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Prediction of octanol-water partition coefficients for the SAMPL6-\(\log P\) molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields

Abstract

All-atom molecular dynamics simulations with stratified alchemical free energy calculations were used to predict the octanol-water partition coefficient \(\log P_{ow}\) of eleven small molecules as part of the SAMPL6-\(\log P\) blind prediction challenge using four different force field parametrizations: standard OPLS-AA with transferable charges, OPLS-AA with non-transferable CM1A charges, AMBER/GAFF, and CHARMM/CGenFF. Octanol parameters for OPLS-AA, GAFF and CHARMM were validated by comparing the density as a function of temperature, the chemical potential, and the hydration free energy to experimental values. The partition coefficients were calculated from the solvation free energy for the compounds in water and pure (“dry”) octanol or “wet” octanol with 27 mol% water dissolved. Absolute solvation free energies were computed by thermodynamic integration (TI) and the multistate Bennett acceptance ratio with uncorrelated samples from data generated by an established protocol using 5-ns windowed alchemical free energy perturbation (FEP) calculations with the Gromacs molecular dynamics package. Equilibration of sets of FEP simulations was quantified by a new measure of convergence based on the analysis of forward and time-reversed trajectories. The accuracy of the \(\log P_{ow}\) predictions was assessed by descriptive statistical measures such as the root mean square error (RMSE) of the data set compared to the experimental values. Discarding the first 1 ns of each 5-ns window as an equilibration phase had a large effect on the GAFF data, where it improved the RMSE by up to 0.8 log units, while the effect for other data sets was smaller or marginally worsened the agreement. Overall, CGenFF gave the best prediction with RMSE 1.2 log units, although for only eight molecules because the current CGenFF workflow for Gromacs does not generate files for certain halogen-containing compounds. Over all eleven compounds, GAFF gave an RMSE of 1.5. The effect of using a mixed water/octanol solvent slightly decreased the accuracy for CGenFF and GAFF and slightly increased it for OPLS-AA. The GAFF and OPLS-AA results displayed a systematic error where molecules were too hydrophobic whereas CGenFF appeared to be more balanced, at least on this small data set.

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References

  1. Kramer W (2011) Transporters, Trojan horses and therapeutics: suitability of bile acid and peptide transporters for drug delivery. Biol Chem 392(1–2):77–94. https://doi.org/10.1515/BC.2011.017

    CAS  Article  PubMed  Google Scholar 

  2. Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6(11):881–90. https://doi.org/10.1038/nrd2445

    CAS  Article  PubMed  Google Scholar 

  3. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approachesto estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1):3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    CAS  Article  Google Scholar 

  4. 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):927–944. https://doi.org/10.1007/s10822-016-9954-8

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD (2020) Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-019-00271-3

    Article  PubMed  Google Scholar 

  6. Beckstein O, Iorga BI (2012) Prediction of hydration free energies for aliphatic and aromatic chloro derivatives using molecular dynamics simulations with the OPLS-AA force field. J Comput Aided Mol Des 26(5):635–645. https://doi.org/10.1007/s10822-011-9527-9

    CAS  Article  PubMed  Google Scholar 

  7. 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(3):265–276. https://doi.org/10.1007/s10822-014-9727-1

    CAS  Article  PubMed  Google Scholar 

  8. Kenney IM, Beckstein O, Iorga BI (2016) Prediction of cyclohexane-water distribution coefficients for the SAMPL5 data set using molecular dynamics simulations with the OPLS-AA force field. J Comput Aided Mol Des 30(11):1045–1058. https://doi.org/10.1007/s10822-016-9949-5

    CAS  Article  PubMed  Google Scholar 

  9. Selwa E, Kenney IM, Beckstein O, Iorga BI (2018) SAMPL6: calculation of macroscopic pKa values from ab initio quantum mechanical free energies. J Comput Aided Mol Des 32(10):1203–1216. https://doi.org/10.1007/s10822-018-0138-6

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Işık M, Levorse D, Rustenburg AS, Ndukwe IE, Wang H, Wang X, Reibarkh M, Martin GE, Makarov AA, Mobley DL, Rhodes T, Chodera JD (2018) pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments. J Comput Aided Mol Des 32(10):1117–1138. https://doi.org/10.1007/s10822-018-0168-0

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas O, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09 revision D.01. Gaussian Inc., Wallingford

    Google Scholar 

  12. Kaminski G, Duffy E, Matsui T, Jorgensen W (1994) Free energies of hydration and pure liquid properties of hydrocarbons from the OPLS all-atom model. J Phys Chem 98(49):13,077–13,082. https://doi.org/10.1021/j100100a043

    CAS  Article  Google Scholar 

  13. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11,225–11,236. https://doi.org/10.1021/ja9621760

