1 Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
2 Department of Chemistry and Biochemistry, Concordia University, Montreal, Quebec H4B 1R6, Canada.
3 Richardson Centre for Food Technology and Research, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.

Arginase is a binuclear manganese metalloenzyme that modulates L-arginine availability and downstream metabolic pathways. It was hypothesized that C-terminal arginine-containing peptides generated through simulated gastrointestinal digestion of white lupin (Lupinus albus) proteins would exhibit arginase inhibitory activity. To test this hypothesis, integrated computational and experimental approaches were employed to identify and characterize arginase-inhibitory peptides from white lupin proteins. Based on the REGNH peptide scaffold, 29 derivative sequences were designed and used to screen the lupin genome, followed by in silico proteolysis, yielding 24 C-terminal arginine-containing peptides. None of the peptides showed evidence of allergenicity. Target prediction identified DR, CR, ER, and QR as top candidates, while molecular docking and 100 ns molecular dynamics simulations revealed isoform-specific binding preferences and stability patterns. Five peptides (CQR, DR, DQR, QER, REGNH) were synthesized and evaluated against arginase I using an in vitro colorimetric assay. DQR showed the highest inhibitory activity among the tested peptides, achieving approximately 17% inhibition at 100 µg/mL. Kinetic analysis indicated an apparent mixed-type inhibition pattern, with increased Km (31.2 to 35.4 mM) and decreased Vmax (0.72 to 0.70 ?Abs/min). Despite a top computational ranking, DR showed negligible in vitro activity (<2%), highlighting the limitations of binding-affinity predictions for forecasting functional enzyme inhibition. These findings demonstrate the potential of integrated computational-experimental approaches for identifying arginase-modulatory peptides encrypted within food proteins.