The DOI matches its Crossref record.
Molecular Recognition of Amino Acids, Peptides, and Proteins by Cucurbit[n]uril Receptors
Abstract
At the forefront of the endeavor to understand and manipulate living systems is the design and study of receptors that bind with high affinity and selectivity to specific amino acids, peptides, and proteins. Cucurbit[n]urils are among the most promising class of synthetic receptors for these targets due to their high affinities and selectivities in aqueous media and to the unique combination of electrostatic and hydrophobic interactions that govern binding. The fundamental supramolecular chemistry in this area has been explored in depth, and novel, useful applications are beginning to emerge.
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Citations
Cited by
Modulators of Protein–Protein Interactions
Milroy, L.-G., Grossmann, T. N., Hennig, S., Brunsveld, L., & Ottmann, C. (2014) · Chemical Reviews · doi:10.1021/cr400698c · Cited by 497
Dynamically Analyte-Responsive Macrocyclic Host–Fluorophore Systems
Ghale, G., & Nau, W. M. (2014) · Accounts of Chemical Research · doi:10.1021/ar500116d · Cited by 361
Metadata
- Authors
- Urbach, A. R., & Ramalingam, V.
- Venue
- Israel Journal of Chemistry
- Publisher
- Wiley
- Published
- 2011-04-15
- Volume
- 51
- Issue
- 5-6
- Pages
- 664-678
- ISSN
- 0021-2148 (ISSN-L)
- 1869-5868
- OpenAlex
- W2007869738
- Cited by
- 185
- References
- 0
- FWCI
- 7.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Molecular Recognition of Amino Acids, Peptides, and Proteins by Cucurbit[n]uril Receptors. https://app.cochat.ai/papers/10.1002/ijch.201100035
- The paper
- Urbach, A. R., & Ramalingam, V. (2011). Molecular Recognition of Amino Acids, Peptides, and Proteins by Cucurbit[n]uril Receptors. Israel Journal of Chemistry. https://doi.org/10.1002/ijch.201100035
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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.
