mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Abstract

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Keywords: anticancer peptides; feature selection; optimal features; sequential forward search; support vector machine.

Figure 1

Performance of various feature encodings in a 10-fold cross-validation.

Figure 2

Comparison of SVM with other classifiers on seven different feature encodings.

Figure 3

Sequential forward search for discriminating between anticancer peptides (ACPs) and non-ACPs. The maximum accuracy obtained from 10-fold cross-validation is shown for each feature encoding.

Figure 4

Performance comparison between the optimal feature set-based model against the respective controls (using all features).

Figure 5

(A) Performance comparison of mACPpred with the single feature models, based on optimal features. (B) Performance comparison between mACPpred and hybrid features-based models.

Figure 6

Comparison of binormal receiver operating characteristics (ROC) curves for ACPs prediction using different methods on independent dataset.