High-throughput prioritization of putative drug targets in the malaria parasite, Plasmodium falciparum

Abstract

Drug resistance to almost all known antimalarials is widespread and is rapidly increasing. This resistance is due to the over and misuse of these antimalarials, thus new antimalarial drugs are necessary to help in the prevention and cure of this widespread disease. Continuous in-depth studies are being done on a handful of putative targets for future exploitation and use, but not many resources are available that focus on performing data mining and target identification on the complete malaria genome, together with relations to chemical compounds. The DISCOVERY Database is a web-based system, developed for the in silico selection of drug target proteins and lead compounds. It is a database filled with malaria information and aspects that might influence the druggability of a malaria parasite protein and guide a scientist in choosing the right ligand for a protein. DISCOVERY can aid in attempting to predict the interaction of ligands with proteins of interest, associating chemical compound with malaria proteins and selective chemical similarity searches. It can be used to mine information on malaria proteins, predict ligands and compare human and mosquito host characteristics. DISCOVERY2 was developed in Java with NetBeans. The protein sequences for the Plasmodium spp. included in DISCOVERY were downloaded from PlasmoDB; the Homo sapiens proteins were downloaded from Ensembl and the Anopheles gambiae proteins was downloaded from VectorBase. Even though DISCOVERY is primarily focused on Plasmodium falciparum it also contains information for all proteins from Plasmodium vivax, Plasmodium yoelii, Plasmodium knowlesi, Plasmodium chabaudi and Plasmodium berghei as well for the human vector and mosquito host. Protein information includes sequences and annotations, functional predictions, gene ontology terms, orthology information, structural information, metabolic pathways, predicted putative protein-ligand interactions, druggability predictions and literature links. Chemical compounds are also included. Recently approaches have illustrated the value of predicting the association of chemical compounds with putative drug targets, especially when the targets of compounds, like the Glaxo Smith Kline dataset with known activity against the parasite may be extrapolated, using protein-ligand interaction databases, like ChemProt. DISCOVERY attempts to use a similar approach in associating chemical compounds with malaria proteins, using sequence homology, and also selective chemical similarity searches. Chapter 1 of this dissertation is a literature review focusing on the in silico identification of potential drug targets. It also mentions a few techniques/approaches with which to accomplish this as well as target databases that can be used to help in the identification process. Chapter 2 describes the steps taken to run and score the Plasmodium falciparum proteins in a high throughput manner through DISCOVERY. Chapter 3 gives four case studies from DISCOVERY, a protein that had a low weighted score, a protein with a very high weighted score and two proteins with weighted scores in between the other two. And Chapter 4 concludes by looking at how researchers can use this study as a starting point. In this dissertation, DISCOVERY2 was used, in conjunction with Taverna pipelines, to study all Plasmodium falciparum proteins in a high throughput manner to be able to identify possible drug targets that might be of importance for future drug identification.