A kernel for the Tropical Disease Initiative

By L. Ortí, R. J. Carbajo, Ursula Pieper, Narayanan Eswar, S. Maurer, Arti K. Rai, Ginger Taylor, M. Todd, A. Pineda-Lucena, Andrej Sali and 1 more. 2009

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a organisms in bold are included in the World Health organization (Geneva) Tropical disease portfolio. b Number of transcripts in each genome. c Number of targets with at least one domain accurately modeled (that is, ModPIPE quality score of at least 1.0). d Number of modeled targets with at least one predicted binding site for a molecule with a Tanimoto score 11 of at least 0.9 to a drug in drugBank 12. e Number of modeled targets with at least one predicted binding site for a molecule in drugBank. To the Editor: Identifying proteins that are good drug targets and finding drug leads that bind to them is generally a challenging problem. It is particularly difficult for neglected tropical diseases, such as malaria and tuberculosis, where research resources are relatively scarce 1. Fortunately, several developments improve our ability to deal with drug discovery for neglected diseases: first, the sequencing of many complete genomes of organisms that cause tropical diseases; second, the determination of a large number of protein structures; third, the creation of compound libraries, including already-approved drugs; and fourth, the availability of improved bioinformatics analysis, including methods for comparative protein structure modeling, binding site identification, virtual ligand screening and drug design. Therefore, we are now in a position to increase the odds of identifying high-quality drug targets and drug leads for neglected tropical diseases. Here we encourage a collaboration among scientists to engage in drug discovery for tropical diseases by providing a 'kernel' for the Tropical Disease Initiative (TDI, http:// www.tropicaldisease.org/) 2. As the Linux kernel did for open source code development, we suggest that the TDI kernel may help overcome a major stumbling block, in this case, for open source drug discovery: the absence of a critical mass of preexisting work that volunteers can build on incrementally. This kernel complements several other initiatives on neglected tropical diseases 3–5 , including collaborative web portals updated list of initiatives, see the TDI website above. The TDI kernel was derived with our software pipeline 6,7 for predicting structures of protein sequences by comparative modeling, localizing small-molecule binding sites on the surfaces of the models and predicting ligands that bind to them. Specifically, the pipeline linked 297 proteins from ten pathogen genomes with already approved drugs that were developed for treating other diseases (Table 1). Such links, if proven experimentally, may significantly increase the efficiency of target identification, target validation, lead discovery, …

Subfields: Epidemiology, Parasitology, Public Health, Environmental and Occupational Health, Medical & Health AI