Algorithm-driven DNA nanostructure design for advanced functionality

Form and function are intimately linked—a guiding principle across both biology and engineering. While evolution and human ingenuity have shaped this landscape through function-driven form selection, AI-driven generative design expands it beyond natural and intuitive geometries, enabling the exploration of structure-driven functionalities that were previously inaccessible. At the nanoscale, however, realizing such non-intuitive architectures remains a key challenge, limiting the development of multifunctional nanostructures across biology, chemistry, and materials science. To address these constraints, we introduce an algorithmic design paradigm that enables DNA helices to follow non-planar 3D trajectories, thereby supporting the structural and functional outcomes required for advanced capabilities. We implemented this paradigm in ENSnano, an open-source software platform that integrates mathematical models to automate structural design in 3D space without the need of human intervention. This framework allows us to rapidly generate DNA nanostructures with key functional features such as curvature, encapsulation, and hierarchical organization—reminiscent of naturally occurring biological architectures. As a key application, we demonstrate an automated pathway from biological to engineered structures by designing and experimentally assembling Vault-like cages directly derived from the emPDB model of the Vault protein, marking a step forward in biomimetic DNA nanostructure design.