Fangzhou Zhao, Daniel Saliba, Jathavan Asohan, Hanadi F Sleiman
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引用次数: 0
Abstract
Covalently branched DNA molecules are hybrid structures where a small molecule core is covalently linked to different DNA strands. They merge the programmability of DNA nanotechnology with synthetic molecules' functionality, offering enhanced stability over their non-covalent counterparts like double-crossover tiles. They enable the efficient assembly of stable DNA nanostructures with new geometries and functionalities. These motifs can be prepared through "DNA printing", which uses a DNA nanostructure as a temporary template to covalently transfer specific DNA strands to a small molecule core. Here, the "printing" process is streamlined with DNA-immobilized polystyrene microspheres, laying the foundation for future automated DNA printing devices. First, the DNA template hybridizes with reactive complementary strands, which are then crosslinked using a small molecule. Second, beads with fully complementary molecules capture the "daughter" products by strand displacement. This ensures high product yields and high recovery of the "mother" template for reuse. This method allows the precise transfer of different DNA strands onto various small molecules, including aromatics and functional porphyrins. Notably, these branching motifs exhibit remarkable stability toward nucleases without any specialized modifications. Moreover, they can serve as robust building blocks for precise assembly of 3D structures, such as an addressable tetrahedron from only two components.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.