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Nonstatistical binding of a protein to clustered carbohydrates

Open access
Horan, Nina, Yan, Lin, Isobe, Hiroyuki, Whitesides, George M., Kahne, Daniel·1999·Proceedings of the National Academy of Sciences

Abstract

Carbohydrate-derivatized self-assembled monolayers (SAMs) are used as a model system to address issues involving cell-surface carbohydrate–protein interactions. Here we examine the influence of carbohydrate surface density on protein-binding avidity. We show that the binding selectivity of Bauhinia purpurea lectin switches from one carbohydrate ligand to another as the surface density of the carbohydrate ligands increases from values of χ sugar ≈ 0.1–1.0. Polyvalent binding is possible at all surface densities investigated; hence, the switch in selectivity is not due simply to the achievement of a critical density that permits polyvalent contacts. Instead, secondary interactions at high surface densities promote a switch in carbohydrate-binding selectivity. These findings may have implications for how changes in the composition and the density of cell-surface carbohydrates influence biological recognition processes and regulatory pathways.

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Metadata

Authors
Horan, N., Yan, L., Isobe, H., Whitesides, G. M., & Kahne, D.
Venue
Proceedings of the National Academy of Sciences
Published
1999-10-12
Volume
96
Issue
21
Pages
11782-11786
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2148444220
Cited by
203
References
0
FWCI
3.48
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Nonstatistical binding of a protein to clustered carbohydrates. https://app.cochat.ai/papers/10.1073/pnas.96.21.11782
The paper
Horan, N., Yan, L., Isobe, H., Whitesides, G. M., & Kahne, D. (1999). Nonstatistical binding of a protein to clustered carbohydrates. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.96.21.11782

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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.