Guo Fei-Fei, Zhang Yu-Qi, Tang Shi-Huan, Tang Xuan, Xu He, Liu Zhong-Yang, Huo Rui-Li, Li Dong, Yang Hong-Jun
Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700, China.
Hebei University Baoding 071002, China State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences (The PHOENIX Center,Beijing) Beijing 102206, China.
Zhongguo Zhong Yao Za Zhi. 2020 May;45(10):2257-2264. doi: 10.19540/j.cnki.cjcmm.20200312.401.
There is urgent need to discover effective traditional Chinese medicine(TCM) for treating coronavirus disease 2019(COVID-19). The development of a bioinformatic tool is beneficial to predict the efficacy of TCM against COVID-19. Here we deve-loped a prediction platform TCMATCOV to predict the efficacy of the anti-coronavirus pneumonia effect of TCM, based on the interaction network imitating the disease network of COVID-19. This COVID-19 network model was constructed by protein-protein interactions of differentially expressed genes in mouse pneumonia caused by SARS-CoV and cytokines specifically up-regulated by COVID-19. TCMATCOV adopted quantitative evaluation algorithm of disease network disturbance after multi-target drug attack to predict potential drug effects. Based on the TCMATCOV platform, 106 TCM were calculated and predicted. Among them, the TCM with a high disturbance score account for a high proportion of the classic anti-COVID-19 prescriptions used by clinicians, suggesting that TCMATCOV has a good prediction ability to discover the effective TCM. The five flavors of Chinese medicine with a disturbance score greater than 1 are mainly spicy and bitter. The main meridian of these TCM is lung, heart, spleen, liver, and stomach meridian. The TCM related with QI and warm TCM have higher disturbance score. As a prediction tool for anti-COVID-19 TCM prescription, TCMATCOV platform possesses the potential to discovery possible effective TCM against COVID-19.
迫切需要发现治疗新型冠状病毒肺炎(COVID-19)的有效中药。开发一种生物信息学工具有助于预测中药对COVID-19的疗效。在此,我们基于模仿COVID-19疾病网络的相互作用网络,开发了一个预测平台TCMATCOV,用于预测中药抗冠状病毒肺炎的疗效。该COVID-19网络模型由严重急性呼吸综合征冠状病毒(SARS-CoV)引起的小鼠肺炎中差异表达基因的蛋白质-蛋白质相互作用以及COVID-19特异性上调的细胞因子构建而成。TCMATCOV采用多靶点药物攻击后疾病网络扰动的定量评估算法来预测潜在的药物效果。基于TCMATCOV平台,对106种中药进行了计算和预测。其中,扰动评分高的中药在临床医生使用的经典抗COVID-19处方中占比很高,这表明TCMATCOV在发现有效中药方面具有良好的预测能力。扰动评分大于1的中药五味主要为辛味和苦味。这些中药的主要归经为肺经、心经、脾经、肝经和胃经。与气和温性相关的中药具有较高的扰动评分。作为抗COVID-19中药处方的预测工具,TCMATCOV平台具有发现可能有效抗COVID-19中药的潜力。