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Cited by 499

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Enzyme Mimicry for Combating Bacteria and Biofilms

Chen, Zhaowei, Wang, Zhenzhen, Ren, Jinsong, Qu, Xiaogang·2018·Accounts of Chemical Research

Abstract

Conspectus Bacterial infection continues to be a growing global health problem with the most widely accepted treatment paradigms restricted to antibiotics. However, antibiotics overuse and misuse have triggered increased multidrug resistance, frustrating the therapeutic outcomes and leading to higher mortalities. Even worse, the tendency of bacteria to form biofilms on living and nonliving surfaces further increases the difficulty in confronting bacteria because the extracellular matrix can act as a robust barrier to prevent the penetration of antibiotics and resist environmental stress. As a result, the inability to completely eliminate bacteria and biofilms often leads to persistent infection, implant failure, and device damage. Therefore, it is of paramount importance to develop alternative antimicrobial agents while avoiding the generation of bacterial resistance. Taking lessons from natural enzymes for destroying cellular structural integrity or interfering with metabolisms such as proliferation, quorum sensing, and programmed death, the construction of artificial enzymes to mimic the enzyme functions will provide unprecedented opportunities for combating bacteria. Moreover, compared to natural enzymes, artificial enzymes possess much higher stability against stringent conditions, easier tunable catalytic activity, and large-scale production for practical use. In this Account, we will focus on our recent progress in the design and synthesis of artificial enzymes as a new generation of “antibiotics”, which have been demonstrated as promising applications in planktonic bacteria inactivation, wound/lung disinfection, as well as biofilm inhibition and dispersion. First, we will introduce direct utilization of the intrinsic catalytic activities of artificial enzymes without dangerous chemical auxiliaries for killing bacteria under mild conditions. Second, to avoid the toxicity caused by overdose of H 2 O 2 in conventional disinfections, we leveraged artificial enzymes with peroxidase-mimic activities to catalyze the generation of hydroxyl radicals at low H 2 O 2 levels while achieving efficient antibacterial outcomes. Importantly, the feasibility of these artificial enzymes was further demonstrated in vivo by mitigating mice wound and lung disinfection. Third, by combining artificial enzymes with stimuli-responsive materials, smart on-demand therapeutic modalities were constructed for thwarting bacteria in a controllable manner. For instance, a photoswitchable “Band-Aid”-like hydrogel doped with artificial enzymes was developed for efficiently killing bacteria without compromising mammal cell proliferation, which was promising for accelerating wound healing. Lastly, regarding the key roles that extracellular DNAs (eDNAs) play in maintaining biofilm integrity, we further designed a multinuclear metal complex-based DNase-mimetic artificial enzyme toward cleaving the eDNA for inhibiting biofilm formation and dispersing the established biofilms. We expect that our rational designs would boost the development of artificial enzymes with different formulations as novel antibacterial agents for clinical and industrial applications.

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Metadata

Authors
Chen, Z., Wang, Z., Ren, J., & Qu, X.
Venue
Accounts of Chemical Research
Published
2018-02-28
Volume
51
Issue
3
Pages
789-799
ISSN
  • 0001-4842 (ISSN-L)
  • 1520-4898
OpenAlex
W2787901343
Cited by
499
References
52
FWCI
16.36
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Enzyme Mimicry for Combating Bacteria and Biofilms. https://app.cochat.ai/papers/10.1021/acs.accounts.8b00011
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Chen, Z., Wang, Z., Ren, J., & Qu, X. (2018). Enzyme Mimicry for Combating Bacteria and Biofilms. Accounts of Chemical Research. https://doi.org/10.1021/acs.accounts.8b00011

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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