Niu Bing, Lu Yi, Wang Jianying, Hu Yan, Chen Jiahui, Chen Qin, He Guangwu, Zheng Linfeng
School of Life Sciences, Shanghai University, Shanghai 200444, China.
Department of Radiology, Shanghai First People's Hospital, Baoshan Branch, Shanghai 200940, China.
Comput Struct Biotechnol J. 2018 Dec 7;17:39-48. doi: 10.1016/j.csbj.2018.11.007. eCollection 2019.
Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q = 0.784 and r = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.
禽流感是一种严重的人畜共患传染病,对当地家禽养殖、人类健康和社会稳定具有巨大的负面影响。因此,设计新型抗禽流感化合物一直是该领域的研究重点。在本研究中,运用计算方法研究具有神经氨酸酶抑制活性的化合物。首先,构建二维结构活性关系(2D-SAR)模型以识别神经氨酸酶抑制剂(NAIs)。结果,10次交叉验证和独立测试的准确率分别为96.84%和98.97%。然后,构建Topomer CoMFA模型以预测抑制活性并分析分子场。通过改变切割方法获得了两个模型。采用第二个模型预测活性(q = 0.784,r = 0.982)。还利用分子对接进一步分析NAIs与人类和禽流感病毒神经氨酸酶之间的结合位点。结果发现,相同的结合总分存在一些差异,但结合位点基本相同。最后,筛选出一些潜在的NAIs并提出了一些优化意见。期望我们的研究能够有助于新型NAIs的研究与开发。