Abstract:
BACKGROUND: Up to half a billion human clinical cases of malaria are reported each year, resulting
in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse
of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate.
Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.
METHODS: A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen
interactions among others. Searching by chemical structure is also available.
RESULTS: An in silico system for the selection of putative drug targets and lead compounds is
presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.
CONCLUSION: The Discovery system allows for the identification of putative drug targets and lead
compounds in Plasmodium species based on the filtering of protein and chemical properties.
