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Journal of Computer-Aided Molecular Design

, Volume 32, Issue 10, pp 937–963 | Cite as

Overview of the SAMPL6 host–guest binding affinity prediction challenge

  • Andrea Rizzi
  • Steven Murkli
  • John N. McNeill
  • Wei Yao
  • Matthew Sullivan
  • Michael K. Gilson
  • Michael W. Chiu
  • Lyle Isaacs
  • Bruce C. Gibb
  • David L. Mobley
  • John D. Chodera
Article

Abstract

Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host–guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host–guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host–guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host–guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host–guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.

Keywords

SAMPL6 Host–guest Blind challenge Binding affinity Free energy Cucurbit[8]uril Octa-acid 

Abbreviations

AM1-BCC

Austin model 1 bond charge correction [58, 59]

AMOEBA

Atomic multipole optimized energetics for biomolecular simulation [103]

B3LYP

Becke 3-parameter Lee-Yang-Parr exchange-correlation functional [13]

B3PW91

Becke 3-parameter Perdew-Wang 91 exchange-correlation functional [13]

CGenFF

CHARMM generalized force field [129]

COSMO-RS

Conductor-like screening model for real solvents [65]

DDM

Double decoupling method [41]

DFT-D3

Density functional theory with the D3 dispersion corrections [44]

FM

Force matching [30]

FSDAM

Fast switching double annihilation method [97, 104]

GAFF

Generalized AMBER force field [130]

HREX

Hamiltonian replica exchange [122]

KECSA

Knowledge-based and empirical combined scoring algorithm [138]

KMTISM

KECSA-movable type implicit solvation model [140]

MD

Molecular dynamics

MMPBSA

Molecular mechanics Poisson Boltzmann/solvent accessible surface area [119]

MovTyp

Movable type method [139]

OPLS3

optimized potential for liquid simulations [48]

PBSA

Poisson–Boltzmann surface area [114]

PM6-DH+

PM6 semiempirical method with dispersion and hydrogen bonding corrections [68, 108]

RESP

Restrained electrostatic potential [12]

REST

Replica exchange with solute torsional tempering [73, 76]

RFEC

Relative free energy calculation

QM/MM

Mixed quantum mechanics and molecular mechanics

SOMD

Double annihilation or decoupling method performed with Sire/OpenMM6.3 software [28, 133]

SQM

Semi-empirical quantum mechanics

TIP3P

Transferable interaction potential three-point [61]

TPSS

Tao, Perdew, Staroverov, and Scuseria exchange functional [125]

US

Umbrella sampling [128]

VSGB2.1

VSGB2.0 solvation model refit to OPLS2.1/3/3e [72]

Notes

Acknowledgements

AR and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges support from NIH Grant No. P30CA008748. JDC, AR, and DLM gratefully acknowledge support from NIH Grant No. R01GM124270 supporting SAMPL blind challenges. AR acknowledges partial support from the Tri-Institutional Program in Computational Biology and Medicine. LI thanks the National Science Foundation for supporting (Grant No. CHE-1404911) the participation in SAMPL6. DLM appreciates financial support from the National Institutes of Health (Grant No. 1R01GM108889-01), the National Science Foundation (Grant No. CHE 1352608). MKG acknowledges funding from the National Institute of General Medical Sciences (2R01GM061300 and 1U01GM111528). AR and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. We thank four anonymous reviewers, whose comments helped us improve the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author contributions

Conceptualization, AR, JDC, DLM; Methodology, AR, JDC, DLM; Software, AR; Formal Analysis, AR, JDC; Investigation, AR, QY, SM, MS, JNM; Resources, JDC, BCG, LI, MWC, MKG, DLM; Data Curation, AR, MWC; Writing-Original Draft, AR, JDC; Writing - Review and Editing, AR, JDC, DLM, MKG, LI, BCG, SM; Visualization, AR, SM; Supervision, JDC, DLM; Project Administration, AR, JDC, DLM; Funding Acquisition, JDC, DLM, MKG, BCG, LI.

Compliance with ethical standards

Conflict of interest

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, the Molecular Sciences Software Institute, the Starr Cancer Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at http://choderalab.org/funding. MKG has an equity interest in and is a cofounder and scientific advisor of VeraChem LLC.

