Studying Peptides Biological Activities Based on Multidimensional Descriptors (E) Using Support Vector Regression

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Abstract

A quantitative multidimensional amino acids descriptors E (E1–E5) has been introduced in bioactive peptides Quantitative Structure–Activity Relationship (QSAR) study. These descriptors correlate well with hydrophobicity, size, preferences for amino acids to occur in α-helices, composition and the net charge, respectively. They were then applied to construct characterization and QSAR analysis on 48 angiotensin-converting enzyme (ACE) inhibitors dipeptides, 55 ACE inhibitors tripeptides and 48 bitter tasting dipeptides by support vector regression (SVR). The leave one out cross validation Q (CV) 2 were 0.886, 0.985 and 0.912, the root mean square error (RMSE) were 0.250, 0.021 and 0.123, respectively. 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 value of the model RMSE obtained using leave-one-parameter-out (LOPO) approach in the SVR model. This will be provided with certain guidance meaning to design and exploit peptide analogues. The results also indicate that SVR can be used as an alternative powerful modeling tool for peptide QSAR studies, and give one advice (LOPO) about evaluating the importance of parameter in SVR model. Moreover, it also offered an idea about nonlinear relation between bioactive of peptides and their structural descriptors E. The establishment of such methods will be a very meaningful work to peptide bioactive investigation in peptide analogue drug design.