Crowdsourcing anti-malarial drug discovery FightMalaria@Home Crowdsourcing anti-malarial drug discovery

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  • 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

    Introduction We propose to perform virtual screening studies against all possible targets in Plasmodium falciparum. Target protein structures include both crystallised proteins and homology models. Molecule libraries include the freely available ZINC compound library, FDA approved drugs and cyclical constrained peptides and peptidomimetics. The computational power needed for this endeavour is colossal. We are therefore building a BOINC server that will harness 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, which should 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 Choice The P. fal proteome was searched against the 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 known crystal structures, but poor homology to hu- man proteins. The figure shows the relative BLASTp scores against the PDB and human datasets, 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 putative or hypothetical proteins that may be of inter- est but remain unannotated. These will be cross-referenced against PlasmoDB (http:// plasmodb.org/) and TDRtargets (http:// tdrtargets.org/, 115 PDB structures, 4735 models) to identify target structures to add to the 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. fal FK506 binding protein (2VN1). Insert high- lights first 100 results.

    1 -11.4 ZINC19324669 6 -10.58 ZINC20581662 2 -10.79 ZINC19340398 7 -10.28 ZINC20841257 3 -10.67 ZINC19340382 8 -10.19 ZINC22078620 4 -10.64 ZINC20522231 9 -10.19 ZINC20309805 5 -10.62 ZINC19322221 10 -10.15 ZINC20167871

    The first 10 docking results from eHiTS using a PS3 GRID (TCD). Results are listed by rank, docking score and ZINC code. Each file con- tained 5000 molecules, and took 30 hours to compute on one machine. Total CPU time was 900 hours = 38 days for one machine, but only 2.4 days on 16 GRID machines.

    Acknowledgements The 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 green carbon atoms. The top hit (ZINC19324669) is shown with purple carbons.