Chien Truong-Quoc (서울대학교) | 한빛사논문 > 한빛사

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Chien Truong-QuocChien Truong-Quoc

서울대학교

Nat. Mater., 14 March 2024 | https://doi.org/10.1038/s41563-024-01846-8 Prediction of DNA origami shape using graph neural network

Chien Truong-Quoc^1,4, Jae Young Lee ^2,4, Kyung Soo Kim ¹ & Do-Nyun Kim ^1,2,3,*

¹Department of Mechanical Engineering, Seoul National University, Seoul, Korea.
²Institute of Advanced Machines and Design, Seoul National University, Seoul, Korea.
³Institute of Engineering Research, Seoul National University, Seoul, Korea.
⁴These authors contributed equally: Chien Truong-Quoc, Jae Young Lee.

^*Corresponding author: correspondence to Do-Nyun Kim

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.

논문정보

형식Research article
게재일2024년 03월 (BRIC 등록일 2024-03-15)
연구진국내 연구진
분야 바이오·의료융합 > 바이오센싱 및 나노바이오물질

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