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Cited by 248

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Peptide/Protein Hybrid Materials: Enhanced Control of Structure and Improved Performance through Conjugation of Biological and Synthetic Polymers

Open access
Vandermeulen, Guido W. M., Klok, Harm‐Anton·2004·Macromolecular Bioscience

Abstract

Summary: The conjugation of peptides/proteins and synthetic polymers is a useful strategy to overcome some of the limitations related to the use of the individual components. This review will highlight two aspects: enhanced structural control at the nanometer level and improved performance, in particular with respect to biomedical applications. In the former case, peptide sequences are mainly used to mediate self‐assembly of synthetic polymers. In the latter case, conjugation of an appropriate synthetic polymer to a pharmaceutically active peptide/protein can, for example, prevent premature enzymatic degradation and enhance blood circulation times, which is therapeutically advantageous. image

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Crossref · 10.1002/mabi.200300079

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Metadata

Authors
Vandermeulen, G. W. M., & Klok, H.
Venue
Macromolecular Bioscience
Publisher
Wiley
Published
2004-04-07
Volume
4
Issue
4
Pages
383-398
ISSN
  • 1616-5187 (ISSN-L)
  • 1616-5195
OpenAlex
W2025531478
Cited by
248
References
4
FWCI
7.40
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Peptide/Protein Hybrid Materials: Enhanced Control of Structure and Improved Performance through Conjugation of Biological and Synthetic Polymers. https://app.cochat.ai/papers/10.1002/mabi.200300079
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Vandermeulen, G. W. M., & Klok, H. (2004). Peptide/Protein Hybrid Materials: Enhanced Control of Structure and Improved Performance through Conjugation of Biological and Synthetic Polymers. Macromolecular Bioscience. https://doi.org/10.1002/mabi.200300079

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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.