Skip to main content

Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

An Erratum to this article was published on 27 July 2017

This article has been updated

Abstract

We describe our efforts to prepare common starting structures and models for the SAMPL5 blind prediction challenge. We generated the starting input files and single configuration potential energies for the host-guest in the SAMPL5 blind prediction challenge for the GROMACS, AMBER, LAMMPS, DESMOND and CHARMM molecular simulation programs. All conversions were fully automated from the originally prepared AMBER input files using a combination of the ParmEd and InterMol conversion programs. We find that the energy calculations for all molecular dynamics engines for this molecular set agree to better than 0.1 % relative absolute energy for all energy components, and in most cases an order of magnitude better, when reasonable choices are made for different cutoff parameters. However, there are some surprising sources of statistically significant differences. Most importantly, different choices of Coulomb’s constant between programs are one of the largest sources of discrepancies in energies. We discuss the measures required to get good agreement in the energies for equivalent starting configurations between the simulation programs, and the energy differences that occur when simulations are run with program-specific default simulation parameter values. Finally, we discuss what was required to automate this conversion and comparison.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Change history

  • 27 July 2017

    An erratum to this article has been published.

References

  1. 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

    CAS  Article  Google Scholar 

  2. 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):475–87

    CAS  Article  Google Scholar 

  3. 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

    CAS  Article  Google Scholar 

  4. Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  6. Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK (2016) Overview of the SAMPL5 host-guest challenge: are we doing better? J Comput Aided Mol Des. doi:10.1007/s10822-016-9974-4

  7. Case D, Babin V, Berryman J, Betz R, Cai Q, Cerutti D, Cheatham T III, Darden T, Duke R, Gohlke H, Goetz A, Gusarov S, Homeyer N, Janowski P, Kaus J, Kolossváry I, Kovalenko A, Lee T, LeGrand S, Luchko T, Luo R, Madej B, Merz K, Paesani F, Roe D, Roitberg A, Sagui C, Salomon-Ferrer R, Seabra G, Simmerling C, Smith W, Swails J, Walker R, Wang J, Wolf R, Wu X, Kollman P (2014) AMBER 14. University of California San Francisco, San Francisco, CA

    Google Scholar 

  8. Hess B, Kutzner C, Dvd Spoel, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447

    CAS  Article  Google Scholar 

  9. Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117(1):1–19

    CAS  Article  Google Scholar 

  10. Bowers KJ, Chow DE, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the ACM/IEEE SC 2006 conference, pp 43–43

  11. Paliwal H, Shirts MR (2013) Using multistate reweighting to rapidly and efficiently explore molecular simulation parameters space for nonbonded interactions. J Chem Theory Comput 9(7):4700–4717

    CAS  Article  Google Scholar 

  12. Sousa da Silva AW, Vranken WF (2012) ACPYPE—Antechamber python parser interface. BMC Res Notes 5:367

    Article  Google Scholar 

  13. Crowley MF, Williamson MJ, Walker RC (2009) CHAMBER: comprehensive support for CHARMM force fields within the AMBER software. Int J Quantum Chem 109(15):3767–3772

    CAS  Article  Google Scholar 

  14. Vermaas JV, Hardy DJ, Stone JE, Tajkhorshid E, Kohlmeyer A (2016) TopoGromacs: automated topology conversion from CHARMM to GROMACS within VMD. J Chem Inf Model 56(6):1112–1116

    CAS  Article  Google Scholar 

  15. Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, Buckner J, Jeong JC, Qi Y, Jo S, Pande VS, Case DA, Brooks CL, MacKerell AD, Klauda JB, Im W (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12(1):405–413

    CAS  Article  Google Scholar 

  16. Allen MP, Tildesley DJ (1989) Computer simulation of liquids. Clarendon Press, New York

    Google Scholar 

  17. Shirts MR, Mobley DL, Chodera JD, Pande VS (2007) Accurate and efficient corrections for missing dispersion interactions in molecular simulations. J Phys Chem B 111(45):13,052–13,063

    CAS  Article  Google Scholar 

  18. Wu X, Brooks BR (2005) Isotropic periodic sum: a method for the calculation of long-range interactions. J Chem Phys 122(4):44,107

    Article  Google Scholar 

  19. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593

    CAS  Article  Google Scholar 

  20. 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. doi:10.1007/s10822-016-9954-8

  21. Gibb CLD, Gibb BC (2013) Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J Comput Aided Mol Des 28(4):319–325

    Article  Google Scholar 

  22. Gan H, Gibb BC (2013) Guest-mediated switching of the assembly state of a water-soluble deep-cavity cavitand. Chem Commun 49(14):1395–1397

    CAS  Article  Google Scholar 

  23. Jordan JH, Gibb BC (2014) Molecular containers assembled through the hydrophobic effect. Chem Soc Rev 44(2):547–585

    Article  Google Scholar 

  24. 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):9554–9563

    CAS  Article  Google Scholar 

  25. in’t Veld PJ, Ismail AE, Grest GS (2007) Application of Ewald summations to long-range dispersion forces. J Chem Phys 127(14):144,711

  26. Wennberg CL, Murtola T, Pall S, Abraham MJ, Hess B, Lindahl E (2015) Direct-Space corrections enable fast and accurate Lorentz–Berthelot combination rule Lennard-Jones lattice summation. J Chem Theory Comput 11(12):5737–5746

Download references

Acknowledgments

The authors would like to thank Frank Pickard (NIH) for sample CHARMM inputs and discussion about evaluation of CHARMM energies, Justin Lemkul (U. Maryland) for advice on CHARMM functional form, and Chris Lee (U. Va., UCSD), Alex Yang (U. Va.), Michael Zhu (U. Va.), Hari Devanathan (U. Va.), and Jacob Rosenthal (U. Va.) for initial work on InterMol. DLM thanks NSF (CHE 1352608) for financial support. This work was also supported in part by grant R01GM061300 and U01GM111528 to MKG, grant R01GM045811 to DAC, and grant 1R01GM108889-01 to DLM. These findings are solely of the authors and do not necessarily represent the views of the NIH. MKG has an equity interest in and is a cofounder and scientific advisor of VeraChem LLC.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael R. Shirts.

Additional information

An erratum to this article is available at https://doi.org/10.1007/s10822-017-0043-4.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shirts, M.R., Klein, C., Swails, J.M. et al. Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset. J Comput Aided Mol Des 31, 147–161 (2017). https://doi.org/10.1007/s10822-016-9977-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-9977-1

Keywords

  • Molecular dynamics
  • Simulation validation
  • Molecular simulation
  • SAMPL5