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Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

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An Erratum to this article was published on 27 July 2017

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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.

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Change history

  • 27 July 2017

    An erratum to this article has been published.

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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.

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Authors and Affiliations

  1. Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA

    Michael R. Shirts

  2. Department of Chemical Engineering, Vanderbilt University, Nashville, TN, USA

    Christoph Klein

  3. Department of Chemistry and Chemical Biology, Rutgers University, New Brunswick, NJ, USA

    Jason M. Swails & David A. Case

  4. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Jian Yin & Michael K. Gilson

  5. Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, CA, USA

    David L. Mobley

  6. Department of Chemical Engineering, University of Virginia, Charlottesville, VA, USA

    Ellen D. Zhong

Authors
  1. Michael R. Shirts
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  2. Christoph Klein
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  3. Jason M. Swails
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  4. Jian Yin
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  5. Michael K. Gilson
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  6. David L. Mobley
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  7. David A. Case
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  8. Ellen D. Zhong
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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.

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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

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

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