G protein-coupled receptors (GPCRs) regulate many aspects of physiology and represent actionable targets for drug discovery. Activation of specific signaling pathways downstream of G-protein coupled receptors (GPCRs) or targeting the receptors at selected cellular locations has the potential to provide therapeutic actions with fewer side effects. However, the understanding of the molecular mechanisms underlying GPCR function is limited in the dynamic cellular environment, hampering drug discovery efforts towards selective ligands. Proximity biotin labeling based on an engineered ascorbic acid peroxidase (APEX) combined with quantitative mass spectrometry is a powerful method to delineate these mechanisms given its capacity to simultaneously capture proximal protein interaction networks and the cellular location of the receptor. However, a major challenge is to extract the various information from these complex datasets. Here, we describe a computational framework for proximity labeling datasets which predicts ligand-dependent subcellular location of GPCRs and quantitatively deconvolutes the effect of receptor location and proximal interactors. We applied this approach to the mu-opioid receptor and not only monitored distinct effects of ligands on receptor trafficking, but also discovered two novel regulators, EYA4 and KCTD12, which modulate MOR-driven G protein-dependent signaling.