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Related Experiment Video

Updated: Feb 18, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware

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Published on: December 25, 2021

2D Enzyme Cascade Network with Efficient Substrate Channeling by Swinging Arms.

Yuhe R Yang^1,2, Jinglin Fu³, Shaun Wootten¹

¹Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 S. McAllister Avenue, Tempe, AZ, 85287, USA.

Chembiochem : a European Journal of Chemical Biology

|November 28, 2017

Summary

Researchers created artificial 2D enzyme networks on DNA origami templates. These networks, using glucose-6-phosphate dehydrogenase (G6PDH) and lactate dehydrogenase (LDH) with swinging arms, improved reaction efficiency through enhanced intermediate transfer.

Keywords:

DNA nanotechnology enzyme cascade redox intermediate scaffold

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Published on: March 20, 2021

Area of Science:

Biochemistry
Synthetic Biology
Nanotechnology

Background:

Cellular metabolism relies on enzyme networks for cascade reactions.
Compartmentalized enzymes are crucial for efficient metabolic pathways.

Purpose of the Study:

To assemble an artificial 2D enzyme network using DNA origami.
To enhance reaction efficiency through controlled substrate channeling and intermediate transfer.

Main Methods:

Utilized DNA origami to create a wireframe template for enzyme assembly.
Incorporated glucose-6-phosphate dehydrogenase (G6PDH) and lactate dehydrogenase (LDH) with swinging arms.
Characterized assemblies using gel electrophoresis and atomic force microscopy (AFM).

Main Results:

Successfully assembled 2D enzyme networks with spatial control over enzyme arrangement.
Optimized swinging arm length and stoichiometry for efficient substrate channeling.
Demonstrated higher reaction efficiency in 2D enzyme systems compared to single enzyme pairs.

Conclusions:

Artificial 2D enzyme networks can be effectively constructed using DNA origami.
Optimized spatial organization enhances the efficiency of cascade reactions by promoting intermediate transfer.
This approach offers a platform for designing artificial metabolic pathways.