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MedComm
Article . 2024 . Peer-reviewed
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Article . 2024
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MedComm
Article . 2024
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mRNA PROTACs: engineering PROTACs for high‐efficiency targeted protein degradation

Authors: Xiaoqi Xue; Chen Zhang; Xiaolin Li; Junqiao Wang; Haowei Zhang; Ying Feng; Naihan Xu; +4 Authors

mRNA PROTACs: engineering PROTACs for high‐efficiency targeted protein degradation

Abstract

AbstractProteolysis‐targeting chimeras (PROTACs) are essential bifunctional molecules that target proteins of interest (POIs) for degradation by cellular ubiquitination machinery. Despite significant progress made in understanding PROTACs' functions, their therapeutic potential remains largely untapped. As a result of the success of highly flexible, scalable, and low‐cost mRNA therapies, as well as the advantages of the first generation of peptide PROTACs (p‐PROTACs), we present for the first time an engineering mRNA PROTACs (m‐PROTACs) strategy. This design combines von Hippel–Lindau (VHL) recruiting peptide encoding mRNA and POI‐binding peptide encoding mRNA to form m‐PROTAC and promote cellular POI degradation. We then performed proof‐of‐concept experiments using two m‐PROTACs targeting two cancer‐related proteins, estrogen receptor alpha and B‐cell lymphoma‐extra large protein. Our results demonstrated that m‐PROTACs could successfully degrade the POIs in different cell lines and more effectively inhibit cell proliferation than the traditional p‐PROTACs. Moreover, the in vivo experiment demonstrated that m‐PROTAC led to significant tumor regression in the 4T1 mouse xenograft model. This finding highlights the enormous potential of m‐PROTAC as a promising approach for targeted protein degradation therapy.

Related Organizations
Keywords

in vitro transcribed mRNA (IVT‐mRNA), mRNA, R, Medicine, Original Articles, peptide proteolysis‐argeting chimeras, Targeted protein degradation (TPD), proteolysis‐argeting chimeras (PROTAC)

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
Green
gold
Related to Research communities
Cancer Research