Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method - Liang, Hang; Zhang, Li; Wang, Lina; Gao, Man; Meng, Xiangfeng; Li, Mengyao; Liu, Junhui; Li, Wei; Meng, Fanzheng (Frontiers in Physiology, 2021)
Influenza A viruses, especially H3N2 and H1N1 subtypes, are viruses that often spread among humans and cause influenza pandemic. There are several big influenza pandemics causing millions of human deaths in history and the threat of influenza viruses to public health is still serious nowadays due to the frequent antigenic drift and antigenic shift events. However, only few effective anti-flu drugs have been developed to date. The high development cost, long research & development time and drug side effects are the major bottlenecks, which could be relieved by drug repositioning. In this study, we proposed a novel antiviral Drug Repositioning method based on minimizing Matrix Nuclear Norm (DRMNN). Specifically, a virus-drug correlation database consisting of 34 viruses and 205 antiviral drugs was first curated from public databases and published literatures. Together with drug similarity on chemical structure and virus sequence similarity, we formulated the drug repositioning problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. DRMNN was compared with 3 recent association prediction algorithms. The AUC of DRMNN in the global 5-fold cross-validation (5-fold CV) is 0.8661, and the AUC in the local leave-one-out cross-validation (LOOCV) is 0.6929. Experiments have shown that DRMNN is better than other algorithms in predicting the drugs against influenza A virus. Taking H3N2 as an example, 10 drugs most likely to be effective against H3N2 viruses were listed, among which 6 drugs were reported to have some effects on the viruses in other literatures. The protein docking experiments between the chemical structure of the prioritized drugs and viral hemagglutinin protein also provided evidence for the potential of the predicted drugs in treating influenza.