Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids

Abstract

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. Here, we report a 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 three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique chemistries for each enzyme. Overall, this work broadens our capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions towards the design of robust polymer-protein hybrid materials.