QSAR and Molecular Docking of Echinopsine Derivatives Containing Acylhydrazone Moiety Against Tobacco Mosaic Virus

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Madadi Mahani Nosrat; Bagherizadeh Maryam

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF ANALYTICAL CHEMISTRY Year:2024 | Volume:11 | Issue:1 Page(s): 21-29

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Information Journal Paper

Title

QSAR and Molecular Docking of Echinopsine Derivatives Containing Acylhydrazone Moiety Against Tobacco Mosaic Virus

Author(s)

Madadi Mahani Nosrat | Bagherizadeh Maryam | Issue Writer Certificate

Keywords

Antiviral activity

Multiple linear regression

Lowest unoccupied molecular orbital (LUMO)

Gibbs free energy

2OM3 protein

Abstract

Tobacco mosaic virus causes great economic damage to tobacco, pepper, cucumber and ornamental flowers all over the world. In the current work, the relationship between the structure and activity of novel series echinopsin derivatives containing acylhydrazone fragments as antiviral activity against tobacco mosaic virus (TMV) was studied using quantitative structure-activity relationship (QSAR) calculations and molecular docking analysis. Molecular docking analysis of echinopsin derivatives with tobacco mosaic virus (2OM3) protein was done using AutoDock software and descriptors such as binding energy, electroestatic energy and hydrogen bond energy were calculated. The negative values of the binding energy illustrated that the binding nature of these derivatives, as the ligand with the 2OM3 protein is strong. For QSAR model first, the dataset was divided into two groups of training and test sets. Then, descriptors were calculated using quantum mechanics, molecular docking and molecular descriptors. Then, modeling was done by multiple linear regression (MLR) method. It was found that of the lowest unoccupied molecular orbital (LUMO) and Gibbs free energy changes play a role in the model. Also, the descriptors of the total energy of van der Waals interactions, hydrogen bond and energy of vdW + H-bond + desolvation of the molecular docking descriptors have an effect in the regression model. This study can play an important role to design anti –TMV inhibitors.

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