Biomacromolecular engineering via high-throughput experimentation for protein-based materials

Biomacromolecular materials are inherently complex and characterized by their chemistry, molecular weight distribution, monomer segment distribution, and architecture; together, the local design dictates the macroscopic and microscopic properties of the polymer of interest. To gain understanding and design rules for these complicated macromolecules, it is essential to move toward more high-throughput data generation and data-driven learning to probe how molecular design affect key physical properties. Protein-based systems are traditionally much more controlled, as each protein can be produced with perfect sequence definition, allowing for highly ordered intramolecular structures, but selecting appropriate sequences remains an open challenge. Bringing together the complexity of polymer-based systems with the well-developed biosynthetic toolbox will allow for progress in both areas. This talk will focus on the development of high-throughput tools to generate protein-based materials, which can be rapidly tested and iterated to affect a number of application spaces, spanning biomaterials, plastic recycling, and structural materials. By harnessing a combination of systematic materials design improvements, the connection of macroscopic properties and fundamental polymer physics principles will be realized with an end goal to systematically control polymer end-of-life.