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Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

View ORCID ProfileMichael R. Shirts, Christoph Klein, Jason M. Swails, Jian Yin, Michael K. Gilson, David L. Mobley, David A. Case, Ellen D. Zhong
doi: https://doi.org/10.1101/077248
Michael R. Shirts
1Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado, USA
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Christoph Klein
2Department of Chemical Engineering, Vanderbilt University, Nashville, TN, USA
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Jason M. Swails
3Department of Chemistry and Chemical Biology, Rutgers University, Rutgers, NJ, USA
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Jian Yin
4Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
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Michael K. Gilson
5Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
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David L. Mobley
6Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, CA, USA
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David A. Case
7Department of Chemistry and Chemical Biology, Rutgers University, Rutgers, NJ, USA
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Ellen D. Zhong
8Department of Chemical Engineering, University of Virginia, Charlottesville, VA, USA
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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 a 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.

Footnotes

  • * michael.shirts{at}colorado.edu

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 25, 2016.
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Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset
Michael R. Shirts, Christoph Klein, Jason M. Swails, Jian Yin, Michael K. Gilson, David L. Mobley, David A. Case, Ellen D. Zhong
bioRxiv 077248; doi: https://doi.org/10.1101/077248
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Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset
Michael R. Shirts, Christoph Klein, Jason M. Swails, Jian Yin, Michael K. Gilson, David L. Mobley, David A. Case, Ellen D. Zhong
bioRxiv 077248; doi: https://doi.org/10.1101/077248

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