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Absolute binding free energy calculations of CBClip host–guest systems in the SAMPL5 blind challenge

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Abstract

Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett’s acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.

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References

  1. Liao C, Sitzmann M, Pugliese A, Nicklaus MC (2011) Software and resources for computational medicinal chemistry. Future Med Chem 3:1057–1085

    Article  CAS  Google Scholar 

  2. Homeyer N, Stoll F, Hillisch A, Gohlke H (2014) Binding free energy calculations for lead optimization: assessment of their accuracy in an industrial drug design context. J Chem Theory Comput 10:3331–3344

    Article  CAS  Google Scholar 

  3. Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395

    Article  Google Scholar 

  4. Shirts MR, Mobley DL, Brown SP (2010) Free-energy calculations in structure-based drug design. Drug Des Struct Ligand Based Approaches. doi:10.1017/CBO9780511730412.007

    Google Scholar 

  5. Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93:2395–2417

    Article  CAS  Google Scholar 

  6. Chipot C, Pohorille A (2007) Free energy calculations. Springer, Berlin

    Book  Google Scholar 

  7. Wang L et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc. doi:10.1021/ja512751q

    Google Scholar 

  8. Barrow SJ, Kasera S, Rowland MJ, Del Barrio J, Scherman OA (2015) Cucurbituril-based molecular recognition. Chem Rev 115:12320–12406

    Article  CAS  Google Scholar 

  9. Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host–guest blind prediction challenge: an overview. J Comput Aided Mol Des 28:305–317

    Article  CAS  Google Scholar 

  10. Gallicchio E, Levy RM (2012) Prediction of SAMPL3 host–guest affinities with the binding energy distribution analysis method (BEDAM). J Comput Aided Mol Des 26:505–516

    Article  CAS  Google Scholar 

  11. König G, Brooks BR (2012) Predicting binding affinities of host–guest systems in the SAMPL3 blind challenge: the performance of relative free energy calculations. J Comput Aided Mol Des 26:543–550

    Article  Google Scholar 

  12. Muddana HS et al (2012) Blind prediction of host–guest binding affinities: a new SAMPL3 challenge. J Comput Aided Mol Des 26:475–487

    Article  CAS  Google Scholar 

  13. Muddana HS, Gilson MK (2012) Prediction of SAMPL3 host–guest binding affinities: evaluating the accuracy of generalized force-fields. J Comput Aided Mol Des 26:517–525

    Article  CAS  Google Scholar 

  14. Geballe MT, Guthrie JP (2012) The SAMPL3 blind prediction challenge: transfer energy overview. J Comput Aided Mol Des 26:489–496

    Article  CAS  Google Scholar 

  15. Reinisch J, Klamt A, Diedenhofen M (2012) Prediction of free energies of hydration with COSMO-RS on the SAMPL3 data set. J Comput Aided Mol Des 26:669–673

    Article  CAS  Google Scholar 

  16. Kulp JL III, Blumenthal SN, Wang Q, Bryan RL, Guarnieri F (2012) A fragment-based approach to the SAMPL3 Challenge. J Comput Aided Mol Des 26:583–594

    Article  CAS  Google Scholar 

  17. Kumar A, Zhang KYJ (2012) Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge. J Comput Aided Mol Des 26:603–616

    Article  CAS  Google Scholar 

  18. König G, Pickard FC, Mei Y, Brooks BR (2014) Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4. J Comput Aided Mol Des 28:245–257

    Article  Google Scholar 

  19. Hsiao Y-W, Söderhjelm P (2014) Prediction of SAMPL4 host–guest binding affinities using funnel metadynamics. J Comput Aided Mol Des 28:443–454

    Article  CAS  Google Scholar 

  20. Monroe JI, Shirts MR (2014) Converging free energies of binding in cucurbit [7] uril and octa-acid host–guest systems from SAMPL4 using expanded ensemble simulations. J Comput Aided Mol Des 28:401–415

    Article  CAS  Google Scholar 

  21. Muddana HS, Yin J, Sapra NV, Fenley AT, Gilson MK (2014) Blind prediction of SAMPL4 cucurbit [7] uril binding affinities with the mining minima method. J Comput Aided Mol Des 28:463–474

    Article  CAS  Google Scholar 

  22. Ellingson BA et al (2014) Efficient calculation of SAMPL4 hydration free energies using OMEGA, SZYBKI, QUACPAC, and Zap TK. J Comput Aided Mol Des 28:289–298

