Prediction of ACE Inhibitor Tripeptides Activity Based on Amino Acid Descriptors(E) from Multiple Linear Regression Model

A novel amino acids quantitative descriptor E, which (E₁~E₅) was derived from the 5 principal components of 237 physical-chemical properties, has been introduced in bioactive peptides Quantitative Structure-Activity Relationship (QSAR) Study in the article. It has been proved that correlate good with hydrophobicity, size, preference for amino acids to occur in Of helices, composition and the net charge, respectively. They were then applied to construct characterization and QSAR analysis on 55 angiotensin-converting enzyme (ACE) inhibitor tripeptides by multiple linear regression (MLR). The leave-one-out cross validation values (Q²(Cv)) was 0.980, the multiple correlation coefficients (R²) was 0.991, the root mean square error (RMSE) for estimated error was 0.062 for ACE inhibitor tri-peptides by MLR. The results showed that, in comparison with the conventional descriptors, the new descriptor (E) is a useful structure characterization method for peptide QSAR analysis. The importance of each parameter or property at each position in peptides is estimated by the regression coefficient value of the MLR model. The establishment of such methods will be a very meaningful work to peptide bioactive investigation in peptide analogue drug design.