Using K-minimum increment of diversity to predict secretory proteins of malaria parasite based on groupings of amino acids.

Due to the complexity of Plasmodium falciparumis genome, predicting secretory proteins of P. falciparum is more difficult than other species. In this study, based on the measure of diversity definition, a new K-nearest neighbor method, K-minimum increment of diversity (K-MID), is introduced to predict secretory proteins. The prediction performance of the K-MID by using amino acids composition as the only input vector achieves 88.89% accuracy with 0.78 Mathew's correlation coefficient (MCC). Further, the several reduced amino acids alphabets are applied to predict secretory proteins and the results show that the prediction results are improved to 90.67% accuracy with 0.83 MCC by using the 169 dipeptide compositions of the reduced amino acids alphabets obtained from Protein Blocks method.