    Article  CAS  Google Scholar 

  23. Manzoni F, Söderhjelm P (2014) Prediction of hydration free energies for the SAMPL4 data set with the AMOEBA polarizable force field. J Comput Aided Mol Des 28:235–244

    Article  CAS  Google Scholar 

  24. Fu J, Liu Y, Wu J (2014) Fast prediction of hydration free energies for SAMPL4 blind test from a classical density functional theory. J Comput Aided Mol Des 28:299–304

    Article  CAS  Google Scholar 

  25. Li L, Dill KA, Fennell CJ (2014) Testing the semi-explicit assembly model of aqueous solvation in the SAMPL4 challenge. J Comput Aided Mol Des 28:259–264

    Article  CAS  Google Scholar 

  26. Gallicchio E et al (2015) BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge. J Comput Aided Mol Des 29:315–325

    Article  CAS  Google Scholar 

  27. Beckstein O, Fourrier A, Iorga BI (2014) Prediction of hydration free energies for the SAMPL4 diverse set of compounds using molecular dynamics simulations with the OPLS-AA force field. J Comput Aided Mol Des 28:265–276

    Article  CAS  Google Scholar 

  28. Park H (2014) Extended solvent-contact model approach to SAMPL4 blind prediction challenge for hydration free energies. J Comput Aided Mol Des 28:175–186

    Article  CAS  Google Scholar 

  29. Mikulskis P et al (2014) Free-energy perturbation and quantum mechanical study of SAMPL4 octa-acid host–guest binding energies. J Comput Aided Mol Des 28:375–400

    Article  CAS  Google Scholar 

  30. Sure R, Antony J, Grimme S (2014) Blind prediction of binding affinities for charged supramolecular host–guest systems: achievements and shortcomings of DFT-D3. J Phys Chem B 118:3431–3440

    Article  CAS  Google Scholar 

  31. Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des 28:135–150

    Article  CAS  Google Scholar 

  32. Gallicchio E et al (2014) Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge. J Comput Aided Mol Des 28:475–490

    Article  CAS  Google Scholar 

  33. Mobley DL et al (2014) Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des 28:327–345

    Article  CAS  Google Scholar 

  34. Perryman AL, Santiago DN, Forli S, Santos-Martins D, Olson AJ (2014) Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge. J Comput Aided Mol Des 28:429–441

    Article  CAS  Google Scholar 

  35. Hogues H, Sulea T, Purisima EO (2014) Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge. J Comput Aided Mol Des 28:417–427

    Article  CAS  Google Scholar 

  36. Sandberg L (2014) Predicting hydration free energies with chemical accuracy: the SAMPL4 challenge. J Comput Aided Mol Des 28:211–219

    Article  CAS  Google Scholar 

  37. Ma D, Zavalij PY, Isaacs L (2010) Acyclic cucurbit[n]uril congeners are high affinity hosts. J Org Chem 75:4786–4795

    Article  CAS  Google Scholar 

  38. Biedermann F et al (2010) Benzobis(imidazolium)-cucurbit[8]uril complexes for binding and sensing aromatic compounds in aqueous solution. Chem A Eur J 16:13716–13722

    Article  CAS  Google Scholar 

  39. Naïm M et al (2007) Solvated interaction energy (SIE) for scoring protein–ligand binding affinities. 1. Exploring the parameter space. J Chem Inf Model 47:122–133

    Article  Google Scholar 

  40. Zhang B, Isaacs L (2014) Acyclic cucurbit[n]uril-type molecular containers: influence of aromatic walls on their function as solubilizing excipients for insoluble drugs. J Med Chem 57:9554–9563

    Article  CAS  Google Scholar 

  41. Gilberg L, Zhang B, Zavalij PY, Sindelar V, Isaacs L (2015) Acyclic cucurbit[n]uril-type molecular containers: influence of glycoluril oligomer length on their function as solubilizing agents. Org Biomol Chem 13:4041–4050

    Article  CAS  Google Scholar 

  42. Lee JW, Samal S, Selvapalam N, Kim H-J, Kim K (2003) Cucurbituril homologues and\n derivatives: new opportunities\nin supramolecular chemistry. Acc Chem Res 36:621–630

