Dataset Information

Employing Expression-Matched Controls Enables High Confidence Proximity-Based Interactome Classification (Dataset 6)

ABSTRACT: Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high confidence interactome maps. This dataset contains proximity labeling proteomics results, including the proximity labeling section of TurboID-HUWE1 in the 293T cell line. It serves as the raw data for Figure 5 in the article.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Fulin Jiang

LAB HEAD: Eric Bennett

PROVIDER: PXD061540 | Pride | 2025-07-14

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	10_293T_TID_GFP_4h_Replicate1.d.zip	Other
	11_293T_TID_GFP_4h_Replicate2.d.zip	Other
	12_293T_TID_GFP_4h_Replicate3.d.zip	Other
	13_293T_TID_GFP_6h_Replicate1.d.zip	Other
	14_293T_TID_GFP_6h_Replicate2.d.zip	Other

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Publications

Employing Expression-Matched Controls Enables High-Confidence Proximity-Based Interactome Classification.

Jiang Fulin F Ge Xuezhen X Bennett Eric J EJ

Molecular & cellular proteomics : MCP 20250527 7

Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high-confidence interactome maps. We demonstrate that ...[more]

PMID: 40441440

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Project description:Classical methods of investigating protein-protein interactions (PPIs) are generally performed in non-living systems, yet in recent years new technologies utilizing proximity labeling (PL) have given researchers the tools to explore proximal PPIs in living systems. PL has distinct advantages over traditional protein interactome studies, such as the ability to identify weak and transient interactions in vitro and in vivo. Most PL studies are performed on targets within or on the cell membrane. We have adapted the original PL method to investigate PPIs within the extracellular compartment, using both BioID2 and TurboID, that we term extracellular PL (ePL). To demonstrate the utility of this modified technique, we investigate the interactome of the widely expressed matrisome protein Tissue inhibitors of metalloproteinases 2 (TIMP2). Tissue inhibitors of metalloproteinases (TIMPs) are a family of multi-functional proteins that were initially defined by their ability to inhibit the enzymatic activity of metalloproteinases (MPs), the major mediators of extracellular matrix (ECM) breakdown and turnover. TIMP2 exhibits a broad expression profile and is often abundant in both normal and diseased tissues. Understanding the functional transformation of matrisome regulators, like TIMP2, during the evolution of tissue microenvironments associated with disease progression is essential for the development of ECM targeted therapeutics. Using carboxyl- and amino-terminal fusion proteins of TIMP2 with BioID2 and TurboID, we describe the TIMP2 proximal interactome. We also illustrate how the TIMP2 interactome changes in the presence of different stimuli, in different cell types, in unique culture conditions (2D vs 3D), and with different reaction kinetics (BioID2 vs. TurboID); demonstrating the power of this technique versus classical PPI methods. We propose that the screening of matrisome targets in disease models using ePL will reveal new therapeutic targets for further comprehensive studies.