Structure⁻Activity Prediction of ACE Inhibitory/Bitter Dipeptides-A Chemometric Approach Based on Stepwise Regression

Forward and backward stepwise regression (FR and BR, respectively) was applied for the structure⁻bioactivity prediction of angiotensin converting enzyme (ACE)-inhibitory/bitter-tasting dipeptides. The datasets used in this study consisted of 28 sequences and numerical variables reflecting dipeptides' physicochemical nature. The data were acquired from the BIOPEP-UWM, Biological Magnetic Resonance Databank, ProtScale, and AAindex databases. The calculations were computed using STATISTICA^®13.1. FR/BR models differed in R² (0.91/0.76, respectively). The impact of C-_atC(-) and N-Molw(+) on the dual function of dipeptides was observed. Positive (+) and negative (-) correlations with log IC₅₀ are presented in parens. Moreover, C-Bur(+), N-_atH(+), and N-Pol(-) were also found to be important in the FR model. The additional statistical significance of N-bul(-), N-Bur(-), and N-Hdr(+) was reported in the BR model. These attributes reflected the composition of the dipeptides. We report that the "ideal" bitter ACE inhibitor should be composed of P, Y, F (C-end) and G, V, I, L (N-end). Functions: log R_caf. = f (observed log IC₅₀) and log R_caf. = f (predicted log IC₅₀) revealed no direct relationships between ACE inhibition and the bitterness of the dipeptides. It probably resulted from some structural discrepancies between the ACE inhibitory/bitter peptides and/or the measure of activity describing one of the two bioactivities. Our protocol can be applicable for the structure⁻bioactivity prediction of other bioactivities peptides.