Abstract:
Malaria is still one of the most proli c communicable diseases in the world with more
than 200 million infections annually, its greatest e ect is felt in the poor nations with-in
sub-saharan Africa and south-east Asia. It is especially fatal for women and children where
out of the 660 000 fatalities in 2010, 86% were below the age of 5.
In the past decade the global fatality rate due to malaria has been signi cantly reduced,
primarily due to proliferation of vector control using treated nets and indoor residual spraying
of DDT. There have, however, been few innovations in anti-malarial therapeutics and with
the threat of the spread of drug resistant strains a need still exists to develop novel drugs to
combat malaria infections. One of the major hinderances to drug development is the huge cost
of the drug development process, where candidate failures late in development are extremely
costly. This is where post-genomic information has the potential of adding great value. By
using all available data pertaining to a disease, one gains higher discerning power to select
good drug candidates and identify risks early in development before serious investments are
made. This need provided the motivation for the development of Discovery; a tool to aid in
the identi cation of protein targets and viable lead compounds for the treatment of malaria.
Discovery was developed at the University of Pretoria to be a platform for a large spectrum
of biological data focused on the malaria causing Plasmodium parasite. It conglomerates
various data types into a web-based interface that allows searching using logical lters or
by using protein or chemical start points. In 2010 it was decided to rebuild Discovery to improve it's functionality and optimize query times. Also, since its inception various new
datasources became available speci cally related to bio-active molecules, these include the ChEMBL database and TCAMS dataset of bio-active molecules and the focus of this project
was the integration of said datasets into Discovery. Large quantities of high quality bioactivity
data have never been available in the public domain and this has opened up the
opportunity to gain even greater insight into the activity of chemical compounds in malaria.
Due to conserved structural/functional similarities of proteins between di erent species it
is possible to derive predictions about a malaria protein or a chemicals activity in malaria
due to experiments carried out on other organisms. These comparisons can be leveraged to
highlight potential new compounds that were previously not considered or prevent wasting
resources persuing potential compounds that pose threats of toxicity to humans. This project
has resulted in a web based system that allows one to search through the chemical space of
the malaria parasite. Allowing them to view sets of predicted protein-ligand interactions for
a given protein based on that proteins similarity to those existing in the bio-active molecule
databases.
