ArticleProtein science : a publication of the Protein Society2024
Tunable crystalline assemblies using surface-engineered protein cages.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Effective charge saturation in ferritin cages.Scientific reports · 2026Article
- Tunable crystalline assemblies using surface-engineered protein cages.Protein science : a publication of the Protein Society · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Assembly of nanoparticles into superlattices yields nanomaterials with novel properties. We have recently shown that engineered protein cages are excellent building blocks for the assembly of inorganic nanoparticles into highly structured hybrid materials, with unprecedented precision. In this study, we show that the protein matrix, composed of surface-charged protein cages, can be readily tuned to achieve a number of different crystalline assemblies. Simply by altering the assembly conditions, different types of crystalline structures were produced, without the need to further modify the cages. Future work can utilize these new protein scaffolds to create nanoparticle superlattices with various assembly geometries and thus tune the functionality of these hybrid materials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.