Supplementary material

10822_2018_170_MOESM1_ESM.pdf (836 kb)
Supplementary material 1 (PDF 837 KB)

References

  1. 1.
    Abel R, Bhat S (2017) Free energy calculation guided virtual screening of synthetically feasible ligand R-group and scaffold modifications: an emerging paradigm for lead optimization. Annu Rep Med Chem 50:237–262Google Scholar
  2. 2.
    Abel R, Mondal S, Masse C, Greenwood J, Harriman G, Ashwell MA, Bhat S, Wester R, Frye L, Kapeller R, Friesner RA (2017a) Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 43:38–44PubMedGoogle Scholar
  3. 3.
    Abel R, Wang L, Harder ED, Berne BJ, Friesner RA (2017b) Advancing drug discovery through enhanced free energy calculations. Acc Chem Res 50(7):1625–1632PubMedGoogle Scholar
  4. 4.
    Abel R, Wang L, Mobley DL, Friesner RA (2017c) A critical review of validation, blind testing, and real-world use of alchemical protein–ligand binding free energy calculations. Curr Top Med Chem 17:2577–2585PubMedGoogle Scholar
  5. 5.
    Aguilar B, Anandakrishnan R, Ruscio JZ, Onufriev AV (2010) Statistics and physical origins of pK and ionization state changes upon protein–ligand binding. Biophys J 98(5):872–880PubMedPubMedCentralGoogle Scholar
  6. 6.
    Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC (2017) Predictions of ligand selectivity from absolute binding free energy calculations. J Am Chem Soc 139(2):946–957PubMedGoogle Scholar
  7. 7.
    Baker BM, Murphy KP (1996) Evaluation of linked protonation effects in protein binding reactions using isothermal titration calorimetry. Biophys J 71(4):2049–2055PubMedPubMedCentralGoogle Scholar
  8. 8.
    Banks JL, Beard HS, Cao Y, Cho AE, Damm W, Farid R, Felts AK, Halgren TA, Mainz DT, Maple JR et al (2005) Integrated modeling program, applied chemical theory (impact). J Comput Chem 26(16):1752–1780PubMedPubMedCentralGoogle Scholar
  9. 9.
    Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL (2016) Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30(11):1–18Google Scholar
  10. 10.
    Bansal N, Zheng Z, Cerutti DS, Merz KM (2017) On the fly estimation of host–guest binding free energies using the movable type method: participation in the sampl5 blind challenge. J Comput-Aided Mol Des 31(1):47–60PubMedGoogle Scholar
  11. 11.
    Bansal N, Zheng Z, Song LF, Pei J, Merz KM Jr (2018) The role of the active site flap in streptavidin/biotin complex formation. J Am Chem Soc 140(16):5434–5446PubMedGoogle Scholar
  12. 12.
    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):10269–10280Google Scholar
  13. 13.
    Becke AD (1993) Density-functional thermochemistry. iii. the role of exact exchange. J Chem Phys 98(7):5648–5652Google Scholar
  14. 14.
    Bell DR, Qi R, Jing Z, Xiang JY, Mejias C, Schnieders MJ, Ponder JW, Ren P (2016) Calculating binding free energies of host–guest systems using the amoeba polarizable force field. Phys Chem Chem Phys 18(44):30261–30269PubMedPubMedCentralGoogle Scholar
  15. 15.
    Bennett CH (1976) Efficient estimation of free energy differences from monte carlo data. J Comput Phys 22(2):245–268Google Scholar
  16. 16.
    Best RB, Vendruscolo M (2004) Determination of protein structures consistent with nmr order parameters. J Am Chem Soc 126(26):8090–8091PubMedGoogle Scholar
  17. 17.
    Bhakat S, Söderhjelm P (2017) Resolving the problem of trapped water in binding cavities: prediction of host–guest binding free energies in the SAMPL5 challenge by funnel metadynamics. J Comput Aided Mol Des 31(1):119–132PubMedGoogle Scholar
  18. 18.
    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):9535–9551Google Scholar
  19. 19.
    Bosisio S, Mey ASJS, Michel J (2017) Blinded predictions of host–guest standard free energies of binding in the SAMPL5 challenge. J Comput Aided Mol Des 31(1):61–70PubMedGoogle Scholar
  20. 20.
