(218d) Data-Driven Design of Polymer-Protein Hybrid Materials

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by a vast chemical and composition space. In this talk, I will describe our strategy to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of chemically distinct enzymes following exposure to thermal denaturing conditions. Using post hoc analysis, molecular modeling, and additional experimental characterization, I will discuss likely reasons underlying the success of these polymer-protein combinations. Overall, this demonstrates a useful application of data science to an otherwise complex design problem, enabling the study of interesting systems that we would not be in a position to identify relying on more conventional methods.