Design of a mucin-selective protease for targeted degradation of cancer-associated mucins Article Swipe
Kayvon Pedram
,
D. Judy Shon
,
Gabrielle S. Tender
,
Natália Rodrigues Mantuano
,
Jason J. Northey
,
Kevin J. Metcalf
,
Simon Wisnovsky
,
Nicholas M. Riley
,
Giovanni C. Forcina
,
Stacy A. Malaker
,
Angel Kuo
,
Benson M. George
,
Caitlyn L. Miller
,
Kerriann M. Casey
,
José G. Vilches-Moure
,
Valerie M. Weaver
,
Heinz Läubli
,
Carolyn R. Bertozzi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.05.20.492748
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.05.20.492748
Targeted protein degradation is an emerging strategy for the elimination of classically undruggable proteins. Here, to expand the landscape of substrates that can be selectively degraded, we designed degraders which are dependent on both peptide sequence and glycosylation status of the target protein. We applied this approach to mucins, O-glycosylated proteins that drive cancer progression through biophysical and immunological mechanisms. Engineering of a bacterial mucin-selective protease yielded a variant for fusion to a cancer antigen-binding nanobody. The resulting conjugate selectively degraded mucins on cancer cells, promoted cell death in culture models of mucin-driven growth and survival, and reduced tumor growth in murine models of breast cancer progression. This work establishes a blueprint for the development of biologics which degrade specific glycoforms of cell surface proteins.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.05.20.492748
- https://www.biorxiv.org/content/biorxiv/early/2022/05/20/2022.05.20.492748.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281399495
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281399495Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.05.20.492748Digital Object Identifier
- Title
-
Design of a mucin-selective protease for targeted degradation of cancer-associated mucinsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-20Full publication date if available
- Authors
-
Kayvon Pedram, D. Judy Shon, Gabrielle S. Tender, Natália Rodrigues Mantuano, Jason J. Northey, Kevin J. Metcalf, Simon Wisnovsky, Nicholas M. Riley, Giovanni C. Forcina, Stacy A. Malaker, Angel Kuo, Benson M. George, Caitlyn L. Miller, Kerriann M. Casey, José G. Vilches-Moure, Valerie M. Weaver, Heinz Läubli, Carolyn R. BertozziList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.05.20.492748Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/05/20/2022.05.20.492748.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/05/20/2022.05.20.492748.full.pdfDirect OA link when available
- Concepts
-
Mucin, Glycosylation, Protease, MUC1, Fusion protein, Cancer cell, Biology, Chemistry, Cancer, Cell biology, Cancer research, Biochemistry, Recombinant DNA, Enzyme, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 4, 2022: 4Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5088409133 |
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| institutions_distinct_count | 18 |
| corresponding_institution_ids | https://openalex.org/I1344073410, https://openalex.org/I97018004 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Good health and well-being |
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| citation_normalized_percentile.is_in_top_10_percent | False |