    Boyce SE, Tellinghuisen J, Chodera JD (2015) Avoiding accuracy-limiting pitfalls in the study of protein–ligand interactions with isothermal titration calorimetry. bioRxiv.  https://doi.org/10.1101/023796 CrossRefGoogle Scholar
  21. 21.
    Caldararu O, Olsson MA, Riplinger C, Neese F, Ryde U (2017) Binding free energies in the SAMPL5 octa-acid host–guest challenge calculated with DFT-D3 and CCSD(T). J Comput Aided Mol Des 31(1):87–106Google Scholar
  22. 22.
    Caldararu O, Olsson MA, Ignjatović MM, Wang M, Ryde U (2018) Binding free energies in the SAMPL6 octa-acid host–guest challenge calculated with MM and QM methods. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0158-2 CrossRefPubMedGoogle Scholar
  23. 23.
    Cao L, Isaacs L (2014) Absolute and relative binding affinity of cucurbit[7]uril towards a series of cationic guests. Supramol Chem 26(3–4):251–258Google Scholar
  24. 24.
    Cournia Z, Allen B, Sherman W (2017) Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J Chem Inf Model 57(12):2911–2937PubMedGoogle Scholar
  25. 25.
    Czodrowski P (2012) Who cares for the protons? Bioorg Med Chem 20(18):5453–5460PubMedGoogle Scholar
  26. 26.
    Czodrowski P, Sotriffer CA, Klebe G (2007) Protonation changes upon ligand binding to trypsin and thrombin: structural interpretation based on pka calculations and itc experiments. J Mol Biol 367(5):1347–1356PubMedGoogle Scholar
  27. 27.
    Drug Design Data Resource. Sampl. https://drugdesigndata.org/about/sampl
  28. 28.
    Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang L-P, Simmonett AC, Harrigan MP, Stern CD et al (2017) Openmm 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659PubMedPubMedCentralGoogle Scholar
  29. 29.
    Eken Y, Patel P, Díaz T, Jones MR, Wilson AK (2018) SAMPL6 host–guest challenge: binding free energies via a multistep approach. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0159-1 CrossRefPubMedGoogle Scholar
  30. 30.
    Ercolessi F, Adams JB (1994) Interatomic potentials from first-principles calculations: the force-matching method. Europhys Lett (EPL) 26(8):583Google Scholar
  31. 31.
    Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh ewald method. J Chem Phys 103(19):8577–8593Google Scholar
  32. 32.
    Ewell J, Gibb BC, Rick SW (2008) Water inside a hydrophobic cavitand molecule. J Phys Chem B 112(33):10272–10279PubMedGoogle Scholar
  33. 33.
    Freeman W, Mock W, Shih N (1981) Cucurbituril. J Am Chem Soc 103(24):7367–7368Google Scholar
  34. 34.
    Gallicchio E, Paris K, Levy RM (2009) The agbnp2 implicit solvation model. J Chem Theory Comput 5(9):2544–2564PubMedPubMedCentralGoogle Scholar
  35. 35.
    Gan H, Benjamin CJ, Gibb BC (2011) Nonmonotonic assembly of a deep-cavity cavitand. J Am Chem Soc 133(13):4770–4773PubMedPubMedCentralGoogle Scholar
  36. 36.
    Geballe MT, Guthrie JP (2012) The SAMPL3 blind prediction challenge: transfer energy overview. J Comput Aided Mol Des 26(5):489–496PubMedPubMedCentralGoogle Scholar
  37. 37.
    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):259–279PubMedPubMedCentralGoogle Scholar
  38. 38.
    Gibb CL, Gibb BC (2004) Well-defined, organic nanoenvironments in water: the hydrophobic effect drives a capsular assembly. J Am Chem Soc 126(37):11408–11409PubMedGoogle Scholar
  39. 39.
    Gibb CL, Gibb BC (2011) Anion binding to hydrophobic concavity is central to the salting-in effects of hofmeister chaotropes. J Am Chem Soc 133(19):7344–7347PubMedPubMedCentralGoogle Scholar
  40. 40.
    Gibb CLD, Gibb BC (2013) Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J Comput Aided Mol Des 28(4):319–325PubMedPubMedCentralGoogle Scholar
  41. 41.
    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):1047–1069PubMedPubMedCentralGoogle Scholar
  42. 42.
