The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles
Open accessAbstract
Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R 2 (less than 0.40). Here, the performance with R 2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R 2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.
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Cites
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Cited by
On the mechanism of tissue-specific mRNA delivery by selective organ targeting nanoparticles
Dilliard, S. A., Cheng, Q., & Siegwart, D. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109256118 · Cited by 1,018
A translational framework to DELIVER nanomedicines to the clinic
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Engineering nano-drug biointerface to overcome biological barriers toward precision drug delivery
Waheed, S., Li, Z., Zhang, F., Chiarini, A., Armato, U., & Wu, J. (2022) · Journal of Nanobiotechnology · doi:10.1186/s12951-022-01605-4 · Cited by 259
Advancing engineering design strategies for targeted cancer nanomedicine
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Metadata
- Authors
- Ban, Z., Yuan, P., Yu, F., Peng, T., Zhou, Q., & Hu, X.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2020-04-24
- Volume
- 117
- Issue
- 19
- Pages
- 10492-10499
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W3020128160
- Cited by
- 313
- References
- 11
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
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