    Article  CAS  Google Scholar 

  43. Masson E, Ling X, Joseph R, Kyeremeh-Mensah L, Lu X (2012) Cucurbituril chemistry: a tale of supramolecular success. RSC Adv 2(4):1213–1247

    Article  CAS  Google Scholar 

  44. Lee J, Scheraga HA, Rackovsky S (1997) New optimization method for conformational energy calculations on polypeptides: conformational space annealing. J Comput Chem 18:1222–1232

    Article  CAS  Google Scholar 

  45. Shin W-H et al (2011) LigDockCSA: protein–ligand docking using conformational space annealing. J Comput Chem 32:3226–3232

    Article  CAS  Google Scholar 

  46. Lee J et al (2011) De novo protein structure prediction by dynamic fragment assembly and conformational space annealing. Proteins Struct Funct Bioinform 79:2403–2417

    Article  CAS  Google Scholar 

  47. Lee J, Gross SP, Lee J (2012) Modularity optimization by conformational space annealing. Phys Rev E 85:056702

    Article  Google Scholar 

  48. Shin WH, Kim JK, Kim DS, Seok C (2013) GalaxyDock2: protein–ligand docking using beta-complex and global optimization. J Comput Chem 34:2647–2656

    Article  CAS  Google Scholar 

  49. Shin W-H, Lee GR, Seok C (2015) Evaluation of GalaxyDock based on the community structure—activity resource 2013 and 2014 benchmark studies. J Chem Inf Model. doi:10.1021/acs.jcim.5b00309

    Google Scholar 

  50. 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

    Article  CAS  Google Scholar 

  51. Boresch S et al (2003) Absolute binding free energies: a quantitative approach for their calculation. J Phys Chem B 107(35):9535–9551

    Article  CAS  Google Scholar 

  52. 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 116:9058

    Article  CAS  Google Scholar 

  53. Itoh SG, Okumura H (2013) Hamiltonian replica-permutation method and its applications to an alanine dipeptide and amyloid-β(29–42) peptides. J Comput Chem 34:2493–2497

    Article  CAS  Google Scholar 

  54. Itoh SSG, Okumura H, Okamoto Y (2010) Replica-exchange method in van der Waals radius space: overcoming steric restrictions for biomolecules. J Chem Phys 132:134105

    Article  Google Scholar 

  55. Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22:245–268

    Article  Google Scholar 

  56. König G, Hudson PS, Boresch S, Woodcock HL (2014) Multiscale free energy simulations: an efficient method for connecting classical MD simulations to QM or QM/MM free energies using Non-Boltzmann Bennett reweighting schemes. J Chem Theory Comput 10:1406–1419

    Article  Google Scholar 

  57. Straatsma TP, Berendsen HJ, Postma JPM, Berendsen C, Postma PM (1986) Free energy of hydrophobic hydration: a molecular dynamics study of noble gases in water. J Chem Phys 85:6720–6727

    Article  CAS  Google Scholar 

  58. Straatsma TP, Berendsen HJC (1988) Free energy of ionic hydration: analysis of a thermodynamic integration technique to evaluate free energy differences by molecular dynamics simulations. J Chem Phys 89:5876

    Article  CAS  Google Scholar 

  59. Lee J, Miller BT, Brooks BR (2016) Computational scheme for pH-dependent binding free energy calculation with explicit solvent. Protein Sci 25:231–243

    Article  CAS  Google Scholar 

  60. Karpen ME, Tobias DJ, Brooks CL (1993) Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV. Biochemistry 32:412–420

    Article  CAS  Google Scholar 

  61. Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. J Comput Chem 28:1145–1152

    Article  CAS  Google Scholar 

  62. Lee J, Lee I-H, Lee J (2003) Unbiased global optimization of Lennard-Jones clusters for N < or =201 using the conformational space annealing method. Phys Rev Lett 91:080201

    Article  Google Scholar 

  63. Wang Q, Pang YP (2007) Accurate reproduction of 161 small-molecule complex crystal structures using the EUDOC program: expanding the use of EUDOC to supramolecular chemistry. PLoS One 2(6):e531

    Article  Google Scholar 

  64. Brooks BR et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614

    Article  CAS  Google Scholar 

  65. Vanommeslaeghe K et al (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31:671–690

    CAS  Google Scholar 

  66. Yu W, He X, Vanommeslaeghe K, MacKerell AD (2012) Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations. J Comput Chem 33:2451–2468

