Pharmacoinformatic Study on the Selective Inhibition of the Protozoan Dihydrofolate Reductase Enzymes

Sharma, Vishnu K. ; Abbat, Sheenu ; Bharatam, P. V. (2017) Pharmacoinformatic Study on the Selective Inhibition of the Protozoan Dihydrofolate Reductase Enzymes Molecular Informatics, 36 (11). p. 1600156. ISSN 1868-1743

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Official URL: http://doi.org/10.1002/minf.201600156

Related URL: http://dx.doi.org/10.1002/minf.201600156

Abstract

Dihydrofolate reductase (DHFR) is an essential enzyme of the folate metabolic pathway in protozoa and it is a validated, potential drug target in many infectious diseases. Information about unique conserved residues of the DHFR enzyme is required to understand residual selectivity of the protozoan DHFR enzyme. The three dimensional crystal structures are not available for all the protozoan DHFR enzymes. Enzyme-substrate/inhibitor interaction information is required for the binding mode characterization in protozoan DHFR for selective inhibitor design. In this work, multiple sequence analysis was carried out in all the studied species. Homology models were built for protozoan DHFR enzymes, for which 3D structures are not available in PDB. The molecular docking and Prime-MMGBSA calculations of the natural substrate (dihydrofolate, DHF) and classical DHFR inhibitor (methotrexate, MTX) were performed in protozoan DHFR enzymes. Comparative sequence analysis showed that an overall sequence identity between the studied species ranging from 22.94 % (CfDHFR-BgDHFR) to 94.61 % (LdDHFR-LmDHFR). Interestingly, it was observed that most of the active site residues were conserved in all the cases and all the enzymes exhibit similar key binding interactions with DHF and MTX in molecular docking analysis, but there are a few key binding residues which differ in protozoan species that makes it suitable for target selectivity. This information can be used to design selective and potent protozoan DHFR enzyme inhibitors.

Item Type:Article
Source:Copyright of this article belongs to FOIA Privacy.
Keywords:DHFR; Clustering; Comparative Sequence Analysis; Homology Modeling; Molecular Docking.
ID Code:116347
Deposited On:12 Apr 2021 09:26
Last Modified:12 Apr 2021 09:26

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