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

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Article number2201809
JournalAdvanced Materials
Volume34
Issue number30
DOIs
StatePublished - Jul 27 2022

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

Keywords

  • Bayesian optimization
  • active learning
  • combinatorial polymer design
  • machine learning
  • polymer–protein conjugates
  • protein formulations
  • single-enzyme nanoparticles

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