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Predicting dual-targeting anti-influenza agents using multi-models

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Abstract

Influenza is an acute respiratory infectious disease caused by influenza viruses. Its subtype can be distinguished based on the antigenicity of two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). One of the main challenges in anti-influenza drug development is the quick evolution of drug resistance due to virus mutations. One solution to this problem is to develop dual-targeting anti-influenza agents. In this paper, a new rationally designed virtual screening protocol that combines structure-based approaches (molecular docking and molecular dynamic simulations) and ligand-based approaches (support vector machines and 3D shape & electrostatic similarity algorithms) is reported for the virtual screening of dual-targeting agents against HA and NA. The final hits came from the consensus of the ligand- and receptor-based knowledge of HA and NA and were tested using ADMET predictions. Evidence from the binding energy calculations and binding mode analyses suggested that several of the hits are promising as dual-targeting anti-influenza agents. The virtual screening protocol may also lead to the identification of innovative drugs in other fields.

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Acknowledgments

This work was funded by the Introduction of Innovative R&D Team Program of Guangdong Province (No. 2009010058) and the National Natural Science Foundation of China (No. 81001372, 81173470). The National Supercomputing Center in Guangzhou (2012Y2-00048, 201200000037) provided the computing resources necessary for this report. The research was also supported in part by the Guangdong Province Key Laboratory of Computational Science and the Guangdong Province Computational Science Innovative Research Team.

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  1. School of Pharmaceutical Sciences & Institute of Human Virology, Sun Yat-Sen University, 132 East Circle Road at University City, Guangzhou, 510006, China

    Yu Wang, Hu Ge, Yali Li, Yufang Xie, Yingyan He, Mengyan Xu, Qiong Gu & Jun Xu

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  1. Yu Wang
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  2. Hu Ge
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  3. Yali Li
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  8. Jun Xu
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Correspondence to Jun Xu.

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Wang, Y., Ge, H., Li, Y. et al. Predicting dual-targeting anti-influenza agents using multi-models. Mol Divers 19, 123–134 (2015). https://doi.org/10.1007/s11030-014-9552-4

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