In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Adamu Uzairu,Gideon Adamu Shallangwa,Mustapha Abdullahi

doi:10.1186/s43088-019-0023-y

Adamu Uzairu, Gideon Adamu Shallangwa + Show 1 more

Open Access

https://doi.org/10.1186/s43088-019-0023-y

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Abstract

BackgroundThis research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target.ResultsIn building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (R2train = 0.72963, R2adjusted = 0.67169, Q2cv = 0.598, {R}_{mathrm{pred}}^2= 0.67295, R2test = 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus.ConclusionThe results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.

Highlights

This research provides a comprehensive analysis of quantitative structureactivity relationship (QSAR) modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features of the compounds to generate a statistically significant model
This study focused on combining both molecular docking approach and QSAR modeling method to the assessment of 25 aryl sulfonamide derivatives as a novel of H5N1 inhibitors
2.2 QSAR analysis 2.2.1 Collection of dataset and optimization Twenty-five (25) already synthesized aryl sulfonamides derivatives together with their tested activity concentrations on H5N1 virus were obtained from the literature [3]

Summary

Introduction

This research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. The molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target. The monthly risk evaluation of influenza at the human-animal boundary published by the World Health Organization in 2019 showed that several influenza A(H5Nx) subtypes continue to be detected in birds in Africa, Europe, and Asia. The WHO reported that there are over 860 reported cases of humans infected by the H5N1 virus, and 50% among them are dead since 2003, establishing that H5N1 virus has higher death rate when compared with other influenza A virus subtypes [1]. Rimantadine and amantadine are M2 proton channel inhibitors that inhibit uncoating of virus-related ribonucleoprotein [3]

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