    CAS  Article  Google Scholar 

  14. Damm W, Frontera A, Tirado-Rives J, Jorgensen W (1997) OPLS all-atom force field for carbohydrates. J Comput Chem 18(16):1955–1970. https://doi.org/10.1002/(SICI)1096-987X(199712)18:16<1955::AID-JCC1>3.0.CO;2-L

    CAS  Article  Google Scholar 

  15. Jorgensen WL, McDonald NA (1998) Development of an all-atom force field for heterocycles. Properties of liquid pyridine and diazenes. J Mol Struct Theochem 424(1–2):145–155. https://doi.org/10.1016/S0166-1280(97)00237-6

    CAS  Article  Google Scholar 

  16. McDonald NA, Jorgensen WL (1998) Development of an all-atom force field for heterocycles. Properties of liquid pyrrole, furan, diazoles, and oxazoles. J Phys Chem B 102(41):8049–8059. https://doi.org/10.1021/jp981200o

    CAS  Article  Google Scholar 

  17. Rizzo RC, Jorgensen WL (1999) OPLS all-atom model for amines: Resolution of the amine hydration problem. J Am Chem Soc 121(20):4827–4836. https://doi.org/10.1021/ja984106u

    CAS  Article  Google Scholar 

  18. 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):6474–6487. https://doi.org/10.1021/jp003919d

    CAS  Article  Google Scholar 

  19. Dodda LS, Cabeza de Vaca I, Tirado-Rives J, Jorgensen WL (2017) LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res 45(W1):W331–W336. https://doi.org/10.1093/nar/gkx312

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD Jr (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):671–90. https://doi.org/10.1002/jcc.21367

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 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(12):3155–3168. https://doi.org/10.1021/ci3003649

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52(12):3144–3154. https://doi.org/10.1021/ci300363c

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Soteras Gutiérrez I, Lin FY, Vanommeslaeghe K, Lemkul JA, Armacost KA, Brooks CL, MacKerell AD (2016) Parametrization of halogen bonds in the CHARMM General Force Field: improved treatment of ligand–protein interactions. Bioorg Med Chem 24(20):4812–4825. https://doi.org/10.1016/j.bmc.2016.06.034

    Article  PubMed  PubMed Central  Google Scholar 

  24. 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):1157–74. https://doi.org/10.1002/jcc.20035

    CAS  Article  PubMed  Google Scholar 

  25. Sousa da Silva AW, Vranken WF (2012) ACPYPE: AnteChamber PYthon Parser interfacE. BMC Res Notes 5:367. https://doi.org/10.1186/1756-0500-5-367

    Article  PubMed  PubMed Central  Google Scholar 

  26. 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):926–935. https://doi.org/10.1063/1.445869

    CAS  Article  Google Scholar 

  27. Huang J, MacKerell AD Jr (2013) CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J Comput Chem 34(25):2135–45. https://doi.org/10.1002/jcc.23354

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–713. https://doi.org/10.1021/acs.jctc.5b00255

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Price DJ, Brooks CL 3rd (2004) A modified TIP3P water potential for simulation with Ewald summation. J Chem Phys 121(20):10,096–10,103. https://doi.org/10.1063/1.1808117

    CAS  Article  Google Scholar 

  30. Šegatin N, Klofutar C (2004) Thermodynamics of the solubility of water in 1-hexanol, 1-octanol, 1-decanol, and cyclohexanol. Chem Mon 135(3):241–248. https://doi.org/10.1007/s00706-003-0053-x

    CAS  Article  Google Scholar 

  31. Lang BE (2012) Solubility of water in octan-1-ol from (275 to 369) K. J Chem Eng Data 57(8):2221–2226. https://doi.org/10.1021/je3001427

    CAS  Article  Google Scholar 

  32. Cumming H, Rücker C (2017) Octanol-Water partition coefficient measurement by a simple 1H NMR method. ACS Omega 2(9):6244–6249. https://doi.org/10.1021/acsomega.7b01102

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  34. Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124. https://doi.org/10.1063/1.2978177

    CAS  Article  Google Scholar 

  35. Dotson D, Beckstein O, Wille D, Kenney I, Lee H, Lim V, Barhaghi MS (2019) alchemistry/alchemlyb: 0.3.0. Software. https://doi.org/10.5281/zenodo.3361016

    Article  Google Scholar 

  36. Mobley DL, Dumont E, Chodera JD, Dill KA (2007) Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J Phys Chem B 111(9):2242–2254. https://doi.org/10.1021/jp0667442

    CAS  Article  PubMed  Google Scholar 

  37. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190. https://doi.org/10.1063/1.328693

    CAS  Article  Google Scholar 

  38. Shirts MR, Pitera JW, Swope WC, Pande VS (2003) Extremely precise free energy calculations of amino acid side chain analogs: comparison of common molecular mechanics force fields for proteins. J Chem Phys 119(11):5740–5761. https://doi.org/10.1063/1.1587119

