Back

μMap-uAA: Photocatalytic proximity labeling targeted with single-residue precision

Kang, M. S.; Li, C.; Buksh, B. F.; Fernandez, D.; MacMillan, D. W. C.

2026-08-06 biochemistry
10.64898/2026.08.05.742576 bioRxiv
Show abstract

Mapping protein microenvironments with residue-level precision in living cells remains challenging. We report Map-uAA, a genetically encoded proximity labeling platform that uses unnatural amino acid incorporation to install a tetrazine-quenched iridium photocatalyst at user-defined protein sites through click chemistry. Photocatalysis is activated by covalent attachment to the incorporated uAA, enabling localized catalytic labeling of proximal biomolecules. Applied to membrane receptors, Map-uAA captures domain- and time-dependent GPCR interaction networks with residue-level spatial precision.

Matching journals

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

1
Nature Chemical Biology
○ 119 papers in training set
Top 0.1%
18.7%
Similar papers in this journal
2
Journal of the American Chemical Society
● 217 papers in training set
Top 0.2%
12.8%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 15%
12.0%
Similar papers in this journal
4
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.2%
6.8%
Similar papers in this journal
50% of probability mass above
5
Nature Chemistry
○ 42 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
6
ACS Central Science
● 71 papers in training set
Top 0.2%
4.4%
7
ACS Chemical Biology
● 167 papers in training set
Top 0.8%
3.5%
8
Advanced Science
○ 286 papers in training set
Top 3%
2.4%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 23%
2.1%
10
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.1%
11
Nature Methods
○ 385 papers in training set
Top 4%
1.9%
12
ChemBioChem
○ 55 papers in training set
Top 0.6%
1.7%
13
Nature Nanotechnology
○ 32 papers in training set
Top 0.3%
1.7%
14
Advanced Materials
○ 56 papers in training set
Top 0.7%
1.5%
15
Cell
○ 431 papers in training set
Top 7%
1.5%
16
Chemical Science
● 73 papers in training set
Top 1%
1.5%
17
Cell Chemical Biology
○ 94 papers in training set
Top 1%
1.1%
18
Nucleic Acids Research
◐ 1281 papers in training set
Top 12%
1.1%
19
Nature
○ 645 papers in training set
Top 10%
0.9%
20
RSC Chemical Biology
● 39 papers in training set
Top 0.7%
0.9%
21
JACS Au
● 43 papers in training set
Top 0.8%
0.9%
22
eLife
● 5828 papers in training set
Top 70%
0.6%
23
Angewandte Chemie
○ 15 papers in training set
Top 0.2%
0.6%
24
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.6%
25
ACS Nano
● 113 papers in training set
Top 2%
0.6%
26
Communications Chemistry
○ 48 papers in training set
Top 2%
0.6%
27
Science Advances
● 1243 papers in training set
Top 33%
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.