Substrate specificity determines protease functions in physiology and in clinical and biotechnological applications, yet quantitative cleavage information is often unavailable, biased, or limited to a small number of events. Here, we develop qPISA (quantitative Protease specificity Inference from Substrate Analysis) to study Dipeptidyl Peptidase Four (DPP4), a key regulator of blood glucose levels. We use mass spectrometry to quantify >40,000 peptides from a complex, commercially available peptide mixture. By analyzing changes in substrate levels quantitatively instead of focusing on qualitative product identification through a binary classifier, we can reveal cooperative interactions within DPP4's active pocket and derive a sequence motif that predicts activity quantitatively. qPISA distinguishes DPP4 from the related C. elegans DPF-3 (a DPP8/9-orthologue), and we relate the differences to the structural features of the two enzymes. We demonstrate that qPISA can direct protein engineering efforts like the stabilization of GLP-1, a key DPP4 substrate used in the treatment of diabetes and obesity. Thus, qPISA offers a versatile approach for profiling protease and especially exopeptidase specificity, facilitating insight into enzyme mechanisms and biotechnological and clinical applications.
Keywords: Cryo-EM; DPP-IV Family Proteases; Peptide Turnover; Protein Engineering; Quantitative Modeling.
© 2024. The Author(s).