Prediction of mitochondrial proteins of malaria parasite using bi-profile Bayes feature extraction

Abstract

Mitochondrial proteins of Plasmodium falciparum are considered as attractive targets for anti-malarial drugs, but the experimental identification of these proteins is a difficult and time-consuming task. Computational prediction of mitochondrial proteins offers an alternative approach. However, the commonly used subcellular location prediction methods are unsuited for P. falciparum mitochondrial proteins whereas the organism and organelle-specific methods were constructed on the basis of a rather small dataset. In this study, a novel dataset termed PfM233, which included 108 mitochondrial and 125 non-mitochondrial proteins with sequence similarity below 25%, was established and the methods for predicting mitochondrial proteins of P. falciparum were described. Both bi-profile Bayes and split amino acid composition were applied to extract the features from the N- and C-terminal sequences of these proteins, which were then used to construct two SVM based classifiers (PfMP-N25 and PfMP-30). Using PfM233 as the dataset, PfMP-N25 and PfMP-30 achieved accuracies (MCCs) of 90.13% (0.80) and 90.99% (0.82). When tested with the commonly used 40 mitochondrial proteins in PfM175 and the 108 mitochondrial proteins in PfM233, these two methods obviously outperformed the existing general, organelle-specific and organism and organelle-specific methods.