Developing Computational Methods to Aid Infectious Disease Therapeutics Through Analysis of Protein Function Evolution

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The recent revolution in high throughput DNA sequencing, started by the Human Genome Project, has led to large collections of data on a diverse set of organisms. This notably includes the parasitic, bacterial and viral agents that cause infectious diseases, as well as the organisms that are responsible for disease transmission. The emergence of this data offers new and exciting opportunities to understand these disease-causing agents and to develop novel therapeutics.An outstanding and challenging problem is to understand the functions of the proteins encoded by these genomes. Time and resources limit the number whose function can be experimentally determined; therefore methods for predicting function are of paramount importance. Moreover, new methods are required when applied to infectious diseases due to the complex relationships between the host organism and the disease causing agent. These associations also have implications for assessing which drugs are suitable for use against infectious diseases and for the development of new therapeutics.An understanding of the complex biochemical relationships that will facilitate the identifying of new drug targets for infectious diseases requires bringing together a range of diverse biological information. The best method for achieving this is using a multidisciplinary approach interfacing biology, chemistry and computer science techniques. In collaboration with colleagues at the London School of Hygiene and Tropical Medicine, the European Bioinformatics Institute and University College London, I will develop a unique computational resource specifically to handle genomes associated with infectious diseases that:- brings together relationships between protein sequences and their molecular structures, putting them into an evolutionary context as well as establishing measures of similarity between the functions of these proteins.- uses the data captured to develop a new method to predict the function of proteins by defining rules bases on the systematic analysis of cases where changes in function occur between related proteins and determining the features of that change.From the outset of the project, the methods developed will be applied to specific problems in infectious disease research, combined with validating predictions in collaboration with experimental groups. I will start by addressing the key enzymes involved in new drug treatments for Chagas disease, the most important parasitic infection in the Americas, with the aim of providing a better understanding of drug-resistance mechanisms. Predictions and functional annotations of the Trypanosoma and Leishmania genomes, the causative agents of sleeping sickness/Chagas disease and Leishmaniasis respectively, will be made to test the methods and to gain insight into how well they can contribute to enhancing the annotations of these genomes. Insights gained from the application and validation process will be used to further enhance the methods developed, ultimately enabling them to be used on any infectious disease agent. The resource and methods developed will also be used to identify new drug targets and possible unintended interactions between the drug and other proteins that may result in side effects in patients. An immediate application will seek to add value to the results of high-throughput drug screens against schistosomes. This trematode worm causes the world's second most socio-economically devastating parasitic disease (highlighted by he World Health Organization). The aim will be to identify which protein(s) the drug(s) might be targeting, and to determine if there is potential for adverse interaction in the human host. The research will eventually be of use in a clinical setting, with the real possibility of helping fight the huge variety of infectious diseases suffered by millions.