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

L. URAN LANDABURU; A. BERENSTEIN; M. BERRIMAN; S. VIDELA; M. ZAROWIECKI; F. AGUERO; A. CHERNOMORETZ; F. BIVORT HAIEK; D SHANMUGAN

Berlin

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

The volume of biological, chemical and functional data deposited inthe public domain is growing rapidly, thanks to next generationsequencing, and highly-automated screening technologies. However,there is still a large data imbalance between model, well-fundedorganisms and pathogens causing neglected diseases (NDs). Wedeveloped a chemogenomics resource, (TDR Targets, tdrtargets.org),that aims to organize and integrate heterogeneous large datasets witha focus on drug discovery for human pathogens. The database alsohosts chemical and genomic data from other organisms to leverage datafor comparative and inference-based queries. One of the major impactsof TDR Targets is to facilitate target and chemical prioritizationsby allowing users to formulate complex queries across diverse queryspaces.In this communication we will highlight new data and functionalityupdates in TDR Targets. In this release, the database has beenupdated to integrate data on >2 million bioactive compounds; 20pathogen genomes; and 30 complete genomes from model organisms andother related pathogens. Furthermore, the data was also used topopulate a recently developed network model (Berenstein AJ, 2016) toproduce i) a novel druggability metric for targets based onthe connectivity in the network to bioactive compounds, ii) to guidenew prioritization strategies for both targets and compounds, andiii) to visually aid in the navigation across target/compound spacesin the web interface. This network model connects protein (target)nodes to compounds, based on curated bioactivity annotations. It alsoconnects proteins to other proteins based on shared annotations, andcompounds to other compounds based on chemical similarity andsubstructure metrics. This chemogenomic network facilitates a numberof inferences, such as inferring plausible targets for orphan drugsor candidate compounds for orphan targets.p { margin-bottom: 0.1in; direction: ltr; color: rgb(0, 0, 0); line-height: 120%; text-align: left; text-decoration: none; }p.western { font-family: "Arial", serif; font-size: 11pt; font-style: normal; font-weight: normal; }p.cjk { font-family: "Arial"; font-size: 11pt; font-style: normal; font-weight: normal; }p.ctl { font-family: "Liberation Serif"; font-size: 12pt; }