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231226s2022 xx |||||o 00| ||eng c |
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7 |
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|a 10.1002/adma.202201809
|2 doi
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|a pubmed24n1315.xml
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|a (DE-627)NLM341161691
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|a (NLM)35593444
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Tamasi, Matthew J
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 28.07.2022
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|a Date Revised 03.03.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.
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|a 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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|a Journal Article
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|a Bayesian optimization
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| 650 |
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4 |
|a active learning
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4 |
|a combinatorial polymer design
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| 650 |
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4 |
|a machine learning
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4 |
|a polymer-protein conjugates
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| 650 |
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4 |
|a protein formulations
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|a single-enzyme nanoparticles
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|a Polymers
|2 NLM
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| 650 |
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|a Proteins
|2 NLM
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| 700 |
1 |
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|a Patel, Roshan A
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Borca, Carlos H
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kosuri, Shashank
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mugnier, Heloise
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Upadhya, Rahul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Murthy, N Sanjeeva
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Webb, Michael A
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gormley, Adam J
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 30 vom: 02. Juli, Seite e2201809
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
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|g volume:34
|g year:2022
|g number:30
|g day:02
|g month:07
|g pages:e2201809
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|u http://dx.doi.org/10.1002/adma.202201809
|3 Volltext
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