PDF(Pubmed)
Abstract：
This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.
摘要：
这项研究描述了两种互补的方法，使用基于网络和序列相似性工具来确定药物再利用机会预测调节病毒蛋白。这种方法可以迅速适应新的和新兴的病毒。第一种方法构建并研究了病毒-宿主-物理相互作用网络；药物靶蛋白的三层多模态网络，人类蛋白质-蛋白质相互作用，和病毒-宿主蛋白质-蛋白质相互作用。第二种方法评估了病毒蛋白和其他蛋白之间的序列相似性，通过构建病毒-宿主-相似性交互网络进行可视化。方法在人类免疫缺陷病毒上进行了验证，乙型肝炎，丙型肝炎,和人乳头瘤病毒，然后部署在SARS-CoV-2上。病毒-宿主-物理相互作用预测与已知抗病毒药物的AUC分别为0.69、0.59、0.78和0.67，反映出分数是有效药物的预测。对于SARS-CoV-2，预测了569种候选药物，其中37例纳入SARS-CoV-2的临床试验（AUC=0.75，P值3.21×10-3）。作为进一步的验证，分析了排名靠前的候选抗病毒药物与蛋白质靶标的结合情况;BindScope产生的结合评分表明成功率为70%.
