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CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field
Open accessAbstract
Proper treatment of nonbonded interactions is essential for the accuracy of molecular dynamics (MD) simulations, especially in studies of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in different MD simulation programs can result in disagreements with published simulations performed with CHARMM due to differences in the protocols used to treat the long-range and 1-4 nonbonded interactions. In this study, we systematically test the use of the C36 lipid FF in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested to find the optimal simulation protocol to best match bilayer properties of six lipids with varying acyl chain saturation and head groups. MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine (DPPC) bilayer were used to obtain the optimal protocol for each program. MD simulations with all programs were found to reasonably match the DPPC bilayer properties (surface area per lipid, chain order parameters, and area compressibility modulus) obtained using the standard protocol used in CHARMM as well as from experiments. The optimal simulation protocol was then applied to the other five lipid simulations and resulted in excellent agreement between results from most simulation programs as well as with experimental data. AMBER compared least favorably with the expected membrane properties, which appears to be due to its use of the hard-truncation in the LJ potential versus a force-based switching function used to smooth the LJ potential as it approaches the cutoff distance. The optimal simulation protocol for each program has been implemented in CHARMM-GUI. This protocol is expected to be applicable to the remainder of the additive C36 FF including the proteins, nucleic acids, carbohydrates, and small molecules.
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Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535
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Lolicato, F., Steringer, J., Saleppico, R., Beyer, D., Fernandez-Sobaberas, J., Unger, S., Klein, S., Riegerová, P., Wegehingel, S., Müller, H., et al. (2023) · doi:10.7554/elife.88579.2 · Cited by 1
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Matusevicius, M., Corey, R. A., Gragera, M., Yamashita, K., Sprenger, T., Ungogo, M. A., Blaza, J. N., Castro-Hartmann, P., Chirgadze, D. Y., Chaitanya Vedithi, S., et al. (2025) · doi:10.1101/2025.05.15.654314 · Cited by 1
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Matusevicius, M., Corey, R. A., Gragera, M., Yamashita, K., Sprenger, T., Ungogo, M., Blaza, J. N., Castro-Hartmann, P., Chirgadze, D. Y., Chaitanya Vedithi, S., et al. (2026) · eLife · doi:10.7554/elife.107460.3 · Cited by 1
Insights from aquaporin structures into drug-resistant sleeping sickness
Matusevicius, M., Corey, R. A., Gragera, M., Yamashita, K., Sprenger, T., Ungogo, M., Blaza, J. N., Castro-Hartmann, P., Chirgadze, D. Y., Chaitanya Vedithi, S., et al. (2026) · eLife · doi:10.7554/elife.107460 · Cited by 1
Lolicato, F., Steringer, J. P., Saleppico, R., Beyer, D., Fernandez-Sobaberas, J., Unger, S., Klein, S., Riegerová, P., Wegehingel, S., Müller, H.-M., et al. (2024) · eLife · doi:10.7554/elife.88579.3
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“23,24 Then, 20 independent 100 ns production runs were performed using the GPU-accelerated CUDA 25 version of AMBER18, pmemd.”
Dabiri, M., Levarski, Z., Struhárňanská, E., Demko, V., Beneš, V., Turňa, J., & Stuchlík, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-38805-z
Lolicato, F., Steringer, J., Saleppico, R., Beyer, D., Fernandez-Sobaberas, J., Unger, S., Klein, S., Riegerová, P., Wegehingel, S., Müller, H., et al. (2023) · doi:10.1101/2023.04.12.536539
Kádková, A., Murach, J., Østergaard, M., Malsam, A., Malsam, J., Lolicato, F., Nickel, W., Söllner, T. H., & Sørensen, J. B. (2024) · doi:10.7554/elife.88619.2
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2024) · doi:10.7554/elife.91973.3
Metadata
- Authors
- Lee, J., Cheng, X., Swails, J. M., Yeom, M. S., Eastman, P. K., Lemkul, J. A., Wei, S., Buckner, J., Jeong, J. C., Qi, Y., Jo, S., Pande, V. S., Case, D. A., Brooks, C. L., MacKerell, A. D., Klauda, J. B., & Im, W.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-12-03
- Volume
- 12
- Issue
- 1
- Pages
- 405-413
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2206954826
- Cited by
- 4,171
- References
- 2
- FWCI
- 36.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
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