Accelerating the characterization of dynamic DNA origami devices with deep neural networks.
Accelerating the characterization of dynamic DNA origami devices with deep neural networks.
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DOI：
10.1038/s41598-023-41459-w
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发表时间：
2023-09-14
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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动态DNA纳米器件的机械特性是必不可少的，以促进其在分子诊断，力传感和纳米机器人，依赖于设备的重新配置和与其他材料的相互作用等应用中的使用。评价动态DNA纳米器件的机械性能的常用方法是通过量化构象分布，其中波动的幅度与刚度相关。这通常通过从实验图像手动测量来执行，这是一个繁琐的过程，也是表征流水线中的关键瓶颈。虽然许多工具支持静态分子结构的分析，但需要工具来促进经历大构象波动的动态DNA装置的快速表征。在这里，我们开发了一个基于深度神经网络（DNN）的数据处理管道来解决这个问题。YOLOv 5和Resnet 50网络架构用于两个关键子任务：粒子检测和姿态（即构象）估计。我们展示了有效的网络性能（粒子检测中的F1得分为0.85），并且在有限的用户输入和小训练集（~ 5到10张图像）的情况下与实验分布具有良好的一致性。我们还证明了该管道可以应用于多个纳米器件，为动态DNA器件的快速表征提供了一种强大的方法。
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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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
4.6
作者：
Chiriboga M;Green CM;Hastman DA;Mathur D;Wei Q;Díaz SA;Medintz IL;Veneziano R
通讯作者：
Veneziano R
影响因子：
64.8
作者：
Douglas, Shawn M.;Dietz, Hendrik;Liedl, Tim;Hoegberg, Bjoern;Graf, Franziska;Shih, William M.
通讯作者：
Shih, William M.
磁驱动DNA微纳米机器人的最新进展
DOI：
10.34133/2022/9758460
发表时间：
2022
期刊：
Cyborg and bionic systems (Washington, D.C.)
影响因子：
--
作者：
通讯作者：
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影响因子：
16.6
作者：
Liu, Minghui;Fu, Jinglin;Yan, Hao
通讯作者：
Yan, Hao