Friday, September 03, 2010

Identification of attractive drug targets in neglected-disease pathogens using an in silico approach

PLoS Negl Trop Dis. 2010 Aug 24;4(8). pii: e804.

Identification of attractive drug targets in neglected-disease pathogens using an in silico approach

Crowther GJ, Shanmugam D, Carmona SJ, Doyle MA, Hertz-Fowler C, Berriman M, Nwaka S, Ralph SA, Roos DS, Van Voorhis WC, Agüero F.

Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, United States of America.

BACKGROUND: The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

METHODOLOGY/PRINCIPAL FINDINGS: Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in to generate highly customized ranked lists of potential targets.

CONCLUSIONS/SIGNIFICANCE: Using the datasets and the tools available in, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

PMID: 20808766 [PubMed - in process]

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