Identification of antihypertensive, antidiabetic, and antioxidant peptides derived from hydrolysates of dairy white wastewaters containing milk proteins using machine learning insights

Dairy white wastewater (WW), a by-product of industrial cleaning processes, contains residual milk proteins that can be enzymatically converted into bioactive peptides. In this study, WW proteins were hydrolyzed using four enzymes, pepsin, trypsin, thermolysin, and pronase E, for up to 240 min, and the resulting hydrolysates were evaluated for ACE inhibition, DPP-IV inhibition, and antioxidant capacity. Thermolysin hydrolysates exhibited the strongest ACE inhibition, with IC 50 values as low as 21.0 μg protein/mL, whereas pepsin and pronase E hydrolysates showed DPP-IV inhibitory activities with IC 50 values of 2.4–3.1 mg protein/mL. Pepsin hydrolysates presented the highest antioxidant capacity, reaching 3.5 μM Trolox equivalents/mg protein. LC-MS/MS analysis combined with multivariate statistics identified 60 discriminant peptides, including 17 peptides previously reported to possess antihypertensive, antidiabetic, and/or antioxidant bioactivities. Based on a combination of PLS-DA loadings, QSAR scores, novelty relative to known bioactive peptides and physicochemical diversity, 20 peptides were synthesized and validated experimentally. Several peptides such as LRF, QW, GAWY, PPF, GPIVL, and SFNPTQL exhibited potent inhibitory effects, with micromolar IC 50 values for ACE and/or DPP-IV. In comparison to chemically synthesized ACE inhibitors like captopril, the most potent peptide, LRF, is five times more active on a molar basis (11.34 μM vs 63.06 μM). These findings demonstrate that WW is a promising source of multifunctional peptides and that integrating peptidomics with machine learning accelerates peptide discovery and validation. • Dairy wastewater proteins represent a valuable source of bioactive peptides. • PLS-DA and QSAR together identify potent peptides from wastewater milk proteins. • 6 newly synthesized peptides demonstrate high ACE, DPP-IV, or antioxidant activity. • 3 peptides exhibited all bioactivities with ability for metabolic syndrome therapy. • Machine learning effectively guided the discovery of novel multifunctional peptides.