Back

Macropinocytosis-Mediated Recyclable LYTACs (McR-TACs) for Membrane and Extracellular Protein Degradation

Liu, P.; Li, Y.; You, Y.; Chen, Y.; Cai, M.; Hu, Q.

2025-09-10 biochemistry
10.1101/2025.09.08.674971 bioRxiv
Show abstract

The degradation of cell membrane and extracellular proteins with lysosome-targeting chimeras (LYTACs) is hampered by non-recyclable and lysosome-shuttling receptor-dependent mechanisms, limiting the wide application of this emerging technology. Here, we developed macropinocytosis-mediated recyclable LYTACs (McR-TACs) that could durably degrade cell membrane and extracellular proteins in a receptor-independent manner. This McR-TAC platform demonstrated the feasibility of harnessing endogenous transport pathways for developing next-generation recyclable protein degraders with broad applicability.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 6%
22.4%
Similar papers in this journal
2
Nature Chemical Biology
○ 119 papers in training set
Top 0.1%
18.8%
Similar papers in this journal
3
Journal of the American Chemical Society
● 217 papers in training set
Top 0.7%
5.3%
Similar papers in this journal
4
ACS Central Science
● 71 papers in training set
Top 0.2%
4.4%
Similar papers in this journal
50% of probability mass above
5
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 14%
3.6%
Similar papers in this journal
6
Science Advances
● 1243 papers in training set
Top 10%
3.3%
7
Advanced Science
○ 286 papers in training set
Top 2%
3.3%
8
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.6%
2.2%
9
Cell Chemical Biology
○ 94 papers in training set
Top 0.5%
2.2%
10
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.7%
2.2%
11
Science China Life Sciences
○ 29 papers in training set
Top 0.2%
1.8%
12
Chemical Science
● 73 papers in training set
Top 0.9%
1.8%
13
Nature Chemistry
○ 42 papers in training set
Top 0.4%
1.8%
14
ACS Chemical Biology
● 167 papers in training set
Top 1%
1.7%
15
Advanced Materials
○ 56 papers in training set
Top 0.6%
1.7%
16
eLife
● 5828 papers in training set
Top 56%
1.4%
17
Communications Biology
○ 993 papers in training set
Top 20%
1.1%
18
Cell Research
○ 51 papers in training set
Top 0.8%
1.1%
19
Cell
○ 431 papers in training set
Top 8%
1.1%
20
Cell Reports
○ 1498 papers in training set
Top 26%
0.9%
21
Nature Biotechnology
○ 172 papers in training set
Top 4%
0.9%
22
Angewandte Chemie
○ 15 papers in training set
Top 0.2%
0.9%
23
iScience
○ 1154 papers in training set
Top 34%
0.9%
24
Nature Nanotechnology
○ 32 papers in training set
Top 0.6%
0.9%
25
ACS Nano
● 113 papers in training set
Top 2%
0.6%
26
Science
● 477 papers in training set
Top 10%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.