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Cited by 177Machine Learning in Materials ScienceEnzyme Catalysis and ImmobilizationPolymer Surface Interaction Studies

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

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Tamasi, Matthew J., Patel, Roshan A., Borca, Carlos H., Kosuri, Shashank, Mugnier, Heloise, Upadhya, Rahul, Murthy, N. Sanjeeva, Webb, Michael A. +1 more·2022·Advanced Materials
DOI 10.1002/adma.202201809PMC PMC9339531

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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.

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  • The application of data-driven methodologies to soft materials remains challenging because of complex, multiscale structure-property relationships.

    “However, their application to soft materials remains challenging because of complex, multiscale structure–property relationships4–6.”

    From Data-driven de novo design of super-adhesive hydrogels (2025) · cites this paper as [6]

    The Introduction (page not provided) says ML-guided copolymer design is limited, particularly by insufficient quality data and laborious synthesis and characterization. That supports the general point that applying data-driven methods to these materials is challenging, but it does not identify complex, multiscale structure–property relationships as the reason or establish the claim for soft materials generally. (LLM, conf=0.84)

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Authors
Tamasi, M. J., Patel, R. A., Borca, C. H., Kosuri, S., Mugnier, H., Upadhya, R., Murthy, N. S., Webb, M. A., & Gormley, A. J.
Affiliations
  • Rutgers, The State University of New Jersey (US)
  • Princeton University (US)
Corresponding author
  • Webb, Michael A.
  • Gormley, Adam J.
ORCID
Venue
Advanced Materials
Publisher
Wiley
Published
2022-06-11
Volume
34
Issue
30
Pages
e2201809-e2201809
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
DOI
10.1002/adma.202201809
PMC
PMC9339531
OpenAlex
W4280529368
Cited by
177
References
60
Citations by year
2026: 32 · 2025: 49 · 2024: 44 · 2023: 42 · 2022: 10
FWCI
10.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Advanced Materials (publishedVersion, cc-by-nc-nd)
  • ChemRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by-nc-nd)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • National Institute of General Medical Sciences (R35GM138296, T32GM135141)
  • National Science Foundation (2009942, 2118860, 2118861)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
  • Physical Sciences › Materials Science › Surfaces, Coatings and Films › Polymer Surface Interaction Studies
Keywords
protein stabilization, random copolymers, active learning (machine learning), machine learning, protein stability, copolymer design
MeSH terms
Machine Learning, Polymers, Proteins, Robotic Surgical Procedures

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