Abstract
Influenza A virus (IAV) is a threat to public health due to its high mutation rate and resistance to existing drugs. In this investigation, 15 targets selected from an influenza virus–host interaction network were successfully constructed as a multitarget virtual screening system for new drug discovery against IAV using Naïve Bayesian, recursive partitioning, and CDOCKER methods. The predictive accuracies of the models were evaluated using training sets and test sets. The system was then used to predict active constituents of Compound Yizhihao (CYZH), a Chinese medicinal compound used to treat influenza. Twenty-eight compounds with multitarget activities were selected for subsequent in vitro evaluation. Of the four compounds predicted to be active on neuraminidase (NA), chlorogenic acid, and orientin showed inhibitory activity in vitro. Linarin, sinensetin, cedar acid, isoliquiritigenin, sinigrin, luteolin, chlorogenic acid, orientin, epigoitrin, and rupestonic acid exhibited significant effects on TNF-α expression, which is almost consistent with predicted results. Results from a cytopathic effect (CPE) reduction assay revealed acacetin, indirubin, tryptanthrin, quercetin, luteolin, emodin, and apigenin had protective effects against wild-type strains of IAV. Quercetin, luteolin, and apigenin had good efficacy against resistant IAV strains in CPE reduction assays. Finally, with the aid of Gene Ontology biological process analysis, the potential mechanisms of CYZH action were revealed. In conclusion, a compound-protein interaction-prediction system was an efficient tool for the discovery of novel compounds against influenza, and the findings from CYZH provide important information for its usage and development.
Highlights
Influenza is an acute respiratory viral infection responsible for seasonal pandemics, causing up to millions of cases of severe illness around the globe each year
Naïve Bayesian NB classifiers is a statistical approach used for categorization, which is based on the frequency of the occurrence of features that can distinguish differences between active and decoy compounds
Influenza A virus (IAV) contains a genome composed of 8 RNA segments encoding the following: surface proteins HA, NA, and M2 ion channel (M2); matrix protein 1 (M1) situated beneath the membrane; three subunits of RNA-dependent RNA polymerase (RdRp) known as polymerase basic protein 1 (PB1), polymerase basic protein 2 (PB2) and polymerase acidic protein (PA); nucleocapsid protein (NP) that coats the viral genome; non-structural protein-1 (NS1); and nuclear export protein (NEP/NS2) (Loregian et al, 2014; Brooke, 2017)
Summary
Influenza (flu) is an acute respiratory viral infection responsible for seasonal pandemics, causing up to millions of cases of severe illness around the globe each year. There are several types of anti-flu drugs available, including inhibitors of neuraminidase (NA), the M2 ion channel, and RNA-dependent RNA polymerase (RdRp), their clinical use is occasionally impeded by high levels of resistance in mutated viral strains (Zu et al, 2015; Schaduangrat et al, 2016). These targets are prone to resistance in the clinic, the development of antiviral drugs with novel modes of action are of high importance. In silico prediction tools for exploring compoundprotein interactions and biochemical mechanisms need to be developed
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