Abstract Proximity labeling has emerged as a powerful approach for identifying protein–protein interaction networks within living systems, particularly those involving weak or transient associations. Here, we present a comprehensive revised proximity labeling workflow, integrating TurboID labeling of endogenously expressed fusion proteins and data-independent acquisition (DIA) mass spectrometry (MS). We benchmark this pipeline with a study of five conserved Caenorhabditis elegans proteins—NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4— that form two NEKL–MLT kinase–scaffold subcomplexes involved in membrane trafficking and actin regulation. Profiling of NEKL–MLT interactomes across 23 experiments validated our approach through the identification of known NEKL–MLT binding partners and conserved nekl–mlt genetic interactors, including the discovery of several novel functional interactors. Importantly, inclusion of methodological variations, stringent controls, and filtering strategies enhanced sensitivity and reproducibility, defining a set of intuitive quantitative metrics for routine assessment of experimental quality. We show that DIA-based interactome workflows produce physiologically relevant findings, even in the presence of experimental noise and variability across biological replicates. Our study underscores the utility of DIA mass spectrometry in proximity labeling applications and highlights the value of incorporating internal controls, quantitative metrics, and biological validation to enhance confidence in candidate interactors.
Fay et al. (2025) studied this question.