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.