Putative Drug and Vaccine Target Identification in Leishmania donovani Membrane Proteins Using Naïve Bayes Probabilistic Classifier.

Predicting the role of protein is one of the most challenging problems. There are few approaches available for the prediction of role of unknown protein in terms of drug target or vaccine candidate. We propose here Naïve Bayes probabilistic classifier, a promising method for reliable predictions. This method is tested on the proteins identified in our mass spectrometry based membrane protemics study of Leishmania donovani parasite that causes a fatal disease (Visceral Leishmaniasis) in humans all around the world. Most of the vaccine/drug targets belonging to membrane proteins are represented as key players in the pathogenesis of Leishmania infection. Analyses of our previous results, using Naïve Bayes probabilistic classifier, indicate that this method predicts the role of unknown/hypothetical protein (as drug target/vaccine candidate) significantly with higher precision. We have employed this method in order to provide probabilistic predictions of unknown/hypothetical proteins as targets. This study reports the unknown/hypothetical proteins of Leishmania membrane fraction as a potential drug targets and vaccine candidate which is vital information for this parasite. Future molecular studies and characterization of these potent targets may produce a recombinant therapeutic/prophylactic tool against Visceral Leishmaniasis. These unknown/hypothetical proteins may open a vast research field to be exploited for novel treatment strategies.