Collaborative Research: DMREF: Machine Learning and Robotics for the Data-Driven Design of Protein-polymer Hybrid Materials

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Non-technical Description: Proteins are widely employed as agents for disease therapy and diagnosis, as well as catalytic components of commercial and industrial processes. In almost all applications, polymers are used as stabilizing constituents to increase durability of proteins in harsh and foreign environments, but the vast majority of stabilizing polymers provide modest protection due to non-specific interactions with the protein surface. Moreover, the surface properties of proteins used in these formulations are the result of evolution for working in mild, biological environments as opposed to the desired harsh, abiological ones. In principle, complex protein-polymer hybrids with tailored chemistries would facilitate superior protection by tightly wrapping polymer around the protein based on engineered complementary interactions. Such tailored formulations would stabilize the protein in its native state even under remarkably harsh conditions and have tremendous value in myriad industrial and military applications. However, these new materials are extraordinarily difficult to design due to their complexity. To address this challenge, this project will combine machine learning (ML) with robotics to rapidly discover new protein-polymer hybrid materials using data analytics and optimization tools. Over time, aggregated data will be used to train advanced ML models that can be applied to the prediction and design of functionality in a wide variety of novel materials. Equally important, this research will focus on the cross-disciplinary training of young data material scientists who will be prepared to enter the workforce and help revolutionize and engineer future materials. This research will feature a large collaborative effort between Rutgers University, Princeton University, and the Air Force Research Laboratory (AFRL).Technical Description: Current approaches to designing complementary protein-polymer interactions rely on labor intensive trial-and-error experimentation due to the lack a generalizable physicochemical framework that can guide the simultaneous design of both polymer and protein constituents at multiple length scales. This research will implement a novel machine learning-driven, bottom-up materials engineering paradigm for the design of protein-polymer hybrid particles; these hybrid particles will then be organized into protein-polymer hybrid assemblies to enhance stability in abiological environments. High-throughput polymer and protein production, characterization, multi-scale molecular simulation, and machine learning will be combined in a closed-loop fashion to both discover novel protein-polymer compositions and understand the physicochemical drivers for enhanced stability. Using these iterative Design-Build-Test-Learn cycles, underlying design principles for generating robust protein-polymer hybrids will be ascertained and the lead time for designing tailor-made protein-polymer hybrid materials will be substantially shortened.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.