@article{oai:nagasaki-u.repo.nii.ac.jp:00002279,
 author = {Makau, Juliann Nzembi and Watanabe, Ken and Ishikawa, Takeshi and Mizuta, Satoshi and Hamada, Tsuyoshi and Kobayashi, Nobuyuki and Nishida, Noriyuki},
 issue = {3},
 journal = {PLOS ONE},
 month = {Mar},
 note = {Influenza viruses have acquired resistance to approved neuraminidase-targeting drugs, increasing the need for new drug targets for the development of novel anti-influenza drugs. Nucleoprotein (NP) is an attractive target since it has an indispensable role in virus replication and its amino acid sequence is well conserved. In this study, we aimed to identify new inhibitors of the NP using a structure-based drug discovery algorithm, named Nagasaki University Docking Engine (NUDE), which has been established especially for the Destination for GPU Intensive Machine (DEGIMA) supercomputer. The hit compounds that showed high binding scores during in silico screening were subsequently evaluated for anti-influenza virus effects using a cell-based assay. A 4-hydroxyquinolinone compound, designated as NUD-1, was found to inhibit the replication of influenza virus in cultured cells. Analysis of binding between NUD-1 and NP using surface plasmon resonance assay and fragment molecular orbital calculations confirmed that NUD-1 binds to NP and could interfere with NP-NP interactions essential for virus replication. Time-of-addition experiments showed that the compound inhibited the mid-stage of infection, corresponding to assembly of the NP and other viral proteins. Moreover, NUD-1 was also effective against various types of influenza A viruses including a clinical isolate of A(H1N1)pdm09 influenza with a 50% inhibitory concentration range of 1.8-2.1 μM. Our data demonstrate that the combined use of NUDE system followed by the cell-based assay is useful to obtain lead compounds for the development of novel anti-influenza drugs., PLOS ONE, 12(3), e0173582; 2017},
 title = {Identification of small molecule inhibitors for influenza a virus using in silico and in vitro approaches},
 volume = {12},
 year = {2017}
}