    Graves AP, Shivakumar DM, Boyce SE, Jacobson MP, Case DA, Shoichet BK (2008) Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J Mol Biol 377(3):914–934PubMedPubMedCentralGoogle Scholar
  43. 43.
    Greenwood JR, Calkins D, Sullivan AP, Shelley JC (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aided Mol Des 24(6–7):591–604PubMedGoogle Scholar
  44. 44.
    Grimme S, Antony J, Ehrlich S, Krieg H (2010) A consistent and accurate ab initio parametrization of density functional dispersion correction (dft-d) for the 94 elements h-pu. J Chem Phys 132(15):154104PubMedPubMedCentralGoogle Scholar
  45. 45.
    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507PubMedPubMedCentralGoogle Scholar
  46. 46.
    Guthrie JP (2014) SAMPL4, a blind challenge for computational solvation free energies: the compounds considered. J Comput Aided Mol Des 28(3):151–168PubMedGoogle Scholar
  47. 47.
    Han K, Hudson PS, Jones MR, Nishikawa N, Tofoleanu F, Brooks BR (2018) Prediction of CB [8] host–guest binding free energies in SAMPL6 using the double-decoupling method. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0144-8 CrossRefPubMedGoogle Scholar
  48. 48.
    Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL et al (2015) Opls3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296PubMedGoogle Scholar
  49. 49.
    Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with omega: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50(4):572–584PubMedPubMedCentralGoogle Scholar
  50. 50.
    Henriksen NM, Fenley AT, Gilson MK (2015) Computational calorimetry: high-precision calculation of host–guest binding thermodynamics. J Chem Theory Comput 11(9):4377–4394PubMedPubMedCentralGoogle Scholar
  51. 51.
    Hillyer MB, Gibb CL, Sokkalingam P, Jordan JH, Ioup SE, Gibb BC (2016) Synthesis of water-soluble deep-cavity cavitands. Org Lett 18(16):4048–4051PubMedGoogle Scholar
  52. 52.
    Horn HW, Swope WC, Pitera JW, Madura JD, Dick TJ, Hura GL, Head-Gordon T (2004) Development of an improved four-site water model for biomolecular simulations: Tip4p-ew. J Chem Phys 120(20):9665–9678PubMedGoogle Scholar
  53. 53.
    Hsiao Y-W, Söderhjelm P (2014) Prediction of sampl4 host–guest binding affinities using funnel metadynamics. J Comput Aided Mol Des 28(4):443–454PubMedGoogle Scholar
  54. 54.
    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.  https://doi.org/10.1007/s10822-018-0165-3 CrossRefPubMedGoogle Scholar
  55. 55.
    Isik M, Rizzi A, Mobley DL, Shirts M (2018) MobleyLab/SAMPL6: Version 1.12: update preliminary SAMPLing analysisGoogle Scholar
  56. 56.
    Jacobson MP, Friesner RA, Xiang Z, Honig B (2002) On the role of the crystal environment in determining protein side-chain conformations. J Mol Biol 320(3):597–608PubMedGoogle Scholar
  57. 57.
    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 Struct Funct Bioinform 55(2):351–367Google Scholar
  58. 58.
    Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. am1-bcc model: I. method. J Comput Chem 21(2):132–146Google Scholar
  59. 59.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. am1-bcc model: Ii. parameterization and validation. J Comput Chem 23(16):1623–1641Google Scholar
  60. 60.
    Jordan IK, Kondrashov FA, Adzhubei IA, Wolf YI, Koonin EV, Kondrashov AS, Sunyaev S (2005) A universal trend of amino acid gain and loss in protein evolution. Nature 433(7026):633–638PubMedGoogle Scholar
  61. 61.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935Google Scholar
  62. 62.
    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(28):6474–6487Google Scholar
  63. 63.
    Kellett K, Duggan BM, Gilson MK (2018) Facile synthesis of a diverse library of mono-3-substituted β-cyclodextrin analogues. ChemRxiv.  https://doi.org/10.26434/chemrxiv.6453302 CrossRefGoogle Scholar
  64. 64.
    Kirkwood JG (1935) Statistical mechanics of fluid mixtures. J Chem Phys 3(5):300–313Google Scholar
