Gaussian process: an alternative approach for QSAM modeling of peptides

Abstract

Different statistical modeling methods (SMMs) are used for nonlinear system classification and regression. On the basis of Bayesian probabilistic inference, Gaussian process (GP) is preliminarily used in the field of quantitative structure-activity relationship (QSAR) but has not yet been applied to quantitative sequence-activity model (QSAM) of biosystems. This paper proposes the application of GP as an alternative tool for the QSAM modeling of peptides. To investigate the modeling performance of GP, three classical peptide panels were used: Angiotensin-I converting enzyme inhibitory dipeptides, bradykinin-potentiating pentapeptides and cationic antimicrobial pentadecapeptides. On this basis, we made a comprehensive comparison between the GP and some widely used SMMs such as PLS, artificial neural network (ANN) and support vector machine (SVM), and gave the conclusions as follow: (1) for those of structurally complicated peptides, particularly the polypeptides, linear PLS was incapable of capturing all dependences hidden in the peptide systems, (2) even in assistance with the monitoring technique, ANN was inclined to be overtrained in the cases of insufficient number of peptide samples, (3) SVM and GP performed best for the three peptide panels. Moreover, since GP was able to correlate the linear and nonlinear-hybrid relationship, it was slightly superior to SVM at most peptide sets.