Machine Learning-Assisted DNA Origami Shape Sorting Using Fingerprinting Nanosensors and Feature Engineering.

Reconfigurable DNA nanostructures have emerged as a promising research area with applications in drug delivery, molecular computing, biosensing, and stimuli-responsive soft nanomaterials. While significant progress has been made in creating novel DNA nanostructures and exploring their applications, comparatively little effort has focused on developing new methodologies to confirm their folding. Conventional imaging approaches typically rely on sophisticated microscopy techniques including atomic force and transmission electron microscopy. Alternative low-cost methods for verifying DNA nanostructure assembly and shape sorting are thus highly valuable. Here, we present a fingerprinting nanosensor array integrated with machine learning (ML) to distinguish between two DNA origami shapes (triangle and nanotube) and to differentiate them from an unfolded scaffold strand. The nanosensor array, consisting of 11 nanoassemblies, termed nanosensors, is prepared by complexing graphene oxide nanosheets (nGO) with 11 fluorophore-labeled single-stranded DNA probes. Upon complexation, the fluorescence of the DNA probes is quenched through graphene oxide-mediated fluorescence quenching. Adding the DNA nanostructures to each nanosensor displaced a fraction of the surface-adsorbed fluorescent DNA probes, producing unique fluorescence recovery signatures that were subsequently processed through feature engineering for accurate ML-assisted classification. Using this approach, we achieved 94% prediction accuracy in discriminating DNA origami triangle, DNA origami nanotube, and unassembled M13 scaffold. Our strategy provides a new and generalizable platform for shape sorting in DNA origami field, offering new avenues for high-throughput, label-free classification.