Metabolic pathway and graph identification of new potential drug targets for Plasmodium Falciparum

Malaria is one of the world's serious diseases causing death of about half a million people in 2015. The protozoan Plasmodium Falciparum inflicts the most damage and is responsible for most malaria related deaths. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing drugs, thus making it essential to discover new drugs. We have established a simple computational tool which analyses the topology of the metabolic network of Plasmodium Falciparum to identify essential enzymes as possible drug targets. We investigated the importance of an enzyme in the metabolic network by deleting (knocking-out) a reaction in simulation and examining its effect on the remaining network. Our algorithm then checked whether neighboring compounds of the investigated reaction could be produced by alternative biochemical pathways by using breadth first searches on the whole network. We proposed the use of evolutionary distances as a feature for our machine learning approach to identify potential drug targets. With the help of the machine learning method with the extracted features, we validated previously confirmed and published drug targets in metabolic network of Plasmodium Falciparum. We further identified two new potential targets: dihydrolipoyl dehydrogenase and aconitate hydratase using our approach.