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CHARMM36m: an improved force field for folded and intrinsically disordered proteins
Open accessAbstract
The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m (http://mackerell.umaryland.edu/charmm_ff.shtml), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.
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“All simulations were performed in an isothermal-isobaric ensemble with a pressure of 1.0 atm and a temperature of 30 °C. Long-range electrostatic interactions were modelled with particle mesh Ewald (PME) with force switching and nonbonded cutoffs set to 9 and 10 Å, respectively 76,77,79,139 .”
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Metadata
- Authors
- Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. D.
- Venue
- Nature Methods
- Published
- 2016-11-07
- Volume
- 14
- Issue
- 1
- Pages
- 71-73
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2555870966
- Cited by
- 7,285
- References
- 0
- FWCI
- 78.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.