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3D-QSAR and cell wall permeability of antitubercular nitroimidazoles against Mycobacterium tuberculosis.


ABSTRACT: Inhibitory activities of monocyclic nitroimidazoles against Mycobacterium tuberculosis (Mtb) deazaflavin-dependent nitroreductase (DDN) were modeled by using docking, pharmacophore alignment and comparative molecular similarity indices analysis (CoMSIA) methods. A statistically significant model obtained from CoMSIA was established based on a training set using pharmacophore-based molecular alignment. The leave-one out cross-validation correlation coefficients q2 (CoMSIA) were 0.681. The CoMSIA model had a good correlation (r2(pred)/CoMSIA = 0.611) between the predicted and experimental activities against excluded test sets. The generated model suggests that electrostatic, hydrophobic and hydrogen bonding interactions all play important roles for interaction between ligands and receptors. The predicted cell wall permeability (logP(app)) for substrates with high inhibitory activity against Mtb were investigated. The distribution coefficient (logD) range was 2.41 < logD < 2.89 for the Mtb cell wall membrane permeability. The larger the polar surface area is, the better the permeability is. A larger radius of gyration (rgry) and a small fraction of rotatable bonds (f(rtob)) of these molecules leads to higher cell wall penetration ability. The information obtained from the in silico tools might be useful in the design of more potent compounds that are active against Mtb.

SUBMITTER: Lee SH 

PROVIDER: S-EPMC6270125 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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3D-QSAR and cell wall permeability of antitubercular nitroimidazoles against Mycobacterium tuberculosis.

Lee Sang-Ho SH   Choi Minsung M   Kim Pilho P   Myung Pyung Keun PK  

Molecules (Basel, Switzerland) 20131108 11


Inhibitory activities of monocyclic nitroimidazoles against Mycobacterium tuberculosis (Mtb) deazaflavin-dependent nitroreductase (DDN) were modeled by using docking, pharmacophore alignment and comparative molecular similarity indices analysis (CoMSIA) methods. A statistically significant model obtained from CoMSIA was established based on a training set using pharmacophore-based molecular alignment. The leave-one out cross-validation correlation coefficients q2 (CoMSIA) were 0.681. The CoMSIA  ...[more]

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