Deep-Learning Assisted, Single-molecule Imaging analysis (Deep-LASI) of multi-color DNA Origami structures

Wanninger, Simon; Asadiatouei, Pooyeh; Bohlen, Johann; Salem, Clemens-Baessem; Tinnefeld, Philip; Ploetz, Evelyn; Lamb, Don C.

doi:10.1101/2023.01.31.526220

Cited by 4 publications

(2 citation statements)

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“…Considering that protein structure prediction has reached the single-structure frontier 84 , the information from smFRET experiments could leverage the power of artificial intelligence to resolve more complex multi-state and ensemble structural models 83 . Vice versa, the power of artificial intelligence and deep learning can be used to increase the throughput for the design and analysis of smFRET experiments [85][86][87] .…”

Section: Discussionmentioning

confidence: 99%

Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins

et al. 2023

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Single-molecule Förster-resonance energy transfer (smFRET) experiments allow the study of biomolecular structure and dynamics in vitro and in vivo. We performed an international blind study involving 19 laboratories to assess the uncertainty of FRET experiments for proteins with respect to the measured FRET efficiency histograms, determination of distances, and the detection and quantification of structural dynamics. Using two protein systems with distinct conformational changes and dynamics, we obtained an uncertainty of the FRET efficiency ≤0.06, corresponding to an interdye distance precision of ≤2 Å and accuracy of ≤5 Å. We further discuss the limits for detecting fluctuations in this distance range and how to identify dye perturbations. Our work demonstrates the ability of smFRET experiments to simultaneously measure distances and avoid the averaging of conformational dynamics for realistic protein systems, highlighting its importance in the expanding toolbox of integrative structural biology.

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Section: Discussionmentioning

confidence: 99%

Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins

et al. 2023

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“…In contrast, from traditional X-ray crystallography or cryo-EM, there are only ~ 200 thousand protein structures shared in the protein data bank (PDB). Specific to DNA nanotechnology, recent studies [28][29][30] showed the DNN is also a great solution for automatically recognizing nanostructure in atomic force microscopy or fluorescence microscopy with high accuracy, which provided a foundation to solving the DNA nanostructure identification problem. However, these works only demonstrated the feasibility of identifying static nanostructures from images and did not address the need for automated property characterization, which is necessary to overcome the characterization bottleneck for dynamic DNA nanodevices.…”

Section: Accelerating the Characterization Of Dynamic Dna Origami Dev...mentioning

confidence: 99%

Accelerating the characterization of dynamic DNA origami devices with deep neural networks

Wang,

Jin,

Castro

2023

Sci Rep

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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.

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Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

Wanninger,

Asadiatouei,

Bohlen

et al. 2023

Nat Commun

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Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite powered by deep neural networks to rapidly analyze single-, two- and three-color single-molecule data, especially from single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Deep-LASI automatically sorts recorded traces, determines FRET correction factors and classifies the state transitions of dynamic traces all in ~20–100 ms per trajectory. We benchmarked Deep-LASI using ground truth simulations as well as experimental data analyzed manually by an expert user and compared the results with a conventional Hidden Markov Model analysis. We illustrate the capabilities of the technique using a highly tunable L-shaped DNA origami structure and use Deep-LASI to perform titrations, analyze protein conformational dynamics and demonstrate its versatility for analyzing both total internal reflection fluorescence microscopy and confocal smFRET data.

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Deep-Learning Assisted, Single-molecule Imaging analysis (Deep-LASI) of multi-color DNA Origami structures

Cited by 4 publications

References 50 publications

Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins

Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins

Accelerating the characterization of dynamic DNA origami devices with deep neural networks

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

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