Isothermal self-assembly of multicomponent and evolutive DNA nanostructures

Self-assembly is both an advantageously spontaneous process to organize molecular or colloidal entities into synthetic functional superstructures and a key-feature of how life builds its components. However, compared to their living counterparts, synthetic materials made by self-assembly usually lack some of the interesting properties of living systems such as multicomponent character or capability to adapt, transform and evolve. Here we describe an isothermal multicomponent DNA self-assembly method that recapitulates all these characteristics and leads to user-defined objects with programmable shape, site-specific function, intrinsic reconfigurability and unprecedented capacity of major transformation and shape evolution. Using a generic magnesium-free buffer containing NaCl, we show that a complex cocktail of hundreds of different DNA strands can spontaneously assemble at room or body temperature to form desired DNA origamis of various shapes with site-specific protein functionalization, extended nanogrids or single-strand tile-assemblies. In situ atomic force microscopy allows us to follow the self-assembly process and demonstrate that DNA origami assembly proceeds through multiple folding pathways, the system escaping kinetic traps until it reaches its equilibrium target structure. We also show that, under thermodynamic control, this method allows a given system to self-select its most stable shape in a large pool of competitive DNA strands. We finally demonstrate the first giant morphological transformation of DNA origamis spontaneously evolving at constant temperature from one shape to a radically different one by the massive exchange of all its constitutive staple strands. This method greatly expands the repertoire of shapes and functions attainable by isothermal self-assembly as well as provides tools to design evolutive DNA assemblies, with applications ranging from directed or self-adaptive morphological changes to nanostructure optimization by evolution.