Background: Malaria is one of the major infectious diseases caused by Plasmodium falciparum
(P. falciparum). The proteins secreted by malarial parasite play important roles in drug design
in anti-malaria. Thus, it is very important to accurately identify secretory proteins of malarial parasite.
Although biochemical experiments can solve the issue, it is both time- and money-consuming. Computational
methods provide an important tool for fast and correct identification of the proteins secreted by
Method: The aim of the letter is to design a powerful prediction model to identify the secretory proteins
of malarial parasite. In this model, the physicochemical properties of residues were incorporated
into traditional pseudo amino acid composition to discretely formulate the secretory protein samples.
Subsequently, the optimal feature subset was obtained by analysis of variance (ANOVA). Finally, the
support vector machine was proposed to perform classification.
Results: In 5-fold cross-validation test, the overall accuracy reached 91.3%. Comparison with other
method proves that the proposed method is powerful and robust.
Conclusion: This study demonstrates that the novel properties are important features for secretory protein