Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors

Show simple item record Zhang, Yuezhou Xhaard, Henri Ghemtio, Leo 2018-08-19T03:24:33Z 2018-08-19T03:24:33Z 2018-08-17
dc.identifier.citation Journal of Cheminformatics. 2018 Aug 17;10(1):40
dc.description.abstract Abstract Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively involved in separating inhibitors from non-inhibitors. The potential targets of betulin derivatives inhibitors were predicted by in silico target fishing using structure-based pharmacophore searching and compound-pharmacophore-target-pathway network analysis, first on PDB and then among L. donovani homologs using a PSI-BLAST search. The essential identified proteins are all related to protein kinase family. Previous research already suggested members of the cyclin-dependent kinase family and MAP kinases as Leishmania potential drug targets. The PSI-BLAST search suggests two L. donovani proteins to be especially attractive as putative betulin target, heat shock protein 83 and membrane transporter D1.
dc.publisher Springer International Publishing
dc.subject Leishmania donovani inhibitors
dc.subject Betulin derivatives
dc.subject Predictive modeling
dc.subject Classification models
dc.subject Recursive partitioning
dc.subject In silico target prediction
dc.subject Structure-based pharmacophore
dc.subject Network analysis
dc.title Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors 2018-08-19T03:24:33Z
dc.language.rfc3066 en
dc.rights.holder The Author(s)

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