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CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data
Open accessAbstract
Protein structure and dynamics can be characterized on the atomistic level with both nuclear magnetic resonance (NMR) experiments and molecular dynamics (MD) simulations. Here, we quantify the ability of the recently presented CHARMM36 (C36) force field (FF) to reproduce various NMR observables using MD simulations. The studied NMR properties include backbone scalar couplings across hydrogen bonds, residual dipolar couplings (RDCs) and relaxation order parameter, as well as scalar couplings, RDCs, and order parameters for side-chain amino- and methyl-containing groups. It is shown that the C36 FF leads to better correlation with experimental data compared to the CHARMM22/CMAP FF and suggest using C36 in protein simulations. Although both CHARMM FFs contains the same nonbond parameters, our results show how the changes in the internal parameters associated with the peptide backbone via CMAP and the χ1 and χ2 dihedral parameters leads to improved treatment of the analyzed nonbond interactions. This highlights the importance of proper treatment of the internal covalent components in modeling nonbond interactions with molecular mechanics FFs.
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Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Less is more: Sampling chemical space with active learning
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O., & Roitberg, A. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023802 · Cited by 811
SARS-CoV-2 escape in vitro from a highly neutralizing COVID-19 convalescent plasma
Andreano, E., Piccini, G., Licastro, D., Casalino, L., Johnson, N. V., Paciello, I., Monego, S. D., Pantano, E., Manganaro, N., Manenti, A., et al. (2020) · doi:10.1101/2020.12.28.424451
Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop
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Metadata
- Authors
- Huang, J., & MacKerell, A. D.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2013-07-06
- Volume
- 34
- Issue
- 25
- Pages
- 2135-2145
- ISSN
- 0192-8651 (ISSN-L)
- OpenAlex
- W2109154308
- Cited by
- 4,430
- References
- 4
- FWCI
- 18.15
- Type
- journal article
- Language
- en
- Open access
- green
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