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• W2897812916 abstract "Interrelated reaction networks steered by multiple types of enzymes are among the most intriguing enzyme-based cellular features. These reaction networks display advanced features such as adaptation, stimuli-responsiveness, and decision-making in accordance with environmental cues. However, artificial enzyme particles are still deficient in network-level capabilities, mostly because delicate enzymes are difficult to immobilize and assemble. In this study, we propose a general strategy to prepare enzyme-based particles that demonstrate network reaction capability. We assembled multiple types of proteins with a nanoscopic binder prepared from polyelectrolyte and graphene. After assembly, the enzymes all preserved their catalytic capabilities. By incorporating multiple types of enzymes, the particles additionally displayed network-reaction capabilities. We were able to use NIR irradiations to quasi-reversibly adjust the catalytic abilities of these enzyme-based particles. In addition, after a biomimetic mineralization process was used to wrap the protein complexes in a MOF shell, the particles were more robust and catalytically active even after being immersed in acidic (pH 4) or basic (pH 10) solutions for 3 days. This study provides an insight into the study of network properties of functional enzyme particles experimentally and enriches scientific understanding of multifunctional or stimuli-responsive behaviors at the reaction network level. The building of artificial reaction networks possesses high potential in realizing intelligent microparticles that can perform complicated tasks." @default.
• W2897812916 created "2018-10-26" @default.
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• W2897812916 date "2018-10-18" @default.
• W2897812916 modified "2023-10-16" @default.
• W2897812916 title "Multiple-Enzyme Graphene Microparticle Presenting Adaptive Chemical Network Capabilities" @default.
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• W2897812916 doi "https://doi.org/10.1021/acsami.8b13183" @default.
• W2897812916 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30336666" @default.
• W2897812916 hasPublicationYear "2018" @default.
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