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

    Journal: Adv. Mater., Volume 34, JUL

    Authors: Tamasi, Matthew J.; Patel, Roshan A.; Borca, Carlos H.; Kosuri, Shashank; Mugnier, Heloise; Upadhya, Rahul; Murthy, N. Sanjeeva; Webb, Michael A.; Gormley, Adam J.

    Organizations: National Institutes of Health (NIH) under NIGMS MIRA Award [R35GM138296]; National Science Foundation under DMREF Award [NSF-DMR-2118860, NSF-DMR-2118861]; National Science Foundation under CBET Award [NSF-ENG-2009942]; Princeton University; National Institutes of Health [GM135141]; National Institutes of Health, National Institute of General Medical Sciences (NIGMS) [P30GM133893]; DOE Office of Biological and Environmental Research [KP1605010]; NIH [S10 OD012331]; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Program [DE-SC0012704]

    Keywords: active learning; Bayesian optimization; combinatorial polymer design; machine learning; polymer-protein conjugates; protein formulations; single-enzyme nanoparticles

    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 the vast chemical and composition space. Here, a strategy is reported 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 and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.
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