Skip to main content

Extensive all-atom Monte Carlo sampling and QM/MM corrections in the SAMPL4 hydration free energy challenge

Abstract

We present our predictions for the SAMPL4 hydration free energy challenge. Extensive all-atom Monte Carlo simulations were employed to sample the compounds in explicit solvent. While the focus of our study was to demonstrate well-converged and reproducible free energies, we attempted to address the deficiencies in the general Amber force field force field with a simple QM/MM correction. We show that by using multiple independent simulations, including different starting configurations, and enhanced sampling with parallel tempering, we can obtain well converged hydration free energies. Additional analysis using dihedral angle distributions, torsion-root mean square deviation plots and thermodynamic cycles support this assertion. We obtain a mean absolute deviation of 1.7 kcal mol−1 and a Kendall’s τ of 0.65 compared with experiment.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Nicholls A, Mobley DL, Guthrie JP, Chodera JD, Bayly CI, Cooper MD, Pande VS (2008) J Med Chem 51:769–779

    Article  CAS  Google Scholar 

  2. Shirts M, Mobley DL, Chodera JD (2007) Annu Rep Comput Chem 3:41–59

    Article  CAS  Google Scholar 

  3. Sharp KA, Honig B (1990) Ann Rev Biophys Biophys Chem 19:301–332

    Article  CAS  Google Scholar 

  4. Hirata F (2004) Molecular theory of solvation. Springer, Dordrecht

    Book  Google Scholar 

  5. Mobley DL, Dummon E, Chodera JD, Dill KA (2007) J Phys Chem B 111:2242–2254

    Article  CAS  Google Scholar 

  6. Rocklin GJ, Mobley DL, Dill KA (2013) J Chem Theory Comput 9:3072–3083

    Article  CAS  Google Scholar 

  7. Guthrie JP (2014) SAMPL4, a blind challenge for computational solvation free energies: the compounds considered. ibid

  8. Mobley DL, Wymer K, Lim NM (2014) Blind prediction of solvation free energies from the SAMPL4-challenge. ibid

  9. Guthrie JP (2009) J Phys Chem B 113:4501–4507

    Article  CAS  Google Scholar 

  10. Geballe MT, Skillman AG, Nichools A, Guthrie JP, Taylor PJ (2010) J Comput Aided Mol Des 24:259–279

    Article  CAS  Google Scholar 

  11. Geballe MT, Guthrie JP (2012) J Comput Aided Mol Des 26:489–496

    Article  CAS  Google Scholar 

  12. Marenich AV, Cramer CJ, Truhlar DG (2009) J Phys Chem B 113:6378–6396

    Article  CAS  Google Scholar 

  13. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) J Comput Chem 25:1605–1612

    Article  CAS  Google Scholar 

  14. Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) J Am Chem Soc 107:3902–3909

    Article  CAS  Google Scholar 

  15. Gaussian 09 Revision A1 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 Jr JA, 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 A D, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ Gaussian Inc Wallingford CT 2009

  16. Wang JM, Wolf RM, Caldwell KW, Kollman PA, Case DA (2004) J Comput Chem 25:1157–1174

    Article  CAS  Google Scholar 

  17. Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23:1623–1641

    Article  CAS  Google Scholar 

  18. Case DA, Cheatham T, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods R (2005) J Comput Chem 26:1668–1688

    Article  CAS  Google Scholar 

  19. Molecular Operating Environment (MOE) (2012) 10; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7

  20. Jorgensen WL, Chandrasekhar J, Madura JD, Impley RW, Klein ML (1983) J Chem Phys 79:926–935

    Article  CAS  Google Scholar 

  21. Kirkwood JG (1935) J Chem Phys 3:300–313

    Article  CAS  Google Scholar 

  22. Woods CJ, King MA, Essex JW (2003) J Phys Chem B 107:13703–13710

    Article  CAS  Google Scholar 

  23. Shirts MR, Pande VS (2005) J Chem Phys 122:134508

    Article  CAS  Google Scholar 

  24. Zwanzig RW (1954) J Chem Phys 22:1420–1427

    Article  CAS  Google Scholar 

  25. Shirts MR, Pitera JW, Swope WC, Pande VS (2003) J Chem Phys 119:5740

    Article  CAS  Google Scholar 

  26. Woods CJ, King MA, Essex JW (2003) J Phys Chem B 107:13711–13718

    Article  CAS  Google Scholar 

  27. Swendsen RH, Wang JS (1986) Phys Rev Lett 57:2607–2609

    Article  Google Scholar 

  28. Michel J, Verdonk ML, Essex JW (2007) J Chem Theory Comput 3:1645–1655

    Article  CAS  Google Scholar 

  29. Beutler TC, Mark AE, van Schaik RC, Gerber PR, van Gunsteren WF (1994) Chem Phys Lett 222:529–539

    Article  CAS  Google Scholar 

  30. Zacharias M, Straatsma TP, McCammon JA (1994) J Chem Phys 100:9025–9031

    Article  CAS  Google Scholar 

  31. Genheden S, Bodnarchuk M, Michel J, Woods CJ ProtoMS 2.3. http://protoms.org/

  32. Beierlein FR, Michel J, Essex JW (2011) J Phys Chem B 115:4911–4926

    Article  CAS  Google Scholar 

  33. Becke AD (1993) J Chem Phys 98:5648

    Article  CAS  Google Scholar 

  34. Lee C, Yang W, Parr RG (1988) Phys Rev B 37:785–789

    Article  CAS  Google Scholar 

  35. Kendall M (1938) Biomet 30:81–89

    Article  Google Scholar 

  36. Efron B (1979) Anal Stat 7:1–26

    Article  Google Scholar 

  37. Genheden S, Ryde U (2010) J Comput Chem 31:837–846

    CAS  Google Scholar 

  38. Cohen J (2009) Statistical power analysis for the behavioral sciences, 2nd edn. NYU Press, USA

    Google Scholar 

  39. Muddana HS, Sapra NV, Fenley AT, Gilson MK (2014) The SAMPL4 hydration challenge: evaluation of partial charge sets with explicit-water molecular dynamics simulations. ibid

  40. Bergdorf M, Peter C, Hünenberger PH (2003) J Chem Phys 119:9129

    Article  CAS  Google Scholar 

  41. Brunsteiner M, Boresch S (2000) J Chem Phys 112:6953

    Article  CAS  Google Scholar 

  42. Brown SP, Muchmore SW, Hajduk PJ (2009) Drug Discov Today 14:420–427

    Article  Google Scholar 

Download references

Acknowledgments

For financial support we acknowledge AstraZeneca Pharmaceuticals and the Wenner-Gren foundations (SG) and Astex Pharmaceuticals (AICM). We acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samuel Genheden.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (BZ2 37 kb)

Supplementary material 2 (BZ2 17 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Genheden, S., Cabedo Martinez, A.I., Criddle, M.P. et al. Extensive all-atom Monte Carlo sampling and QM/MM corrections in the SAMPL4 hydration free energy challenge. J Comput Aided Mol Des 28, 187–200 (2014). https://doi.org/10.1007/s10822-014-9717-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-014-9717-3

Keywords

  • SAMPL4
  • Hydration free energy
  • Monte Carlo simulation
  • QM/MM corrections
  • SMD