    Article  CAS  Google Scholar 

  67. Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52:3144–3154

    Article  CAS  Google Scholar 

  68. Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52:3155–3168

    Article  CAS  Google Scholar 

  69. Boresch S, Bruckner S (2011) Avoiding the van der Waals endpoint problem using serial atomic insertion. J Comput Chem 32:2449–2458

    Article  CAS  Google Scholar 

  70. Nose S, Nosé S (1984) A unified formulation of the constant temperature molecular dynamics methods. J Chem Phys 81:511

    Article  CAS  Google Scholar 

  71. Hoover W (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31:1695–1697

    Article  CAS  Google Scholar 

  72. Martyna GJ, Klein ML (1992) Nose–Hoover chains: the canonical ensemble via continuous dynamics. J Chem Phys 97(4):2635

    Article  Google Scholar 

  73. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N log(N) method for Ewald sums in large systems. J Chem Phys 98:10089

    Article  CAS  Google Scholar 

  74. Ryckaert JP, 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 23:327–341

    Article  CAS  Google Scholar 

  75. Lee J, Miller BT, Damjanović A, Brooks BR (2014) Constant pH molecular dynamics in explicit solvent with enveloping distribution sampling and hamiltonian exchange. J Chem Theory Comput 10:2738–2750

    Article  CAS  Google Scholar 

  76. Lee J, Miller BT, Damjanovic A, Brooks BR (2015) Enhancing constant-pH simulation in explicit solvent with a two-dimensional replica exchange method. J Chem Theory Comput 11:2560–2574

    Article  CAS  Google Scholar 

  77. De Ruiter A, Boresch S, Oostenbrink C (2013) Comparison of thermodynamic integration and Bennett’s acceptance ratio for calculating relative protein–Ligand binding free energies. J Comput Chem 34:1024–1034

    Article  Google Scholar 

  78. Bruckner S, Boresch S (2011) Efficiency of Alchemical free energy simulations. II. improvements for thermodynamic integration. J Comput Chem 32:1320–1333

    Article  CAS  Google Scholar 

  79. Brun V (1953) A generalization of the formula of Simpson for non-equidistant ordinates. Nord Mat Tidskr 1:10–15

    Google Scholar 

  80. König G, Bruckner S, Boresch S (2009) Unorthodox uses of Bennett’s acceptance ratio method. J Comput Chem 30:1712–1718

    Article  Google Scholar 

  81. König G, Boresch S (2011) Non-Boltzmann sampling and Bennett’s acceptance ratio method: how to profit from bending the rules. J Comput Chem 32:1082–1090

    Article  Google Scholar 

  82. König G, Miller BT, Boresch S, Wu X, Brooks BR (2012) Enhanced sampling in free energy calculations: combining SGLD with the Bennett’s acceptance ratio and enveloping distribution sampling methods. J Chem Theory Comput 8:3650–3662

    Article  Google Scholar 

  83. Mooney CZ, Duval RD, Duval R (1993) Bootstrapping: a nonparametric approach to statistical inference. Sage, NY

    Book  Google Scholar 

  84. Zheng Z, Ucisik MN, Merz KM (2013) The movable type method applied to protein–ligand binding. J Chem Theory Comput 9:5526–5538

    Article  CAS  Google Scholar 

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Acknowledgments

The authors would like to thank R. Pastor for stimulating discussions. The research was supported by the Intramural Research Program of the NIH, NHLBI. Computational resources and services used in this work were provided by the LoBoS cluster of the National Institutes of Health.

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Authors and Affiliations

  1. Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA

    Juyong Lee, Florentina Tofoleanu, Frank C. Pickard IV, Gerhard König, Jing Huang, Ana Damjanović & Bernard R. Brooks

  2. Department of Pharmaceutical Science, School of Pharmacy, University of Maryland, Baltimore, MD, 20852, USA

    Jing Huang

  3. Department of Chemistry, Seoul National University, Seoul, Republic of Korea

    Minkyung Baek & Chaok Seok

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  1. Juyong Lee
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  2. Florentina Tofoleanu
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Correspondence to Juyong Lee.

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Lee, J., Tofoleanu, F., Pickard, F.C. et al. Absolute binding free energy calculations of CBClip host–guest systems in the SAMPL5 blind challenge. J Comput Aided Mol Des 31, 71–85 (2017). https://doi.org/10.1007/s10822-016-9968-2

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