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Gradated assembly of multiple proteins into supramolecular nanomaterials

Open access
Hudalla, Gregory A., Sun, Tao, Gasiorowski, Joshua Z., Han, Huifang, Tian, Ye F., Chong, Anita S., Collier, Joel H.·2014·Nature Materials

Abstract

Biomaterials displaying precise ratios of different bioactive protein components are critical for applications ranging from vaccines to regenerative medicine, but their design is often hindered by limited choices and cross-reactivity of protein conjugation chemistries. Here, we describe a strategy for inducing multiple different expressed proteins of choice to assemble into nanofibers and gels with exceptional compositional control. The strategy employs novel “βTail” tags, which allow for good protein expression in bacteriological cultures, yet can be induced to co-assemble into nanomaterials when mixed with additional β-sheet fibrillizing peptides. Multiple different βTail fusion proteins could be inserted into peptide nanofibers alone or in combination at predictable, smoothly gradated concentrations, providing a simple yet versatile route to install precise combinations of proteins into nanomaterials. The technology is illustrated by achieving precisely targeted hues using mixtures of fluorescent proteins, by creating nanofibers bearing enzymatic activity, and by adjusting antigenic dominance in vaccines.

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Metadata

Authors
Hudalla, G. A., Sun, T., Gasiorowski, J. Z., Han, H., Tian, Y. F., Chong, A. S., & Collier, J. H.
Venue
Nature Materials
Published
2014-06-15
Volume
13
Issue
8
Pages
829-836
ISSN
  • 1476-1122 (ISSN-L)
  • 1476-4660
OpenAlex
W2066839973
Cited by
269
References
0
FWCI
9.45
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Gradated assembly of multiple proteins into supramolecular nanomaterials. https://app.cochat.ai/papers/10.1038/nmat3998
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Hudalla, G. A., Sun, T., Gasiorowski, J. Z., Han, H., Tian, Y. F., Chong, A. S., & Collier, J. H. (2014). Gradated assembly of multiple proteins into supramolecular nanomaterials. Nature Materials. https://doi.org/10.1038/nmat3998

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.