J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds.

Tópicos

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Resumo

JECTIVE: 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.MATERIALS AND 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.

Resumo Limpo

jectiv identifi potenti opportun drug repurpos develop autom approach prescreen predict proteom organ databas known drug target use freeli avail resourcesmateri method employ combin rubi script leverag data drugbank chembl databas mysql blast predict potenti drug target publish genom result previous cellbas screen identifi inhibitor cryptosporidium parvum growth use valid insilico predict methodresult invitro valid result use cellbas c parvum growth assay show predict inhibitor signific like expect chanc confirm activ predict inhibitor confirm depend drug target databas use method use predict inhibitor follow diseasecaus protozoan parasit includ c parvum entamoeba histolytica giardia intestinali leishmania braziliensi leishmania donovani leishmania major naegleria gruberi proxi naegleria fowleri plasmodium falciparum plasmodium vivax toxoplasma gondii trichomona vaginali trypanosoma brucei trypanosoma cruziconclus although proteomewid screen drug target disadvantag insilico method can develop fast broad inexpens effect invitro valid result c parvum indic method present can use construct librari direct small molecul screen pipelin structur model dock program facilit targetbas drug develop

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