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基于人工智能预测DNA纳米结构上的蛋白质冠组成

AI-Based Prediction of Protein Corona Composition on DNA Nanostructures.

作者信息

Huzar Jared, Coreas Roxana, Landry Markita P, Tikhomirov Grigory

机构信息

Biophysics Graduate Group, University of California, Berkeley, Berkeley, California 94720, United States.

Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States.

出版信息

ACS Nano. 2025 Feb 4;19(4):4333-4345. doi: 10.1021/acsnano.4c12259. Epub 2025 Jan 8.

DOI:10.1021/acsnano.4c12259
PMID:39772513
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11803750/
Abstract

DNA nanotechnology has emerged as a powerful approach to engineering biophysical tools, therapeutics, and diagnostics because it enables the construction of designer nanoscale structures with high programmability. Based on DNA base pairing rules, nanostructure size, shape, surface functionality, and structural reconfiguration can be programmed with a degree of spatial, temporal, and energetic precision that is difficult to achieve with other methods. However, the properties and structure of DNA constructs are greatly altered due to spontaneous protein adsorption from biofluids. These adsorbed proteins, referred to as the protein corona, remain challenging to control or predict, and subsequently, their functionality and fate are difficult to engineer. To address these challenges, we prepared a library of diverse DNA nanostructures and investigated the relationship between their design features and the composition of their protein corona. We identified protein characteristics important for their adsorption to DNA nanostructures and developed a machine-learning model that predicts which proteins will be enriched on a DNA nanostructure based on the DNA structures' design features and protein properties. Our work will help to understand and program the function of DNA nanostructures for biophysical and biomedical applications.

摘要

DNA纳米技术已成为一种强大的方法,用于构建生物物理工具、治疗药物和诊断手段,因为它能够构建具有高度可编程性的定制纳米级结构。基于DNA碱基配对规则,可以在空间、时间和能量精度上对纳米结构的大小、形状、表面功能和结构重构进行编程,而这是其他方法难以实现的。然而,由于生物流体中蛋白质的自发吸附,DNA构建体的性质和结构会发生很大变化。这些吸附的蛋白质被称为蛋白质冠,其控制或预测仍然具有挑战性,因此,它们的功能和归宿很难设计。为了应对这些挑战,我们制备了一个多样化的DNA纳米结构文库,并研究了它们的设计特征与其蛋白质冠组成之间的关系。我们确定了对蛋白质吸附到DNA纳米结构至关重要的蛋白质特征,并开发了一种机器学习模型,该模型可以根据DNA结构的设计特征和蛋白质特性预测哪些蛋白质会在DNA纳米结构上富集。我们的工作将有助于理解和设计DNA纳米结构在生物物理和生物医学应用中的功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/bd0fd0614cc8/nn4c12259_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/76fe5e524778/nn4c12259_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/28e8e4e60c2a/nn4c12259_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/3020604ea2c0/nn4c12259_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/28caa7f7c10a/nn4c12259_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/b41f1a9fc21e/nn4c12259_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/bd0fd0614cc8/nn4c12259_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/76fe5e524778/nn4c12259_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/28e8e4e60c2a/nn4c12259_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/3020604ea2c0/nn4c12259_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/28caa7f7c10a/nn4c12259_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/b41f1a9fc21e/nn4c12259_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ddc/11803750/bd0fd0614cc8/nn4c12259_0006.jpg

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本文引用的文献

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Collective cell behaviors manipulated by synthetic DNA nanostructures.由合成DNA纳米结构操纵的集体细胞行为。
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CD5L is a canonical component of circulatory IgM.CD5L 是循环 IgM 的规范组成部分。
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Confinement in Dual-Chain-Locked DNA Origami Nanocages Programs Marker-Responsive Delivery of CRISPR/Cas9 Ribonucleoproteins.
蛋白质冠层组成调节结肠癌细胞对聚合物胶束的摄取。
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