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Dynamic DNA nanotechnology involves the creation of nanoscale devices made of DNA whose primary function arises from their ability to undergo controlled motion or reconfiguration. In the past two decades, dynamic DNA nanotechnology has evolved to the point where it is now being employed in devices intended for applications in sensing, drug delivery, computation, nanorobotics, and more. In this review article, we discuss the design of dynamic DNA nanodevices and the characterization and prediction of device behavior. We also identify a number of continuing challenges in dynamic DNA nanotechnology and discuss potential solutions to those challenges.
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Accelerating the characterization of dynamic DNA origami devices with deep neural networks
Abstract Mechanical characterization of dynamic DNA nanodevices is essential to facilitate their use in applications like molecular diagnostics, force sensing, and nanorobotics that rely on device reconfiguration and interactions with other materials. A common approach to evaluate the mechanical properties of dynamic DNA nanodevices is by quantifying conformational distributions, where the magnitude of fluctuations correlates to the stiffness. This is generally carried out through manual measurement from experimental images, which is a tedious process and a critical bottleneck in the characterization pipeline. While many tools support the analysis of static molecular structures, there is a need for tools to facilitate the rapid characterization of dynamic DNA devices that undergo large conformational fluctuations. Here, we develop a data processing pipeline based on Deep Neural Networks (DNNs) to address this problem. The YOLOv5 and Resnet50 network architecture were used for the two key subtasks: particle detection and pose (i.e. conformation) estimation. We demonstrate effective network performance (F1 score 0.85 in particle detection) and good agreement with experimental distributions with limited user input and small training sets (~ 5 to 10 images). We also demonstrate this pipeline can be applied to multiple nanodevices, providing a robust approach for the rapid characterization of dynamic DNA devices.
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- Award ID(s):
- 1921881
- PAR ID:
- 10462799
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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