Plasmod-PPI: A web-server predicting complex biopolymer targets in plasmodium with entropy measures of protein-protein interactions

We can define structural indices of polymer or biopolymer complex structures and use them in the prediction of new drug targets in parasites. For instance, Plasmodium falciparum causes the most severe form of Malaria and kills up to 2.7 million people annually whereas Plasmodium vivax is geographically the most widely distributed cause with more than 80 million clinical cases. Due to drug resistance and toxicity, discovering novel drug targets is mandatory; such as Protein-Protein Complexes unique in this pathogen and not present in human host (pPPCs). Additionally, the 3D structure of an increasing number of Plasmodium proteins is being reported in public databases making easier the development of bioinformatics models to predict pPPCs. In addition, some PPCs expressed both in parasite and human, such as DHFR synthase, play a significant role in drug resistance in both Malaria and Human Cancer. However, there are no general models to predict pPPCs using indices of PPC biopolymer structure. Therefore, we introduced herein new Markov Chain numerical descriptors of protein-protein Interactions (PPIs) based on electrostatic entropy measures and calculated these parameters for 5257 pairs of proteins (774 pPPCs and 4483 non-pPPCs) from more than 20 organisms, including parasite and human hosts. We found a simple Classification Tree with high Accuracy, Sensitivity, and Specificity (90.2-98.5%) both in training and independent test sub-sets and implemented this predictor in the user-friendly web server PlasmodPPI freely available at http://miaja.tic.udc.es/Bio-AIMS/PlasmodPPI.php.