Study of Peptides QSAR Based on Multidimensional Attributes (E) Using Multiple Linear Regression

A new amino acids descriptor E, which (E1~E5) has been introduced in bioactive peptides Quantitative Structure-Activity Relationship (QSAR) Study. It has been proved that correlate good with hydrophobicity, size, preference for amino acids to occur in -helices, composition and the net charge, respectively. They were then applied to construct characterization and QSAR analysis on 48 bitter tasting dipeptides and 30 bradykinin potentiating (BP) pentapeptides using multiple linear regression (MLR). The leave-one-out cross validation values (Q2(CV)) were 0.888 and 0.797, the multiple correlation coefficients (R2) were 0.940 and 0.891, respectively for bitter tasting dipeptides and BP pentapeptides. The results showed that, in comparison with the conventional descriptors, the descriptor (E) is a useful structure characterization method for peptide QSAR analysis. The importance of each 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 drug design.