Prediction of DNA origami shape using graph neural network

被引:8
|
作者
Truong-Quoc, Chien [1 ]
Lee, Jae Young [2 ]
Kim, Kyung Soo [1 ]
Kim, Do-Nyun [1 ,2 ,3 ]
机构
[1] Seoul Natl Univ, Dept Mech Engn, Seoul, South Korea
[2] Seoul Natl Univ, Inst Adv Machines & Design, Seoul, South Korea
[3] Seoul Natl Univ, Inst Engn Res, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
FOLDING DNA; ASSEMBLIES;
D O I
10.1038/s41563-024-01846-8
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
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. Limited datasets hinder the accurate prediction of DNA origami structures. A data-driven and physics-informed approach for model training is presented using a graph neural network to facilitate the rapid virtual prototyping of DNA-based nanostructures.
引用
收藏
页码:984 / 992
页数:13
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