Researchers have developed a computational design framework that improves the reliability of DNA origami assembly by reducing unwanted molecular interactions that can derail nanoscale structures during fabrication. The work, published in Nature Communications, introduces a sequence-selection approach that helps predict and avoid problematic DNA interactions prior to synthesis. By optimizing sequence design in advance, the tool improves folding outcomes in experimental DNA nanostructures that are otherwise prone to misassembly.
DNA origami techniques allow researchers to build precise two- and three-dimensional nanoscale structures by folding a long single DNA strand with hundreds of shorter “staple” strands. When heated and slowly cooled, the staples bind to specific regions of the scaffold strand, guiding it into a predefined shape.
However, unintended interactions between strands can disrupt assembly. These off-target interactions can create kinetic traps that prevent structures from folding correctly, reducing yields even when the overall design is structurally sound.
Improving sequence design to reduce assembly errors
To address this challenge, an international team developed a computational framework for sequence selection to reduce predicted off-target interactions in DNA origami design. The research was led by scientists from Newcastle University in conjunction with the University of Bordeaux, the Università degli Studi di Udine, the Israel Institute of Technology, and Universität Bonn.
The researchers tested both two-dimensional and three-dimensional DNA origami structures. According to the study, designs optimized using the framework yielded higher folding yields than non-optimized sequence configurations, while sequences with higher predicted off-target interactions were more likely to fail during assembly.
The framework evaluates scaffold and staple sequence regions to identify configurations more likely to produce unintended interactions, helping researchers prioritize designs before synthesis.
Experimental validation of computational predictions
To validate the approach, the team combined computational modeling with experimental techniques, including imaging and single-molecule optical tweezers. These methods allowed the researchers to assess folding behavior and structural performance across different DNA origami designs.
The results show that sequence-optimized designs produced more reliable folding outcomes and improved structural consistency across tested configurations. The findings highlight the influence of sequence selection on both assembly efficiency and nanoscale structural quality.
Implications for DNA nanofabrication workflows
For laboratories working in nanotechnology and molecular engineering, the framework may help streamline DNA origami design by identifying sequences less likely to result in misfolding. The authors report that better sequence-level design choices can improve assembly outcomes and contribute to more consistent fabrication workflows. Although the approach does not eliminate experimental variability, it adds a predictive step before synthesis.
The findings suggest that incorporating computational sequence selection into DNA nanotechnology workflows could enhance the reliability of nanoscale DNA structures used in applications such as molecular devices and targeted delivery systems.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
