The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Refinement of the Cornell et al. Nucleic Acids Force Field Based on Reference Quantum Chemical Calculations of Glycosidic Torsion Profiles
Open accessAbstract
We report a reparameterization of the glycosidic torsion χ of the Cornell et al. AMBER force field for RNA, χ(OL). The parameters remove destabilization of the anti region found in the ff99 force field and thus prevent formation of spurious ladder-like structural distortions in RNA simulations. They also improve the description of the syn region and the syn-anti balance as well as enhance MD simulations of various RNA structures. Although χ(OL) can be combined with both ff99 and ff99bsc0, we recommend the latter. We do not recommend using χ(OL) for B-DNA because it does not improve upon ff99bsc0 for canonical structures. However, it might be useful in simulations of DNA molecules containing syn nucleotides. Our parametrization is based on high-level QM calculations and differs from conventional parametrization approaches in that it incorporates some previously neglected solvation-related effects (which appear to be essential for obtaining correct anti/high-anti balance). Our χ(OL) force field is compared with several previous glycosidic torsion parametrizations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Self-consistent molecular orbital methods. XX. A basis set for correlated wave functions
Krishnan, R., Binkley, J. S., Seeger, R., & Pople, J. A. (1980) · The Journal of Chemical Physics · doi:10.1063/1.438955 · Cited by 17,772
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
Self-citation
Cited by
ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB
Maier, J. A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K. E., & Simmerling, C. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00255 · Cited by 10,830
An overview of the Amber biomolecular simulation package
Salomon‐Ferrer, R., Case, D. A., & Walker, R. C. (2013) · WIREs Computational Molecular Science · doi:10.1002/wcms.1121 · Cited by 2,623
Biomolecular Simulation: A Computational Microscope for Molecular Biology
Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. E. (2012) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-042910-155245 · Cited by 1,198
RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview
Šponer, J., Bussi, G., Krepl, M., Banáš, P., Bottaro, S., Cunha, R. A., Gil-Ley, A., Pinamonti, G., Poblete, S., Jurečka, P., et al. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00427 · Cited by 599
Self-citationHart, K., Foloppe, N., Baker, C. M., Denning, E. J., Nilsson, L., & MacKerell, A. D. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct200723y · Cited by 578
Assessing the Current State of Amber Force Field Modifications for DNA
Galindo-Murillo, R., Robertson, J. C., Zgarbová, M., Šponer, J., Otyepka, M., Jurečka, P., & Cheatham, T. E. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00186 · Cited by 527
Self-citationCHARMM-GUI supports the Amber force fields
Lee, J., Hitzenberger, M., Rieger, M., Kern, N. R., Zacharias, M., & Im, W. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0012280 · Cited by 428
Chen, A. A., & García, A. E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1309392110 · Cited by 289
Martini coarse-grained force field: Extension to RNA
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
Swails, J. M., York, D. M., & Roitberg, A. E. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct401042b · Cited by 268
Structural basis for Cas9 off-target activity
Pacesa, M., Lin, C.-H., Cléry, A., Saha, A., Arantes, P. R., Bargsten, K., Irby, M. J., Allain, F. H.-T., Palermo, G., Cameron, P., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.026 · Cited by 198
Palermo, G., Chen, J. S., Ricci, C. G., Rivalta, I., Jinek, M., Batista, V. S., Doudna, J. A., & McCammon, J. A. (2018) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583518000070 · Cited by 123
Structural basis for transcriptional start site control of HIV-1 RNA fate
Brown, J. D., Kharytonchyk, S., Chaudry, I., Iyer, A. S., Carter, H., Becker, G., Desai, Y., Glang, L., Choi, S. H., Singh, K., et al. (2020) · Science · doi:10.1126/science.aaz7959 · Cited by 118
RNA targeting and cleavage by the type III-Dv CRISPR effector complex
Schwartz, E. A., Bravo, J. P. K., Ahsan, M., Macias, L. A., McCafferty, C. L., Dangerfield, T. L., Walker, J. N., Brodbelt, J. S., Palermo, G., Fineran, P. C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47506-y · Cited by 30
Molecular view of ER membrane remodeling by the Sec61/TRAP translocon
Karki, S., Javanainen, M., Rehan, S., Tranter, D., Kellosalo, J., Huiskonen, J. T., Happonen, L., & Paavilainen, V. (2023) · The EMBO Reports · doi:10.15252/embr.202357910 · Cited by 25
Zhang, D., Qiao, L., Lei, X., Dong, X., Tong, Y., Wang, J., Wang, Z., & Zhou, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39410-8 · Cited by 21
Ojha, A. A., Votapka, L. W., & Amaro, R. E. (2023) · Chemical Science · doi:10.1039/d3sc04195f · Cited by 19
Short RNA chaperones promote aggregation-resistant TDP-43 conformers to mitigate neurodegeneration
Copley, K. E., Mauna, J. C., Danielson, H. L., Chen, Q., Ozguney, B., Ngo, M., Xie, L., Smirnov, A., Davis, M., Mayne, L., et al. (2026) · Science · doi:10.1126/science.adv3301 · Cited by 16
CRISPR-Cas12a REC2–Nuc interactions drive target-strand cleavage and constrain trans cleavage
Newman, A., Saha, A., Starrs, L., Arantes, P. R., Palermo, G., & Burgio, G. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf988 · Cited by 14
Molecular insights into RNA recognition and gene regulation by the TRIM-NHL protein Mei-P26
Salerno-Kochan, A., Horn, A., Ghosh, P., Nithin, C., Kościelniak, A., Meindl, A., Strauss, D., Krutyhołowa, R., Rossbach, O., Bujnicki, J. M., et al. (2022) · Life Science Alliance · doi:10.26508/lsa.202201418 · Cited by 8
Metadata
- Authors
- Zgarbová, M., Otyepka, M., Šponer, J., Mládek, A., Banáš, P., Cheatham, T. E., & Jurečka, P.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-08-02
- Volume
- 7
- Issue
- 9
- Pages
- 2886-2902
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2081046982
- Cited by
- 1,165
- References
- 2
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.