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

Wang, Yu; Hu, Ge; Li, Yali; Xie, Yufang; He, Yanjie; Xu, Mengyan; Gu, Qiong; Xu, Jing

doi:10.1007/s11030-014-9552-4

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…In this study, we have extracted pure Asprellcosides B from Ilex asprella , using phytochemistry and chromatographic methodologies (Porath and Flodin, 1959; Lerman, 1987) and evaluated its anti-influenza virus activity. We have also determined the HA as the viral target of Asprellcosides B, based on findings from a molecular docking simulation (Wang et al, 2015, 2016; Makau et al, 2017) and surface plasmon resonance (SPR) analysis (Giannetti, 2011). These results suggest that Asprellcosides B blocks the HA-mediated cell entry of the influenza A virus.…”

Section: Introductionmentioning

confidence: 99%

Asprellcosides B of Ilex asprella Inhibits Influenza A Virus Infection by Blocking the Hemagglutinin- Mediated Membrane Fusion

Zhang

Chen

et al. 2019

Front. Microbiol.

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Ilex asprella is routinely used in China as a traditional medicinal herb to treat influenza (Flu). However, its specific antiviral activity and underlying molecular mechanism have not yet been determined. In this study, we sought to determine the antiviral activity and mechanism of Asprellcosides B, an active component extracted from Ilex asprella, and used against the influenza A virus cell culture. We also performed a computer-assisted structural modeling analysis and carried out surface plasmon resonance (SPR) experiments in the hope of determining the viral target of Asprellcosides B. Results from our studies show that Asprellcosides B reduced virus replication by up to 63% with an IC50 of about 9 μM. It also decreased the low pH-induced and virus-mediated hemolysis by 71% in vitro. Molecular docking simulation analysis suggested a possible binding of Asprellcosides B to the hemagglutinin (HA), which was confirmed by a surface plasmon resonance (SPR) assay. Altogether, our findings demonstrate that Asprellcosides B inhibits the influenza A virus, through a specific binding to the HA, resulting in the blockade of the HA-mediated membrane fusion.

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Section: Introductionmentioning

confidence: 99%

Asprellcosides B of Ilex asprella Inhibits Influenza A Virus Infection by Blocking the Hemagglutinin- Mediated Membrane Fusion

Zhang

Chen

et al. 2019

Front. Microbiol.

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“…It should be mentioned that the application of LBVS tools for the planned design of ligands with a predefined dual-target profile is a recent development and represents an important challenge to medicinal chemists [16]. .…”

Section: Introductionmentioning

confidence: 99%

<strong>Virtual screening tailored ensembles of QSAR models for the discovery of dual A<sub>2A</sub> Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors</strong>

Helguera

Pérez-Castillo

Cordeiro

et al. 2015

Proceedings of MOL2NET, International Conference on Multidisciplinary Sciences

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Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. Frequently no analysis is performed of the diversity of the ensemble members or no considerations regarding the applicability domain of the base model are made. In this research we propose a method employing genetic algorithms optimization for the generation of virtual SciForum Mol2Net, 2015, 1(Section B), pages 1-10, Proceedings 2 http://sciforum.net/conference/mol2net-1 screening tailored ensembles that address problems in the current applications of ensemble methods to virtual screening. The proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson's disease therapeutics. .

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Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models

Lian

Fang

et al. 2015

Mol Divers

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Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with IC50 values in the range of 12.9-185.0 nM, and against H3N2 NA with IC50 values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with IC50 values ranging 39.5-63.8 μM against H1N1 NA and 44.5-114.1 μM against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays.

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

Cited by 6 publications

References 39 publications

Asprellcosides B of Ilex asprella Inhibits Influenza A Virus Infection by Blocking the Hemagglutinin- Mediated Membrane Fusion

Asprellcosides B of Ilex asprella Inhibits Influenza A Virus Infection by Blocking the Hemagglutinin- Mediated Membrane Fusion

<strong>Virtual screening tailored ensembles of QSAR models for the discovery of dual A<sub>2A</sub> Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors</strong>

Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models

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