Journal of Computer-Aided Molecular Design

, Volume 31, Issue 1, pp 147–161 | Cite as

Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

  • Michael R. ShirtsEmail author
  • Christoph Klein
  • Jason M. Swails
  • Jian Yin
  • Michael K. Gilson
  • David L. Mobley
  • David A. Case
  • Ellen D. Zhong


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.


Molecular dynamics Simulation validation Molecular simulation SAMPL5 



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.


  1. 1.
    Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 28:305–317CrossRefGoogle Scholar
  2. 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):475–87CrossRefGoogle Scholar
  3. 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):259–279CrossRefGoogle Scholar
  4. 4.
    Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507CrossRefGoogle Scholar
  5. 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 28:401–415CrossRefGoogle Scholar
  6. 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. 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, CAGoogle Scholar
  8. 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 4:435–447CrossRefGoogle Scholar
  9. 9.
    Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117(1):1–19CrossRefGoogle Scholar
  10. 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 43–43Google Scholar
  11. 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):4700–4717CrossRefGoogle Scholar
  12. 12.
    Sousa da Silva AW, Vranken WF (2012) ACPYPE—Antechamber python parser interface. BMC Res Notes 5:367CrossRefGoogle Scholar
  13. 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):3767–3772CrossRefGoogle Scholar
  14. 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):1112–1116CrossRefGoogle Scholar
  15. 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):405–413CrossRefGoogle Scholar
  16. 16.
    Allen MP, Tildesley DJ (1989) Computer simulation of liquids. Clarendon Press, New YorkGoogle Scholar
  17. 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):13,052–13,063CrossRefGoogle Scholar
  18. 18.
    Wu X, Brooks BR (2005) Isotropic periodic sum: a method for the calculation of long-range interactions. J Chem Phys 122(4):44,107CrossRefGoogle Scholar
  19. 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):8577–8593CrossRefGoogle Scholar
  20. 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. 21.
    Gibb CLD, Gibb BC (2013) Binding of cyclic carboxylates to octa-acid deep-cavity cavitand. J Comput Aided Mol Des 28(4):319–325CrossRefGoogle Scholar
  22. 22.
    Gan H, Gibb BC (2013) Guest-mediated switching of the assembly state of a water-soluble deep-cavity cavitand. Chem Commun 49(14):1395–1397CrossRefGoogle Scholar
  23. 23.
    Jordan JH, Gibb BC (2014) Molecular containers assembled through the hydrophobic effect. Chem Soc Rev 44(2):547–585CrossRefGoogle Scholar
  24. 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):9554–9563CrossRefGoogle Scholar
  25. 25.
    in’t Veld PJ, Ismail AE, Grest GS (2007) Application of Ewald summations to long-range dispersion forces. J Chem Phys 127(14):144,711Google Scholar
  26. 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):5737–5746Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michael R. Shirts
    • 1
    Email author
  • Christoph Klein
    • 2
  • Jason M. Swails
    • 3
  • Jian Yin
    • 4
  • Michael K. Gilson
    • 4
  • David L. Mobley
    • 5
  • David A. Case
    • 3
  • Ellen D. Zhong
    • 6
  1. 1.Department of Chemical and Biological EngineeringUniversity of Colorado BoulderBoulderUSA
  2. 2.Department of Chemical EngineeringVanderbilt UniversityNashvilleUSA
  3. 3.Department of Chemistry and Chemical BiologyRutgers UniversityNew BrunswickUSA
  4. 4.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California San DiegoLa JollaUSA
  5. 5.Departments of Pharmaceutical Sciences and ChemistryUniversity of California, IrvineIrvineUSA
  6. 6.Department of Chemical EngineeringUniversity of VirginiaCharlottesvilleUSA

Personalised recommendations