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Cited by 336

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Nature and magnitude of aromatic stacking of nucleic acid bases

Šponer, Jiří, Riley, Kevin E., Hobza, Pavel·2008·Physical Chemistry Chemical Physics

Abstract

This review summarises recent advances in quantum chemical calculations of base-stacking forces in nucleic acids. We explain in detail the very complex relationship between the gas-phase base-stacking energies, as revealed by quantum chemical (QM) calculations, and the highly variable roles of these interactions in nucleic acids. This issue is rarely discussed in quantum chemical and physical chemistry literature. We further extensively discuss methods that are available for base-stacking studies, complexity of comparison of stacking calculations with gas phase experiments, balance of forces in stacked complexes of nucleic acid bases, and the relation between QM and force field descriptions. We also review all recent calculations on base-stacking systems, including details analysis of the B-DNA stacking. Specific attention is paid to the highest accuracy QM calculations, to the decomposition of the interactions, and development of dispersion-balanced DFT methods. Future prospects of computational studies of base stacking are discussed.

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Crossref · 10.1039/b719370j

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Metadata

Authors
Šponer, J., Riley, K. E., & Hobza, P.
Venue
Physical Chemistry Chemical Physics
Published
2008
Volume
10
Issue
19
Pages
2595
ISSN
  • 1463-9076 (ISSN-L)
  • 1463-9084
OpenAlex
W1966547781
Cited by
336
References
3
FWCI
12.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nature and magnitude of aromatic stacking of nucleic acid bases. https://app.cochat.ai/papers/10.1039/b719370j
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Šponer, J., Riley, K. E., & Hobza, P. (2008). Nature and magnitude of aromatic stacking of nucleic acid bases. Physical Chemistry Chemical Physics. https://doi.org/10.1039/b719370j

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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.