  65. 65.
    Klamt A (1995) Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem 99(7):2224–2235Google Scholar
  66. 66.
    Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19(2):120–127PubMedPubMedCentralGoogle Scholar
  67. 67.
    Kohlhoff KJ, Shukla D, Lawrenz M, Bowman GR, Konerding DE, Belov D, Altman RB, Pande VS (2014) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6(1):15–21Google Scholar
  68. 68.
    Korth M (2010) Third-generation hydrogen-bonding corrections for semiempirical qm methods and force fields. J Chem Theory Comput 6(12):3808–3816Google Scholar
  69. 69.
    Kuhn B, Tichý M, Wang L, Robinson S, Martin RE, Kuglstatter A, Benz J, Giroud M, Schirmeister T, Abel R, Diederich F, Hert J (2017) Prospective evaluation of free energy calculations for the prioritization of cathepsin L inhibitors. J Med Chem 60(6):2485–2497PubMedGoogle Scholar
  70. 70.
    Laury ML, Wang Z, Gordon AS, Ponder JW (2018) Absolute binding free energies for the SAMPL6 cucurbit [8] uril host–guest challenge via the AMOEBA polarizable force field. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0147-5 CrossRefPubMedGoogle Scholar
  71. 71.
    Lee J, Miller BT, Brooks BR (2016) Computational scheme for ph-dependent binding free energy calculation with explicit solvent. Protein Sci 25(1):231–243PubMedGoogle Scholar
  72. 72.
    Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA (2011) The vsgb 2.0 model: a next generation energy model for high resolution protein structure modeling. Protein Struct Funct Bioinform 79:2794–2812Google Scholar
  73. 73.
    Liu P, Kim B, Friesner RA, Berne B (2005a) Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc Natl Acad Sci USA 102(39):13749–13754PubMedGoogle Scholar
  74. 74.
    Liu S, Ruspic C, Mukhopadhyay P, Chakrabarti S, Zavalij PY, Isaacs L (2005b) The cucurbit[n]uril family: prime components for self-sorting systems. J Am Chem Soc 127(45):15959–15967Google Scholar
  75. 75.
    Ma D, Zavalij PY, Isaacs L (2010) Acyclic cucurbit[n]uril congeners are high affinity hosts. J Org Chem 75(14):4786–4795PubMedGoogle Scholar
  76. 76.
    Marsili S, Signorini GF, Chelli R, Marchi M, Procacci P (2010) Orac: a molecular dynamics simulation program to explore free energy surfaces in biomolecular systems at the atomistic level. J Comput Chem 31(5):1106–1116PubMedGoogle Scholar
  77. 77.
    McGann M (2011) Fred pose prediction and virtual screening accuracy. J Chem Inf Model 51(3):578–596Google Scholar
  78. 78.
    McGann M (2012) Fred and hybrid docking performance on standardized datasets. J Comput Aided Mol Des 26(8):897–906Google Scholar
  79. 79.
    Mikulskis P, Cioloboc D, Andrejić M, Khare S, Brorsson J, Genheden S, Mata RA, Söderhjelm P, Ryde U (2014) Free-energy perturbation and quantum mechanical study of SAMPL4 octa-acid host–guest binding energies. J Comput Aided Mol Des 28(4):375–400PubMedPubMedCentralGoogle Scholar
  80. 80.
    Mobley DL, Chodera JD, Dill KA (2006) On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J Chem Phys 125(8):084902PubMedPubMedCentralGoogle Scholar
  81. 81.
    Mobley DL, Chodera JD, Isaacs L, Gibb BC (2016a) Advancing predictive modeling through focused development of model systems to drive new modeling innovations. Department of Pharmaceutical Sciences, UCI, IrvineGoogle Scholar
  82. 82.
    Mobley DL, Chodera JD, Isaacs L, Gibb BC (2016b) Advancing predictive modeling through focused development of model systems to drive new modeling innovations. https://escholarship.org/uc/item/7cf8c6cr
  83. 83.
    Mobley DL, Gilson MK (2016) Predicting binding free energies: frontiers and benchmarks. bioRxiv.  https://doi.org/10.1101/074625 CrossRefGoogle Scholar
  84. 84.
    Mobley DL, Gilson MK (2017) Predicting binding free energies: frontiers and benchmarks. Annu Rev Biophys 46:531–558PubMedPubMedCentralGoogle Scholar
