Homology-based in silico identification of putative protein-ligand interactions in the malaria parasite

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.