Protein-protein interactions (PPIs) are fundamental to life processes. Precisely deciphering these interactions is crucial for understanding disease mechanisms and developing corresponding therapeutics. Although affinity purification-mass spectrometry (AP-MS) has become a common method for studying PPIs, it has significant limitations in detecting weak, transient interactions and membrane protein complexes. Proximity labeling techniques (e.g., BioID, APEX) can capture transient interactions but typically require fusion with relatively large labeling enzymes (20–50 kDa), which may interfere with normal protein function and localization. Their operational procedures are also complex, and their applicability is limited.

On September 15, 2025, a collaborative team led by Professor Sheng-Ce Tao from Shanghai Center for Systems Biomedicine at Shanghai Jiao Tong University, together with Dr. He-Wei Jiang from the LinGang Laboratory published a research paper titled "Sensitive and specific affinity purification-mass spectrometry assisted by PafA-mediated proximity labeling" in Cell Reports Methods. This study developed a novel proteomics method—APPLE-MS (Affinity Purification coupled Proximity LabEling-Mass Spectrometry)—which successfully integrates the high-specificity enrichment capability of the Twin-Strep tag with PafA-mediated proximity labeling. This enables the efficient and highly sensitive capture of both stable and transient protein interactions within a single workflow.

APPLE-MS ingeniously utilizes the PafA enzyme from the prokaryotic ubiquitin-like protein (Pup) labeling system. This enzyme catalyzes the covalent labeling of neighboring proteins with PupE. Subsequently, the technology leverages the high affinity between the Twin-Strep tag and streptavidin for efficient enrichment, allowing the capture and MS identification of PPIs in a single streamlined process. This method is not only operationally simple but also effectively captures weak interactions (with binding affinity up to 76 μM) and membrane protein complexes that are challenging for traditional AP-MS, all within the native cellular environment. Its modular design allows stable capture of transient interactions via covalent linkage under stringent wash conditions, while minimizing interference with the “bait” protein's function, significantly improving data reproducibility and signal-to-noise ratio.

The research team validated the superior performance of APPLE-MS across multiple model systems. In studying the interactome of the SARS-CoV-2 ORF9b protein, APPLE-MS successfully identified 138 high-confidence interacting proteins, 4.07 times more than traditional AP-MS, with a markedly improved signal-to-noise ratio. Further time-resolved analysis revealed ORF9b's dynamic regulatory role in mitochondrial metabolism and immune pathways during antiviral response. This included changing interactions with key components of the RIG-I/MAVS signaling pathway (such as RNF123 and TUFM), and the protein's strategy of reprogramming host metabolism by targeting the TCA cycle and oxidative phosphorylation complexes at different infection stages. These findings provide deep insights into the molecular mechanisms of ORF9b as a multifunctional virulence hub.

Furthermore, using CRISPR-Cas9 to introduce the Twin-Strep tag into the endogenous PIN1 gene, the team discovered novel functions of PIN1 in DNA replication and non-coding RNA processing, including interactions with replisome components like the MCM complex and RFC1. These results expand our understanding of PIN1's role in maintaining genome stability and cell cycle regulation, and demonstrate APPLE-MS's capability for studying endogenous protein interactions under physiologically relevant conditions.

Notably, APPLE-MS was successfully applied to study the interactions of the membrane protein GLP-1 receptor. By performing proximity labeling *in situ* at the cell surface, the method captured interaction events in the native membrane environment without requiring cell lysis. Researchers identified 301 potential interacting proteins in INS-1E cells, including several known and novel co-receptors and signaling components, such as ATPase subunits and transporter complexes. These findings offer new perspectives for understanding GLP-1's role in blood glucose regulation and other physiological functions, highlighting APPLE-MS's broad application potential in GPCR signaling network research.

In summary, the APPLE-MS technology combines the high specificity of AP-MS with the high sensitivity of proximity labeling, making it particularly suitable for studying membrane-associated complexes and dynamic interaction networks. Its small tag design and universal enzyme system allow broad application across various cell types, supporting both endogenous tagging and multiplexed analysis strategies. This technology provides a powerful tool for deciphering disease mechanisms and discovering new drug targets, promising to play a significant role in diverse biological scenarios in the future.

Dr. He-Wei Jiang , Team Leader at LinGang Laboratory, and Professor Sheng-Ce Tao from Shanghai Jiao Tong University are the co-corresponding authors of the paper. Joint Master's student Shi-Han Luo (Shanghai Jiao Tong University & LinGang Laboratory), Research Assistant Li-Juan Xie (LinGang Laboratory), Ph.D. candidate Lin Yang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences), and Postdoctoral Fellow Zhe-Yao Hu (Shanghai Jiao Tong University) are the co-first authors. Shanghai Center for Systems Biomedicine at Shanghai Jiao Tong University is the primary affiliation. This research was supported by grants from the Natural Science Foundation of China (No. 92374110 and 32271492) and the Shanghai Jiao Tong University Medical-Engineering Interdisciplinary Research Fund (2023-2025), among others.