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Year : 2013  |  Volume : 50  |  Issue : 1  |  Page : 38-44

Molecular modeling, structural analysis and identification of ligand binding sites of trypanothione reductase from Leishmania mexicana

Marmara University, Faculty of Arts and Sciences, Department of Biology, Istanbul, Iran

Correspondence Address:
Ozal Mutlu
Marmara University, Faculty of Arts and Sciences, Department of Biology, Goztepe Campus, 34722, Goztepe, Istanbul
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Source of Support: None, Conflict of Interest: None

PMID: 23703438

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Background & objectives: Trypanothione reductase (TR) is a member of FAD-dependent NADPH oxidoreductase protein family and it is a key enzyme which connects the NADPH and the thiol-based redox system. Inhibition studies indicate that TR is an essential enzyme for parasite survival. Therefore, it is an attractive target enzyme for novel drug candidates. There is no structural model for TR of Leishmania mexicana (LmTR) in the protein databases. In this work, 3D structure of TR from L. mexicana was identified by template-based in silico homology modeling method, resultant model was validated, structurally analyzed and possible ligand binding pockets were identified. Methods: For computational molecular modeling study, firstly, template was identified by BLAST search against PDB database. Multiple alignments were achieved by ClustalW2. Molecular modeling of LmTR was done and possible drug targeting sites were identified. Refinement of the model was done by performing local energy minimization for backbone, hydrogen and side chains. Model was validated by web-based servers. Results: A reliable 3D model for TR from L. mexicana was modeled by using L. infantum trypanothione reductase (LiTR) as a template. RMSD results according to C-alpha, visible atoms and backbone were 0.809 Å, 0.732 Å and 0.728 Å respectively. Ramachandran plot indicates that model shows an acceptable stereochemistry. Conclusion: Modeled structure of LmTR shows high similarity with LiTR based on overall structural features like domains and folding patterns. Predicted structure will provide a source for the further docking studies of various peptide-based inhibitors.

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