The ubiquitin-proteasome system plays a critical role in biology by regulating protein degradation. Despite their importance, precise recognition specificity is known for a few of the 600 E3s. Here, we establish a two-pronged strategy for identifying and mapping critical residues of internal degrons on a proteome-scale in HEK-293T cells. We employ global protein stability profiling combined with machine learning to identify 15,800 peptides likely to contain sequence-dependent degrons. We combine this with scanning mutagenesis to define critical residues for over 5,000 predicted degrons. Focusing on Cullin-RING ligase degrons, we generated mutational fingerprints for 219 degrons and developed DegronID, a computational algorithm enabling the clustering of degron peptides with similar motifs. CRISPR analysis enabled the discovery of E3-degron pairs, of which we uncovered 16 pairs that revealed extensive degron variability and structural determinants. We provide the visualization of these data on the public DegronID data browser as a resource for future exploration. Display omitted •Proteome-wide global protein stability (GPS) assay identifies 15,800 degron peptides•Critical degron residues were mapped by scanning and saturation mutagenesis•CRISPR screening reveals cognate E3 ligases for 16 distinct degrons•E3-degron pairs were docked by Alphafold2 and validated by co-immunoprecipitation Zhang et al. combined a proteome-wide screen with machine learning to identify thousands of short linear motifs (degrons) in human proteins that are associated with sequence-dependent protein degradation. They went on to map critical residues, identify interacting ligases, and validate binding for a diverse collection of internal degrons.