In Vitro Anti-viral Activities and Structure-activity Relationship Studies of Flavones and Dihydroflavone Derivatives as Influenza Virus Potential Neuraminidase Inhibitors

被引:0
|
作者
Sun Jia-Ying [1 ]
He Yun-Qing [1 ]
Du Hui-Rong [1 ]
Liu Chang-Lu [1 ]
Chen An-Yin [1 ]
Mei Hu [2 ]
机构
[1] Sichuan Univ Arts & Sci, Dept Chem & Chem Engn, Dazhou 635000, Sichuan, Peoples R China
[2] Chongqing Univ, Coll Bioengn, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金;
关键词
neuraminidase inhibitors; activities; flavones and dihydroflavones; docking; QSAR; EMERGENCY-USE AUTHORIZATION; SCORING FUNCTION; LIGANDS; QSAR; VIEW;
D O I
暂无
中图分类号
O61 [无机化学];
学科分类号
070301 ; 081704 ;
摘要
From the bioassay tests, 14 neuraminidase inhibitors (NIs) flavones and dihydroflavones derivatives from natural plants displayed different degree of inhibitory activities. Furthermore, compounds No. 8 and 14 showed good inhibitory activity against influenza A virus with IC50 = 835.4 and 860.6 mu g/mL. Then, to investigate interactions between NIs and neuraminidase (NA), molecular docking was performed. Docking results indicated that Arg118, Asp151, Arg292 and Arg371 were the key residues in the active pocket of 2ht8. Main influencing factors of interactions between NIs and NA were hydrogen bond and electrostatic, then hydrophobic factor. Moreover, experimental activities of NIs were consistent with total scores of the docking. In order to understand the chemical-biological interactions governing their activities toward NA, QSAR models of 14 NIs were developed. The obtained HQSAR (hologram quantitative structure activity relationship), PLS (partial least squares) and SRA (stepwise regression analysis) models were robust and had good exterior predictive capabilities. Moreover, squared multiple correlation coefficients (R-2) and squared cross-validated correlation coefficients (Q(2)) of HQSAR and PLS models based on descriptors by Gaussian and Sarchitect were 0.832 and 0.721, 0.925 and 0.688, 0.892 and 0.692, respectively. R-2 and SE (standard error) of SRA model based on descriptors by Gaussian were 0.922 and 0.072. Therefore, these models may be further used to design and evaluate the bioactivity of new compounds.
引用
收藏
页码:1641 / 1651
页数:11
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