The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models
Abstract
CHARMM27 is a widespread and popular force field for biomolecular simulation, and several recent algorithms such as implicit solvent models have been developed specifically for it. We have here implemented the CHARMM force field and all necessary extended functional forms in the GROMACS molecular simulation package, to make CHARMM-specific features available and to test them in combination with techniques for extended time steps, to make all major force fields available for comparison studies in GROMACS, and to test various solvent model optimizations, in particular the effect of Lennard-Jones interactions on hydrogens. The implementation has full support both for CHARMM-specific features such as multiple potentials over the same dihedral angle and the grid-based energy correction map on the ϕ, ψ protein backbone dihedrals, as well as all GROMACS features such as virtual hydrogen interaction sites that enable 5 fs time steps. The medium-to-long time effects of both the correction maps and virtual sites have been tested by performing a series of 100 ns simulations using different models for water representation, including comparisons between CHARMM and traditional TIP3P. Including the correction maps improves sampling of near native-state conformations in our systems, and to some extent it is even able to refine distorted protein conformations. Finally, we show that this accuracy is largely maintained with a new implicit solvent implementation that works with virtual interaction sites, which enables performance in excess of 250 ns/day for a 900-atom protein on a quad-core desktop computer.
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Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 41,298
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 22,514
Polymorphic transitions in single crystals: A new molecular dynamics method
Parrinello, M., & Rahman, A. (1981) · Journal of Applied Physics · doi:10.1063/1.328693 · Cited by 19,790
The PyMOL Molecular Graphics System
DeLano, W. L. (2002) · Medical Entomology and Zoology · Cited by 18,147
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,089
Scalable molecular dynamics with NAMD
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
Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996) · Journal of the American Chemical Society · doi:10.1021/ja9621760 · Cited by 15,409
CHARMM: A program for macromolecular energy, minimization, and dynamics calculations
Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S., & Karplus, M. (1983) · Journal of Computational Chemistry · doi:10.1002/jcc.540040211 · Cited by 14,944
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp973084f · Cited by 14,311
Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., Yang, R., Cieplak, P., Luo, R., Lee, T., et al. (2003) · Journal of Computational Chemistry · doi:10.1002/jcc.10349 · Cited by 4,441
Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., & Jorgensen, W. L. (2001) · The Journal of Physical Chemistry B · doi:10.1021/jp003919d · Cited by 4,060
Wang, J., Cieplak, P., & Kollman, P. A. (2000) · Journal of Computational Chemistry · doi:10.1002/1096-987x(200009)21:12<1049::aid-jcc3>3.0.co;2-f · Cited by 4,015
P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation
Hess, B. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700200b · Cited by 3,987
Self-citationOostenbrink, C., Villa, A., Mark, A. E., & Van Gunsteren, W. F. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20090 · Cited by 3,719
Mackerell, A. D., Feig, M., & Brooks, C. L. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20065 · Cited by 3,319
Onufriev, A., Bashford, D., & Case, D. A. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20033 · Cited by 2,507
New spherical‐cutoff methods for long‐range forces in macromolecular simulation
Steinbach, P. J., & Brooks, B. R. (1994) · Journal of Computational Chemistry · doi:10.1002/jcc.540150702 · Cited by 1,147
The radial distribution functions of water and ice from 220 to 673 K and at pressures up to 400 MPa
Soper, A. K. (2000) · Chemical Physics · doi:10.1016/s0301-0104(00)00179-8 · Cited by 1,098
Improving efficiency of large time-scale molecular dynamics simulations of hydrogen-rich systems
Feenstra, K. A., Hess, B., & Berendsen, H. J. C. (1999) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199906)20:8<786::aid-jcc5>3.0.co;2-b · Cited by 859
Self-citationDetermining the shear viscosity of model liquids from molecular dynamics simulations
Hess, B. (2002) · The Journal of Chemical Physics · doi:10.1063/1.1421362 · Cited by 808
Self-citationAbsolute comparison of simulated and experimental protein-folding dynamics
Snow, C. D., Nguyen, H., Pande, V. S., & Gruebele, M. (2002) · Nature · doi:10.1038/nature01160 · Cited by 701
The Third IgG-Binding Domain from Streptococcal Protein G
Derrick, J. P., & Wigley, D. B. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1691 · Cited by 348
Hess, B., & van der Vegt, N. F. A. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp0641029 · Cited by 332
Self-citationProgress and challenges in high‐resolution refinement of protein structure models
Misura, K. M. S., & Baker, D. A. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20376 · Cited by 153
Solvent dramatically affects protein structure refinement
Chopra, G., Summa, C. M., & Levitt, M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810818105 · Cited by 98
Refining homology models by combining replica‐exchange molecular dynamics and statistical potentials
Zhu, J., Fan, H., Périole, X., Honig, B. H., & Mark, A. E. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22005 · Cited by 79
Brute-Force Molecular Dynamics Simulations of Villin Headpiece: Comparison with NMR Parameters
van der Spoel, D., & Lindahl, E. R. (2003) · The Journal of Physical Chemistry B · doi:10.1021/jp034108n · Cited by 66
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Zoete, V., Cuendet, M. A., Grosdidier, A., & Michielin, O. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21816 · Cited by 2,370
Self-citationLancaster, G. I., Langley, K. G., Berglund, N. A., Kammoun, H. L., Reibe, S., Estevez, E., Weir, J., Mellett, N. A., Pernes, G., Conway, J. R. W., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.03.014 · Cited by 466
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Panganiban, B., Qiao, B., Jiang, T., DelRe, C., Obadia, M. M., Nguyen, T. D., Smith, A. A. A., Hall, A., Sit, I., Crosby, M. G., et al. (2018) · Science · doi:10.1126/science.aao0335 · Cited by 294
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Di Rita, A., Peschiaroli, A., D′Acunzo, P., Strobbe, D., Hu, Z., Gruber, J., Nygaard, M., Lambrughi, M., Melino, G., Papaleo, E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05722-3 · Cited by 284
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Uusitalo, J. J., Ingólfsson, H. I., Marrink, S. J., & Faustino, I. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.05.043 · Cited by 259
Metadata
- Authors
- Bjelkmar, P., Larsson, P., Cuendet, M. A., Hess, B., & Lindahl, E.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2010-01-25
- Volume
- 6
- Issue
- 2
- Pages
- 459-466
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2052038479
- Cited by
- 1,303
- References
- 28
- FWCI
- 11.81
- Type
- journal article
- Language
- en
- Open access
- closed
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