    CAS  Article  Google Scholar 

  39. Essman U, Perela L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8592. https://doi.org/10.1063/1.470117

    Article  Google Scholar 

  40. Hess B (2008) P-LINCS: a parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4(1):116–122. https://doi.org/10.1021/ct700200b

    CAS  Article  PubMed  Google Scholar 

  41. Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447. https://doi.org/10.1021/ct700301q

    CAS  Article  PubMed  Google Scholar 

  42. Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E (2015) Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Markidis S, Laure E (eds) Solving software challenges for Exascale: international conference on exascale applications and software, EASC 2014, Stockholm, Sweden, April 2–3, 2014, revised selected papers. Lecture notes in computer science, vol 8759, pp 3–27. Springer, Basel. https://doi.org/10.1007/978-3-319-15976-8_1

  43. Klimovich PV, Shirts MR, Mobley DL (2015) Guidelines for the analysis of free energy calculations. J Comput Aided Mol Des 29(5):397–411. https://doi.org/10.1007/s10822-015-9840-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Chodera JD (2016) A simple method for automated equilibration detection in molecular simulations. J Chem Theory Comput 12(4):1799–1805. https://doi.org/10.1021/acs.jctc.5b00784

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Jorge M, Garrido N, Queimada A, Economou I, Macedo E (2010) Effect of the integration method on the accuracy and computational efficiency of free energy calculations using thermodynamic integration. J Chem Theory Comput 6(4):1018–1027. https://doi.org/10.1021/ct900661c

    CAS  Article  Google Scholar 

  46. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey C, Polat I, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P, SciPy 1.0 Contributors (2019) Scipy 1.0—fundamental algorithms for scientific computing in Python. arXiv:1907.10121v1

  47. Faber NKM (1999) Estimating the uncertainty in estimates of root mean square error of prediction: application to determining the size of an adequate test set in multivariate calibration. Chemom Intell Lab Syst 49(1):79–89. https://doi.org/10.1016/S0169-7439(99)00027-1

    CAS  Article  Google Scholar 

  48. Zangi R (2018) Refinement of the OPLSAA force-field for liquid alcohols. ACS Omega 3(12):18089–18099. https://doi.org/10.1021/acsomega.8b03132

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Işık M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL (2020) Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P challenge. J Comput Aided Mol Des (in press)

  50. Yang W, Bitetti-Putzer R, Karplus M (2004) Free energy simulations: Use of reverse cumulative averaging to determine the equilibrated region and the time required for convergence. J Chem Phys 120(6):2618–2628. https://doi.org/10.1063/1.1638996

    CAS  Article  PubMed  Google Scholar 

  51. Paranahewage SS, Gierhart CS, Fennell CJ (2016) Predicting water-to-cyclohexane partitioning of the SAMPL5 molecules using dielectric balancing of force fields. J Comput Aided Mol Des 30(11):1059–1065. https://doi.org/10.1007/s10822-016-9950-z

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Brini E, Fennell CJ, Fernandez-Serra M, Hribar-Lee B, Lukšič M, Dill KA (2017) How water’s properties are encoded in its molecular structure and energies. Chem Rev 117(19):12,385–12,414. https://doi.org/10.1021/acs.chemrev.7b00259

    CAS  Article  Google Scholar 

  53. Swope WC, Horn HW, Rice JE (2010) Accounting for polarization cost when using fixed charge force fields. II. Method and application for computing effect of polarization cost on free energy of hydration. J Phys Chem B 114(26):8631–8645. https://doi.org/10.1021/jp911701h

    CAS  Article  PubMed  Google Scholar 

  54. Lundborg M, Lindahl E (2015) Automatic gromacs topology generation and comparisons of force fields for solvation free energy calculations. J Phys Chem B 119(3):810–23. https://doi.org/10.1021/jp505332p

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank David L. Mobley and Teresa Danielle Bergazin for useful discussions and sharing unpublished data. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Awards Number R01GM118772 and R01GM125081. BII was supported in part by Grants ANR-10-LABX-33 (LabEx LERMIT) and ANR-14-JAMR-0002-03 (JPIAMR) from the French National Research Agency (ANR), and by a Grant DIM MAL-INF from the Région Ile-de-France

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Fan, S., Iorga, B.I. & Beckstein, O. Prediction of octanol-water partition coefficients for the SAMPL6-\(\log P\) molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields. J Comput Aided Mol Des 34, 543–560 (2020). https://doi.org/10.1007/s10822-019-00267-z

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Keywords

  • Molecular dynamics
  • Solvation free energy
  • OPLS-AA force field
  • AMBER force field
  • CHARMM force field
  • Ligand parametrization
  • Free energy perturbation
  • Octanol-water partition coefficient