title: Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds.
abstract: OBJECTIVE
  To identify potential opportunities for drug repurposing by developing an automated approach to pre-screen the predicted proteomes of any organism against databases of known drug targets using only freely available resources.
  
  
  METHODS
  We employed a combination of Ruby scripts that leverage data from the DrugBank and ChEMBL databases, MySQL, and BLAST to predict potential drugs and their targets from 13 published genomes. Results from a previous cell-based screen to identify inhibitors of Cryptosporidium parvum growth were used to validate our in-silico prediction method.
  
  
  RESULTS
  In-vitro validation of these results, using a cell-based C parvum growth assay, showed that the predicted inhibitors were significantly more likely than expected by chance to have confirmed activity, with 8.9-15.6% of predicted inhibitors confirmed depending on the drug target database used. This method was then used to predict inhibitors for the following 13 disease-causing protozoan parasites, including: C parvum, Entamoeba histolytica, Giardia intestinalis, Leishmania braziliensis, Leishmania donovani, Leishmania major, Naegleria gruberi (in proxy of Naegleria fowleri), Plasmodium falciparum, Plasmodium vivax, Toxoplasma gondii, Trichomonas vaginalis, Trypanosoma brucei and Trypanosoma cruzi.
  
  
  CONCLUSIONS
  Although proteome-wide screens for drug targets have disadvantages, in-silico methods can be developed that are fast, broad, inexpensive, and effective. In-vitro validation of our results for C parvum indicate that the method presented here can be used to construct a library for more directed small molecule screening, or pipelined into structural modeling and docking programs to facilitate target-based drug development.
authors.affiliation: Cell, Molecular, and Biomedical Sciences Graduate Program, University of Vermont College of Medicine, Burlington, Vermont, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Sateriale
authors.forename: Kovi
authors.initials: K
authors.lastname: Bessoff
authors.forename: Indra Neil
authors.initials: IN
authors.lastname: Sarkar
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Huston
mesh_terms: D000818:Animals; D000977:Antiparasitic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D003198:Computer Simulation; D016785:Cryptosporidium parvum / Q000187:drug effects; D030562:Databases, Protein; D058492:Drug Repositioning / Q000379:methods*; D006801:Humans; D010271:Parasites / Q000187:drug effects*; D010272:Parasitic Diseases / Q000188:drug therapy; D021261:Parasitic Sensitivity Tests; D020543:Proteome
chemical_list: D000977:Antiparasitic Agents; D020543:Proteome
keywords: computational biology; drug repositioning; drug therapy; parasitology; proteomics