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 proximity labeling sections of TurboID-RNF10 based on the Flp-IN system and TurboID-HUWE1 based on the PiggyBac system. It serves as the raw data for Figures 3 and 5 in the article, as well as Supplementary Figures 3 and 5.