Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpose of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature-based 3D pharmacophoremodels were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacophore model (Hypo1), which consists of two hydrogen-bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys134 and Lys137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the top ranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.
ASJC Scopus subject areas
- 化學 (全部)