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Prediction of DNA origami shape using graph neural network

Nature Materials (2024)Cite this article

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Abstract

Unlike proteins, which have a wealth of validated structural data, experimentally or computationally validated DNA origami datasets are limited. Here we present a graph neural network that can predict the three-dimensional conformation of DNA origami assemblies both rapidly and accurately. We develop a hybrid data-driven and physics-informed approach for model training, designed to minimize not only the data-driven loss but also the physics-informed loss. By employing an ensemble strategy, the model can successfully infer the shape of monomeric DNA origami structures almost in real time. Further refinement of the model in an unsupervised manner enables the analysis of supramolecular assemblies consisting of tens to hundreds of DNA blocks. The proposed model enables an automated inverse design of DNA origami structures for given target shapes. Our approach facilitates the real-time virtual prototyping of DNA origami, broadening its design space.

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Fig. 1: DGNN.
Fig. 2: Hybrid data-driven and physics-informed training algorithm.
Fig. 3: Ensemble model.
Fig. 4: Analysis of supramolecular assemblies.
Fig. 5: Design optimization.
Fig. 6: Experimental validation of optimized spiral and helical structures.

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Data availability

All data are included in the Article and its Supplementary Information. Source data are provided with this paper.

Code availability

The proposed framework is provided through Deep SNUPI53, which is available via GitHub at https://github.com/SSDL-SNU/DeepSNUPI.

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Acknowledgements

This research was supported by the National Convergence Research of Scientific Challenges through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) of the Korean government (NRF-2020M3F7A1094299), the Bio & Medical Technology Development Program of the NRF funded by the Korean government (MSIT) (NRF-2022M3E5F1018465) and the National Supercomputing Center with supercomputing resources including technical support (KSC-2021-CHA-0025). Financial support was also provided by the Youlchon Foundation (Nongshim Corporation and affiliated companies) in Korea. J.Y.L. acknowledges support from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (NRF-2021R1C1C2003554).

Author information

Author notes

  1. These authors contributed equally: Chien Truong-Quoc, Jae Young Lee.

Authors and Affiliations

  1. Department of Mechanical Engineering, Seoul National University, Seoul, Korea

    Chien Truong-Quoc, Kyung Soo Kim & Do-Nyun Kim

  2. Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea

    Jae Young Lee & Do-Nyun Kim

  3. Institute of Engineering Research, Seoul National University, Seoul, Korea

    Do-Nyun Kim

Authors
  1. Chien Truong-Quoc
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Contributions

J.Y.L., C.T.-Q. and D.-N.K. designed the study. C.T.-Q. and J.Y.L. developed the approach. C.T.-Q. and J.Y.L. analysed the data. K.S.K. performed the experiments and processed the results. J.Y.L., C.T.-Q., K.S.K. and D.-N.K. wrote and revised the manuscript.

Corresponding author

Correspondence to Do-Nyun Kim.

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Competing interests

D.-N.K., J.Y.L. and C.T.-Q. are co-inventors on a patent application related to this work filed by Seoul National University R&DB Foundation (no. KR10-2023-0064976 filed on 19 May 2023), Korea.

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Nature Materials thanks Hendrik Dietz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Notes 1–8, Figs. 1–9 and Tables 1–17.

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Truong-Quoc, C., Lee, J.Y., Kim, K.S. et al. Prediction of DNA origami shape using graph neural network. Nat. Mater. (2024). https://doi.org/10.1038/s41563-024-01846-8

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