  85. 85.
    Mobley DL, Heinzelmann G, Henriksen NM, Gilson MK (2017) Predicting binding free energies: frontiers and benchmarks (a perpetual review). Department of Pharmaceutical Sciences, UCI, IrvineGoogle Scholar
  86. 86.
    Mobley DL, Liu S, Lim NM, Wymer KL, Perryman AL, Forli S, Deng N, Su J, Branson K, Olson AJ (2014a) Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des 28(4):327–345PubMedPubMedCentralGoogle Scholar
  87. 87.
    Mobley DL, Wymer KL, Lim NM, Guthrie JP (2014b) Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des 28(3):135–150PubMedPubMedCentralGoogle Scholar
  88. 88.
    Mock W, Shih N (1983) Host–guest binding capacity of cucurbituril. J Org Chem 48(20):3618–3619Google Scholar
  89. 89.
    Moghaddam S, Inoue Y, Gilson MK (2009) Host–guest complexes with protein–ligand-like affinities: computational analysis and design. J Am Chem Soc 131(11):4012–4021PubMedPubMedCentralGoogle Scholar
  90. 90.
    Moghaddam S, Yang C, Rekharsky M, Ko YH, Kim K, Inoue Y, Gilson MK (2011) New ultrahigh affinity host–guest complexes of Cucurbit[7]uril with Bicyclo[2.2.2]octane and adamantane guests: thermodynamic analysis and evaluation of M2 affinity calculations. J Am Chem Soc 133:3570–3581PubMedPubMedCentralGoogle Scholar
  91. 91.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014a) The SAMPL4 host–guest blind prediction challenge: an overview. J Comput Aided Mol Des 28(4):305–317PubMedPubMedCentralGoogle Scholar
  92. 92.
    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(5):517–525PubMedPubMedCentralGoogle Scholar
  93. 93.
    Muddana HS, Varnado CD, 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):475–487PubMedPubMedCentralGoogle Scholar
  94. 94.
    Muddana HS, Yin J, Sapra NV, Fenley AT, Gilson MK (2014b) Blind prediction of sampl4 cucurbit[7]uril binding affinities with the mining minima method. J Comput Aided Mol Des 28(4):463–474PubMedPubMedCentralGoogle Scholar
  95. 95.
    Murkli S, McNeill JN, Isaacs L (2018) Cucurbit[8]uril guest complexes: blinded dataset for the SAMPL6 challenge. Supramol Chem. AcceptedGoogle Scholar
  96. 96.
    Neeb M, Czodrowski P, Heine A, Barandun LJ, Hohn C, Diederich Fran C, Klebe G (2014) Chasing protons: how isothermal titration calorimetry, mutagenesis, and p\(k_a\) calculations trace the locus of charge in ligand binding to a tRNA-binding enzyme. J Med Chem 57(13):5554–5565PubMedGoogle Scholar
  97. 97.
    Nerattini F, Chelli R, Procacci P (2016) Ii. dissociation free energies in drug–receptor systems via nonequilibrium alchemical simulations: application to the fk506-related immunophilin ligands. Phys Chem Chem Phys 18(22):15005–15018PubMedPubMedCentralGoogle Scholar
  98. 98.
    Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J Med Chem 51(4):769–779PubMedPubMedCentralGoogle Scholar
  99. 99.
    Nishikawa N, Han K, Wu 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.  https://doi.org/10.1007/s10822-018-0166-2 CrossRefPubMedGoogle Scholar
  100. 100.
    Ong W, Kaifer AE (2004) Salt effects on the apparent stability of the cucurbit [7] uril- methyl viologen inclusion complex. J Org Chem 69(4):1383–1385PubMedGoogle Scholar
  101. 101.
    Pal RK, Haider K, Kaur D, Flynn W, Xia J, Levy RM, Taran T, Wickstrom L, Kurtzman T, Gallicchio E (2017) 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(1):29–44PubMedGoogle Scholar
  102. 102.
    Papadourakis M, Bosisio S, Michel J (2018) Blinded predictions of standard binding free energies: lessons learned from the SAMPL6 challenge. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0154-6 CrossRefPubMedGoogle Scholar
  103. 103.
    Ponder JW, Wu C, Ren P, Pande VS, Chodera JD, Schnieders MJ, Haque I, Mobley DL, Lambrecht DS, DiStasio RA Jr (2010) Current status of the amoeba polarizable force field. J Phys Chem B 114(8):2549–2564PubMedPubMedCentralGoogle Scholar
