Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset

• Published in: Journal of computer-aided molecular design Vol. 31; no. 1; pp. 147 - 161
• Main Authors: Shirts, Michael R., Klein, Christoph, Swails, Jason M., Yin, Jian, Gilson, Michael K., Mobley, David L., Case, David A., Zhong, Ellen D.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2017; Springer Nature B.V
• Subjects: Animal Anatomy; Binding Sites; Blinds; CAD; Chemistry; Chemistry and Materials Science; Computer aided design; Computer Applications in Chemistry; Computer simulation; Conversion; Energy; Engines; Equivalence; Histology; Ligands; Mathematical models; Molecular Conformation; Molecular dynamics; Molecular Dynamics Simulation; Molecular Structure; Morphology; Parameters; Physical Chemistry; Protein Binding; Proteins - chemistry; Simulation; Software; Solvents - chemistry; Thermodynamics; Water - chemistry; Molecular simulation; SAMPL5; Molecular dynamics; Simulation validation
• ISSN: 0920-654X; 1573-4951
• DOI: 10.1007/s10822-016-9977-1

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.