):1500–1510. CrossRefGoogle Scholar
  69. 69.
    Hudson PS, Han K, Woodcock HL, Brooks BR (2018) Force Matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale. J Comput-Aided Mol Des (in revision)Google Scholar
  70. 70.
    Damm-Ganamet KL, Smith RD, Dunbar JB, Stuckey JA, Carlson HA (2013) CSAR benchmark exercise 2011–2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model 53(8):1853–1870. CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Xie B, Nguyen TH, Minh DDL (2017) Absolute binding free energies between T4 Lysozyme and 141 small molecules: calculations based on multiple rigid receptor configurations. J Chem Theory Comput 13(6):2930–2944. CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28(6):1145–1152. CrossRefPubMedGoogle Scholar
  73. 73.
    Lee J, Scheraga HA, Rackovsky S (1997) New optimization method for conformational energy calculations on polypeptides: conformational space annealing. J Comput Chem 18 (9):1222–1232.;2-7 CrossRefGoogle Scholar
  74. 74.
    Domański J, Hedger G, Best RB, Stansfeld PJ, Sansom MSP (2017) Convergence and sampling in determining free energy landscapes for membrane protein association. J Phys Chem B 121(15):3364–3375. CrossRefPubMedGoogle Scholar
  75. 75.
    Nishikawa N, Han K, W X, Tofoleanu F, Brooks BR (2018) Comparison of the umbrella sampling and the double decoupling method in binding free energy predictions for SAMPL6 octa-acid host-guest challenges. J Comput-Aided Mol Des (in revision)Google Scholar
  76. 76.
    Bilkova E, Pleskot R, Rissanen S, Sun S, Czogalla A, Cwiklik L, Róg T, Vattulainen I, Cremer PS, Jungwirth P, Coskun Ü (2017) Calcium directly regulates phosphatidylinositol 4,5-bisphosphate headgroup conformation and recognition. J Am Chem Soc 139(11):4019–4024. CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Jurkiewicz P, Cwiklik L, Vojtíšková A, Jungwirth P, Hof M (2012) Structure, dynamics, and hydration of POPC/POPS bilayers suspended in NaCl, KCl, and CsCl solutions. Biochimica et Biophysica Acta (BBA) 1818(3):609–616. CrossRefGoogle Scholar
  78. 78.
    Han K, Venable RM, Bryant AM, Legacy CJ, Shen R, Li H, Roux B, Gericke A, Pastor RW (2018) Graph-theoretic analysis of monomethyl phosphate clustering in ionic solutions. J Phys Chem B 122(4):1484–1494. CrossRefPubMedGoogle Scholar
  79. 79.
    Collins KD (1997) Charge density-dependent strength of hydration and biological structure. Biophys J 72(1):65–76CrossRefGoogle Scholar
  80. 80.
    Hribar B, Southall NT, Vlachy V, Dill KA (2002) How ions affect the structure of water. J Am Chem Soc 124(41):12302–12311. CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Christian M, Chris MRM O (2017) Update on phosphate and charged post-translationally modified amino acid parameters in the GROMOS force field. J Comput Chem 38(10):714–720. CrossRefGoogle Scholar
  82. 82.
    Steinbrecher T, Latzer J, Case DA (2012) Revised AMBER parameters for bioorganic phosphates. J Chem Theor Comput 8(11):4405–4412. CrossRefGoogle Scholar
  83. 83.
    Venable RM, Luo Y, Gawrisch K, Roux B, Pastor RW (2013) Simulations of anionic lipid membranes: development of interaction-specific ion parameters and validation using NMR data. J Phys Chem B 117(35):10183–10192. CrossRefPubMedGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  1. 1.Laboratory of Computational Biology, National Heart, Lung and Blood InstituteNational Institutes of HealthBethesdaUSA

Personalised recommendations