  104. 104.
    Procacci P (2016) I. Dissociation free energies of drug-receptor systems via non-equilibrium alchemical simulations: a theoretical framework. Phys Chem Chem Phys 18(22):14991–15004Google Scholar
  105. 105.
    Procacci P, Guarrasi M, Guarnieri G (2018) SAMPL6 host–guest blind predictions using a non equilibrium alchemical approach. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0151-9 CrossRefPubMedGoogle Scholar
  106. 106.
    Rekharsky MV, Ko YH, Selvapalam N, Kim K, Inoue Y (2007a) Complexation thermodynamics of cucurbit[6]uril with aliphatic alcohols, amines, and diamines. Supramol Chem 19(1–2):39–46Google Scholar
  107. 107.
    Rekharsky MV, Mori T, Yang C, Ko YH, Selvapalam N, Kim H, Sobransingh D, Kaifer AE, Liu S, Isaacs L, Chen W, Moghaddam S, Gilson MK, Kim K, Inoue Y (2007b) A synthetic host–guest system achieves avidin-biotin affinity by overcoming enthalpy–entropy compensation. PNAS 104(52):20737–20742PubMedGoogle Scholar
  108. 108.
    R̆ezác̆ J, Fanfrlík J, Salahub D, Hobza P (2009) Semiempirical quantum chemical pm6 method augmented by dispersion and h-bonding correction terms reliably describes various types of noncovalent complexes. J Chem Theory Comput 5(7):1749–1760PubMedGoogle Scholar
  109. 109.
    Rogers KE, Ortiz-Sánchez JM, Baron R, Fajer M, de Oliveira CAF, McCammon JA (2012) On the role of dewetting transitions in host–guest binding free energy calculations. J Chem Theory Comput 9(1):46–53PubMedPubMedCentralGoogle Scholar
  110. 110.
    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):681–691Google Scholar
  111. 111.
    Shirts MR, Bair E, Hooker G, Pande VS (2003) Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods. Phys Rev Lett 91(14):140601PubMedGoogle Scholar
  112. 112.
    Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124105PubMedPubMedCentralGoogle Scholar
  113. 113.
    Shirts MR, Mobley DL, Brown SP (2010) Free energy calculations in structure-based drug design. In: Merz KM Jr, Ringe D, Reynolds CH (eds) Drug design: structure-and ligand-based approaches. Cambridge University Press, Cambridge, pp 61–66Google Scholar
  114. 114.
    Sitkoff D, Sharp KA, Honig B (1994) Accurate calculation of hydration free energies using macroscopic solvent models. J Phys Chem 98(7):1978–1988Google Scholar
  115. 115.
    Skillman AG (2012) SAMPL3: blinded prediction of host–guest binding affinities, hydration free energies, and trypsin inhibitors. J Comput Aided Mol Des 26(5):473–474PubMedGoogle Scholar
  116. 116.
    Skillman AG, Geballe MT, Nicholls A (2010) SAMPL2 challenge: prediction of solvation energies and tautomer ratios. J Comput Aided Mol Des 24(4):257–258PubMedGoogle Scholar
  117. 117.
    Sokkalingam P, Shraberg J, Rick SW, Gibb BC (2015) Binding hydrated anions with hydrophobic pockets. J Am Chem Soc 138(1):48–51PubMedPubMedCentralGoogle Scholar
  118. 118.
    Song LF, Bansal N, Zheng Z, Merz KM (2018) Detailed potential of mean force studies on host–guest systems from the SAMPL6 challenge. J Comput Aided Mol Des.  https://doi.org/10.1007/s10822-018-0153-7 CrossRefPubMedGoogle Scholar
  119. 119.
    Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of dna, rna, and phosphoramidate- dna helices. J Am Chem Soc 120(37):9401–9409Google Scholar
  120. 120.
    Steuber H, Czodrowski P, Sotriffer CA, Klebe G (2007) Tracing changes in protonation: a prerequisite to factorize thermodynamic data of inhibitor binding to aldose reductase. J Mol Biol 373(5):1305–1320PubMedGoogle Scholar
  121. 121.
    Straatsma T, McCammon J (1991) Multiconfiguration thermodynamic integration. J Chem Phys 95(2):1175–1188Google Scholar
  122. 122.
    Sugita Y, Kitao A, Okamoto Y (2000) Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 113(15):6042–6051Google Scholar
  123. 123.
