@article{df7502853f1c4647b25594facb970872,
title = "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 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.",
keywords = "Bayesian optimization, active learning, combinatorial polymer design, machine learning, polymer–protein conjugates, protein formulations, single-enzyme nanoparticles",
author = "Tamasi, {Matthew J.} and Patel, {Roshan A.} and Borca, {Carlos H.} and Shashank Kosuri and Heloise Mugnier and Rahul Upadhya and Murthy, {N. Sanjeeva} and Webb, {Michael A.} and Gormley, {Adam J.}",
note = "Funding Information: A.J.G. acknowledges support from the National Institutes of Health (NIH) under NIGMS MIRA Award R35GM138296, and the National Science Foundation under DMREF Award NSF‐DMR‐2118860 and CBET Award Number NSF‐ENG‐2009942. R.A.P., C.H.B., and M.A.W. acknowledge support from the National Science Foundation under DMREF Award Number NSF‐DMR‐2118861 as well as start‐up funds from Princeton University. M.J.T. acknowledges additional support from the National Institutes of Health (GM135141). The training and optimization with ML models was performed with resources from Princeton Research Computing at Princeton University, which is a consortium led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing. A.J.G. and N.S.M. acknowledge James Byrnes, beamline scientist at NSLS‐II beamline 16‐ID for Life Science X‐ray Scattering (LiX), for his assistance with conducting experiments at Brookhaven National Laboratory. The LiX beamline is part of the Center for BioMolecular Structure (CBMS), which is primarily supported by the National Institutes of Health, National Institute of General Medical Sciences (NIGMS) through a P30 Grant (P30GM133893), and by the DOE Office of Biological and Environmental Research (KP1605010). LiX also received additional support from NIH Grant S10 OD012331. As part of NSLS‐II, a national user facility at Brookhaven National Laboratory, work performed at the CBMS was supported in part by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Program under contract number DE‐SC0012704. Funding Information: A.J.G. acknowledges support from the National Institutes of Health (NIH) under NIGMS MIRA Award R35GM138296, and the National Science Foundation under DMREF Award NSF-DMR-2118860 and CBET Award Number NSF-ENG-2009942. R.A.P., C.H.B., and M.A.W. acknowledge support from the National Science Foundation under DMREF Award Number NSF-DMR-2118861 as well as start-up funds from Princeton University. M.J.T. acknowledges additional support from the National Institutes of Health (GM135141). The training and optimization with ML models was performed with resources from Princeton Research Computing at Princeton University, which is a consortium led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing. A.J.G. and N.S.M. acknowledge James Byrnes, beamline scientist at NSLS-II beamline 16-ID for Life Science X-ray Scattering (LiX), for his assistance with conducting experiments at Brookhaven National Laboratory. The LiX beamline is part of the Center for BioMolecular Structure (CBMS), which is primarily supported by the National Institutes of Health, National Institute of General Medical Sciences (NIGMS) through a P30 Grant (P30GM133893), and by the DOE Office of Biological and Environmental Research (KP1605010). LiX also received additional support from NIH Grant S10 OD012331. As part of NSLS-II, a national user facility at Brookhaven National Laboratory, work performed at the CBMS was supported in part by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Program under contract number DE-SC0012704. Publisher Copyright: {\textcopyright} 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.",
year = "2022",
month = jul,
day = "27",
doi = "10.1002/adma.202201809",
language = "English (US)",
volume = "34",
journal = "Advanced Materials",
issn = "0935-9648",
publisher = "Wiley-VCH Verlag",
number = "30",
}