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

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

doi:10.1002/adma.202201809

Title: Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids

Journal Article · 20 May 2022 · Advanced Materials

DOI: https://doi.org/10.1002/adma.202201809 · OSTI ID:1981354

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Rutgers Univ., Piscataway, NJ (United States)
Princeton Univ., NJ (United States)

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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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institute of General Medical Sciences (NIGMS); National Institutes of Health (NIH); National Science Foundation (NSF)

Grant/Contract Number:: SC0012704

OSTI ID:: 1981354

Journal Information:: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 30 Vol. 34; ISSN 0935-9648

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids

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