Automated sequence design of 2D wireframe DNA origami with honeycomb edges

Wireframe DNA origami has emerged as a powerful approach to fabricating nearly arbitrary 2D and 3D geometries at the nanometer-scale. Complex scaffold and staple routing needed to design wireframe DNA origami objects, however, render fully automated, geometry-based sequence design approaches essenti...

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Bibliographic Details
Main Authors: Jun, Hyungmin (Author), Wang, Xiao (Author), Bricker, William P (Author), Bathe, Mark (Author)
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: Springer, 2020-03-06T18:32:20Z.
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Description
Summary:Wireframe DNA origami has emerged as a powerful approach to fabricating nearly arbitrary 2D and 3D geometries at the nanometer-scale. Complex scaffold and staple routing needed to design wireframe DNA origami objects, however, render fully automated, geometry-based sequence design approaches essential for their synthesis. And wireframe DNA origami structural fidelity can be limited by wireframe edges that are composed only of one or two duplexes. Here we introduce a fully automated computational approach that programs 2D wireframe origami assemblies using honeycomb edges composed of six parallel duplexes. These wireframe assemblies show enhanced structural fidelity from electron microscopy-based measurement of programmed angles compared with identical geometries programmed using dual-duplex edges. Molecular dynamics provides additional theoretical support for the enhanced structural fidelity observed. Application of our top-down sequence design procedure to a variety of complex objects demonstrates its broad utility for programmable 2D nanoscale materials.
National Science Foundation (Grant CCF-1564025)
National Science Foundation (Grant CBET-1729397)
National Science Foundation (Grant CHE-1839155)
United States. Office of Naval Research (Grant N00014-17-1-2609)