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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.

 

Recent advances in structural DNA nanotechnology have been facilitated by design tools that continue to push the limits of structural complexity while simplifying an often-tedious design process. We recently introduced the software MagicDNA, which enables design of complex 3D DNA assemblies with many components; however, the design of structures with free-form features like vertices or curvature still required iterative design guided by simulation feedback and user intuition. Here, we present an updated design tool, MagicDNA 2.0, that automates the design of free-form 3D geometries, leveraging design models informed by coarse-grained molecular dynamics simulations. Our GUI-based, stepwise design approach integrates a high level of automation with versatile control over assembly and subcomponent design parameters. We experimentally validated this approach by fabricating a range of DNA origami assemblies with complex free-form geometries, including a 3D Nozzle, G-clef, and Hilbert and Trifolium curves, confirming excellent agreement between design input, simulation, and structure formation.

 

In this work, we describe the development of a computational model for arrays of rotary DNA origami elements which can self-organize on a large scale and explore the interesting morphologies and order–disorder transition behavior of these systems.

 

Structural DNA nanotechnology has enabled the design and construction of complex nanoscale structures with precise geometry and programmable dynamic and mechanical properties. Recent efforts have led to major advances in the capacity to actuate shape changes of DNA origami devices and incorporate DNA origami into larger assemblies, which open the prospect of using DNA to design shape-morphing assemblies as components of micro-scale reconfigurable or sensing materials. Indeed, a few studies have constructed higher order assemblies with reconfigurable devices; however, these demonstrations have utilized structures with relatively simple motion, primarily hinges that open and close. To advance the shape changing capabilities of DNA origami assemblies, we developed a multi-component DNA origami 6-bar mechanism that can be reconfigured into various shapes and can be incorporated into larger assemblies while maintaining capabilities for a variety of shape transformations. We demonstrate the folding of the 6-bar mechanism into four different shapes and demonstrate multiple transitions between these shapes. We also studied the shape preferences of the 6-bar mechanism in competitive folding reactions to gain insight into the relative free energies of the shapes. Furthermore, we polymerized the 6-bar mechanism into tubes with various cross-sections, defined by the shape of the individual mechanism, and we demonstrate the ability to change the shape of the tube cross-section. This expansion of current single-device reconfiguration to higher order scales provides a foundation for nano to micron scale DNA nanotechnology applications such as biosensing or materials with tunable properties.