TDR Targets: integrated chemogenomic mining of pathogen genomes for drug discovery.

LANDABURU, LIONEL URÁN; CHERNOMORETZ A; VIDELA, SANTIAGO; BERENSTEIN AJ; BIVORT HAIEK F; ZAROWIECKI M; BERRIMAN M; SHANMUGAM D; AGÜERO F

Berlin

Simposio; 21st-Century Drug Discovery and Development for Global Health (S3); 2018

Keystone Symposia

The volume of biological, chemical and functional data deposited in the public domain isgrowing rapidly, thanks to next generation sequencing, and highly-automated screeningtechnologies. However, there is still a large data imbalance between model, well-fundedorganisms and pathogens causing neglected diseases (NDs). We developed a chemogenomicsresource, (TDR Targets, tdrtargets.org), that aims to organize and integrate heterogeneouslarge datasets with a focus on drug discovery for human pathogens. The database also hostschemical and genomic data from other organisms to leverage data for comparative andinference-based queries. One of the major impacts of TDR Targets is to facilitate target andchemical prioritizations by allowing users to formulate complex queries across diverse queryspaces.In this communication we will highlight new data and functionality updates in TDR Targets. Inthis release, the database has been updated to integrate data on >2 million bioactivecompounds; 20 pathogen genomes; and 30 complete genomes from model organisms andother related pathogens. Furthermore, the data was also used to populate a recently developedcomplex network model (Berenstein AJ, 2016) to produce i) a novel ​ druggability ​ metric fortargets based on the connectivity in the network to bioactive compounds, ii) to guide newprioritization strategies for both targets and compounds, and iii) to visually aid in the navigationacross target/compound spaces in the web interface. This network model connects protein(target) nodes to compounds, based on curated bioactivity annotations. It also connects proteinsto other proteins based on shared annotations, and compounds to other compounds based onchemical similarity and substructure metrics. This chemogenomic network facilitates a numberof inferences, such as inferring plausible targets for orphan drugs or candidate compounds fororphan targets. Funded by ANPCyT-Glaxo Argentina (PICTO-Glaxo-2013-0067).