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Page 1: Crowdsourcing anti-malarial drug discovery FightMalaria@Home · Crowdsourcing anti-malarial drug discovery Anthony Chubb, Kevin O’Brien, Catherine Mooney, Denis Shields, The FM@H

FightMalaria@Home:Crowdsourcing anti-malarial drug discovery

Anthony Chubb, Kevin O’Brien, Catherine Mooney, Denis Shields, The FM@H Team*University College Dublin, UCD-CASL, Dublin 4, Ireland

IntroductionWe propose to perform virtual screening studies against all possible targets in Plasmodium falciparum. Target protein structures include both crystallisedproteins and homology models. Molecule libraries include the freely available ZINC compound library, FDA approved drugs and cyclical constrainedpeptides and peptidomimetics. The computational power needed for this endeavour is colossal. We are therefore building a BOINC server that willharness the world’s unused PC power in screen-saver mode from kind donors. As the computation and analysis is all volunteer based, using open-source or donated software, we will make all results available to the public. Our aim is that other groups purchase and test the hit molecules, whichshould provide novel therapeutic drugs. If you would like to get involved, please go to our website http://bioinfo-casl.ucd.ie/fmah/.

Data Flow Website

http://bioinfo-casl.ucd.ie/fmah/

Target ChoiceThe P. fal proteome was searched againstthe human, PDB and DrugBank datasets us-ing the Genomes2Drugs server (http://www.bioinformatics.rcsi.ie/g2d/) to iden-tify proteins that had good homology to knowncrystal structures, but poor homology to hu-man proteins. The figure shows the relativeBLASTp scores against the PDB and humandatasets, with the highlighted region indicat-ing proteins that have not yet been crystallised,but are likely to be unique to the Plasmod-ium parasite and are good candidates for ho-mology modelling. Numerous are putativeor hypothetical proteins that may be of inter-est but remain unannotated. These will becross-referenced against PlasmoDB (http://plasmodb.org/) and TDRtargets (http://tdrtargets.org/, 115 PDB structures, 4735models) to identify target structures to add tothe virtual screening pipeline.

Docking Results

eHiTS docking results for 150k ZINC drug-like compounds (diversity Tanimoto score 80%)docked into the crystal structure of the P. falFK506 binding protein (2VN1). Insert high-lights first 100 results.

1 -11.4 ZINC19324669 6 -10.58 ZINC205816622 -10.79 ZINC19340398 7 -10.28 ZINC208412573 -10.67 ZINC19340382 8 -10.19 ZINC220786204 -10.64 ZINC20522231 9 -10.19 ZINC203098055 -10.62 ZINC19322221 10 -10.15 ZINC20167871

The first 10 docking results from eHiTS usinga PS3 GRID (TCD). Results are listed by rank,docking score and ZINC code. Each file con-tained 5000 molecules, and took 30 hours tocompute on one machine. Total CPU time was900 hours = 38 days for one machine, but only2.4 days on 16 GRID machines.

AcknowledgementsThe work was funded through an IRCSET grant to Prof. Denis Shields. SimBioSys for the use of their eHiTS software.

* listed on our website.

Docked Poses

The crystallographic position of the native lig-and FK506 in P. fal FKBP35 is shown with greencarbon atoms. The top hit (ZINC19324669) isshown with purple carbons.

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