    Sullivan MR, Sokkalingam P, Nguyen T, Donahue JP, Gibb BC (2017) Binding of carboxylate and trimethylammonium salts to octa-acid and TEMOA deep-cavity cavitands. J Comput Aided Mol Des 31(1):1–8Google Scholar
  124. 124.
    Sultan MM, Denny RA, Unwalla R, Lovering F, Pande VS (2017) Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain. Sci Rep 7(1):15604PubMedPubMedCentralGoogle Scholar
  125. 125.
    Tao J, Perdew JP, Staroverov VN, Scuseria GE (2003) Climbing the density functional ladder: nonempirical meta-generalized gradient approximation designed for molecules and solids. Phys Rev Lett 91(14):146401PubMedGoogle Scholar
  126. 126.
    Tironi IG, Sperb R, Smith PE, van Gunsteren WF (1995) A generalized reaction field method for molecular dynamics simulations. J Chem Phys 102(13):5451–5459Google Scholar
  127. 127.
    Tofoleanu F, Lee J, Pickard FC IV, König G, Huang J, Baek M, Seok C, Brooks BR (2017) Absolute binding free energies for octa-acids and guests in sampl5. J Comput Aided Mol Des 31(1):107–118PubMedGoogle Scholar
  128. 128.
    Torrie GM, Valleau JP (1974) Monte carlo free energy estimates using non-boltzmann sampling: application to the sub-critical lennard-jones fluid. Chem Phys Lett 28(4):578–581Google Scholar
  129. 129.
    Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I 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(4):671–690PubMedPubMedCentralGoogle Scholar
  130. 130.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174Google Scholar
  131. 131.
    Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R (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 137(7):2695–2703PubMedGoogle Scholar
  132. 132.
    White AD, Voth GA (2014) Efficient and minimal method to bias molecular simulations with experimental data. J Chem Theory Comput 10(8):3023–3030PubMedGoogle Scholar
  133. 133.
    Woods CJ, Mey AS, Calabro G, Julien M. Sire molecular simulation framework. https://siremol.org
  134. 134.
    Yin J, Fenley AT, Henriksen NM, Gilson MK (2015) Toward improved force-field accuracy through sensitivity analysis of host–guest binding thermodynamics. J Phys Chem B 119(32):10145–10155PubMedPubMedCentralGoogle Scholar
  135. 135.
    Yin J, Henriksen NM, Muddana HS, Gilson MK (2018) Bind3p: optimization of a water model based on host–guest binding data. J Chem Theory Comput 14(7):3621–3636PubMedGoogle Scholar
  136. 136.
    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):1–19Google Scholar
  137. 137.
    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(22):9554–9563PubMedPubMedCentralGoogle Scholar
  138. 138.
    Zheng Z, Merz KM Jr (2013) Development of the knowledge-based and empirical combined scoring algorithm (kecsa) to score protein–ligand interactions. J Chem Inf Model 53(5):1073–1083PubMedPubMedCentralGoogle Scholar
  139. 139.
    Zheng Z, Ucisik MN, Merz KM (2013) The movable type method applied to protein–ligand binding. J Chem Theory Comput 9(12):5526–5538PubMedPubMedCentralGoogle Scholar
  140. 140.
    Zheng Z, Wang T, Li P, Merz KM Jr (2015) Kecsa-movable type implicit solvation model (kmtism). J Chem Theory Comput 11(2):667–682PubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computational and Systems Biology Program, Sloan Kettering InstituteMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Tri-Institutional Training Program in Computational Biology and MedicineNew YorkUSA
  3. 3.Department of Chemistry and BiochemistryUniversity of MarylandCollege ParkUSA
  4. 4.Department of ChemistryTulane UniversityLouisianaUSA
  5. 5.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of CaliforniaSan Diego, La JollaUSA
  6. 6.Qualcomm Institute, University of CaliforniaSan Diego, La JollaUSA
  7. 7.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of CaliforniaIrvineUSA

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