Programmable supramolecular nanoarchitectures for targeting biological patterns

Intricate spatial organization is omnipresent in biological systems across molecular, cellular, and tissue scales and has significant implications for health and disease. Biological patterns arise from complex molecular interactions and gradients, which, respectively, determine the structure of protein complexes and tissues. These biological patterns not only shape the fundamental architecture of life but also influence pathogen evolution and host-pathogen interactions. The emergence of new pathogens and the rising financial burden of existing global health challenges drive the need for innovative therapeutic, diagnostic, and drug-testing solutions that extend beyond traditional molecular platforms like small molecules, peptides, and antibodies. This thesis focuses on the application of programmable functionalized nucleic acid architectures for targeting viral glycoproteins (nanoscale patterns) as well as for the construction of morphogen gradients within a hydrogel (microscale patterns). These two directions ultimately aim to contribute to the development of novel geometry-tailored multivalent targeting and sensing nanomaterials and convenient micropatterning platform for tissue engineering.

We started by investigating how ligand presentation patterns influence low-valency interactions using a testbed DNA origami nanostructure (Chapter 2). This system allowed us to deconvolute ligand's affinity, valency, and spatial configuration parameters and discover a super-selective binding regime, where nanostructures with identical molecular composition and ligand valency exhibit different binding behavior solely based on their ligand presentation geometry.

Budling upon our findings, we applied this concept of multivalent pattern recognition towards directed evolution of binders (Chapter 3). By incorporating a target-tailored multimerization scaffold into the selection of synthetic polymers, we were able to produce novel multivalent binding agents, structurally and functionally distinct to any known binding modalities. We called this molecular platform Multivalent Evolved DNA-based SUpramolecular Assemblies (MEDUSA). We further expanded the potential field of applications for MEDUSA by engineering a proof-of-principle molecular sensor for viral detection.

Next, we leveraged data from the selection of the MEDUSA library to train a machine learning model (Chapter 4). By analyzing structure-activity relationships, we showcased the model's ability to capture key sequence and structural features that govern binding interactions.

Finally, we shift our focus to the development of a programmable hydrogel micropatterning system for spatially and temporally controlled molecular release (Chapter 5). By integrating a programmable DNA-based exchange mechanism with light-controlled molecular release, we developed a simple and scalable platform for constructing morphogen patterns within hydrogels.

Overall, this work demonstrates how programmable nucleic acid arch