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Updated: Jan 6, 2026

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics
Published on: June 23, 2022
The APEXTAC System for Ligand-Guided Proximity Labeling.
Min Ma1, YiYu Wang1, Johannes E Koeckenberger1
1Department of Chemistry and Biochemistry, University of California San Diego, San Diego, CA, 92093, USA.
The new APEXTAC system enables unbiased drug-target identification in live cells. It overcomes BioTAC limitations, offering faster labeling and improved accuracy for protein homeostasis research.
Area of Science:
- Chemical Biology
- Molecular Biology
- Drug Discovery
Background:
- Unbiased drug-target identification is crucial for understanding drug mechanisms and off-target effects.
- Existing methods like BioTAC have limitations, including specific chemistry and labeling duration.
- The need for improved proximity labeling tools in live-cell studies is evident.
Purpose of the Study:
- To develop and validate the APEXTAC system, a novel small molecule-guided proximity labeling platform.
- To compare APEXTAC head-to-head with the BioTAC system for drug-target identification.
- To assess APEXTAC's utility in studying protein homeostasis and targeted protein degradation.
Main Methods:
- Development of the APEXTAC system utilizing the APEX2 peroxidase enzyme.
- Head-to-head comparison of APEXTAC and BioTAC using (+)-JQ1 for target identification.
- Assessment of labeling efficiency and specificity in live cells.
Main Results:
- APEXTAC successfully labeled E3-ligases via their ligands without proteasome inhibitors.
- The APEXTAC system demonstrated faster and potentially more accurate target identification compared to BioTAC.
- APEXTAC proved effective for live-cell target identification across various molecules, including protein homeostasis components.
Conclusions:
- APEXTAC is a valuable complementary tool for unbiased live-cell drug-target identification.
- The system overcomes key limitations of previous proximity labeling methods.
- APEXTAC shows significant potential for applications in targeted protein degradation research.
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