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Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding

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Young, Tom, Abel, Robert, Kim, Byungchan, Berne, Bruce J., Friesner, Richard A.·2007·Proceedings of the National Academy of Sciences

Abstract

The thermodynamic properties and phase behavior of water in confined regions can vary significantly from that observed in the bulk. This is particularly true for systems in which the confinement is on the molecular-length scale. In this study, we use molecular dynamics simulations and a powerful solvent analysis technique based on inhomogenous solvation theory to investigate the properties of water molecules that solvate the confined regions of protein active sites. Our simulations and analysis indicate that the solvation of protein active sites that are characterized by hydrophobic enclosure and correlated hydrogen bonds induce atypical entropic and enthalpic penalties of hydration. These penalties apparently stabilize the protein–ligand complex with respect to the independently solvated ligand and protein, which leads to enhanced binding affinities. Our analysis elucidates several challenging cases, including the super affinity of the streptavidin–biotin system.

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Metadata

Authors
Young, T., Abel, R., Kim, B., Berne, B. J., & Friesner, R. A.
Venue
Proceedings of the National Academy of Sciences
Published
2007-01-16
Volume
104
Issue
3
Pages
808-813
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2081719547
Cited by
683
References
0
FWCI
7.06
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. https://app.cochat.ai/papers/10.1073/pnas.0610202104
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Young, T., Abel, R., Kim, B., Berne, B. J., & Friesner, R. A. (2007). Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0610202104

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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.