Employing Expression-Matched Controls Enables High Confidence Proximity-Based Interactome Classification (Dataset 2)

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 whole-cell extract proteomics results, including the TurboID quality control selection section, the TurboID-GFP expression section, and the RNF10 proximity labeling section. It serves as the raw data for Figures 1, 2, and 4 in the article, as well as Supplementary Figures 1, 2, and 4.