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Cited by 650

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy

Open access
King, Neil P., Sheffler, William, Sawaya, Michael R., Vollmar, Breanna S., Sumida, John P., André, Ingemar, Gonen, Tamir, Yeates, Todd O., Baker, David·2012·Science

Abstract

Design and Build: Self-assembling biomolecules are attractive building blocks in the development of functional materials. Sophisticated DNA-based materials have been developed; however, progress in designing protein-based materials has been slower. King et al. (p. 1171 ) describe a general computational method in which protein building blocks are first symmetrically docked onto a target architecture, and then binding interfaces that drive self-assembly of the building blocks are designed. As a proof of principle, trimeric building blocks were used to design self-assembling 12-subunit complexes with tetrahedral symmetry and 24-subunit complexes with octahedral symmetry. Lai et al. (p. 1129 ) were able to build a 12-subunit tetrahedral protein cage from fused oligomeric protein domains.

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Crossref · 10.1126/science.1219364

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Metadata

Authors
King, N. P., Sheffler, W., Sawaya, M. R., Vollmar, B. S., Sumida, J. P., André, I., Gonen, T., Yeates, T. O., & Baker, D.
Venue
Science
Published
2012-06
Volume
336
Issue
6085
Pages
1171-1174
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2015526636
Cited by
650
References
6
FWCI
19.17
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy. https://app.cochat.ai/papers/10.1126/science.1219364
The paper
King, N. P., Sheffler, W., Sawaya, M. R., Vollmar, B. S., Sumida, J. P., André, I., Gonen, T., Yeates, T. O., & Baker, D. (2012). Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy. Science. https://doi.org/10.1126/science.1219364

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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.