Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA.
The McGowan Institute for Regenerative Medicine (MIRM), University of Pittsburgh, Pittsburgh, PA 15260, USA.
Viruses. 2020 Sep 26;12(10):1087. doi: 10.3390/v12101087.
In a short time, the COVID-19 pandemic has left the world with over 25 million cases and staggering death tolls that are still rising. Treatments for SARS-CoV-2 infection are desperately needed as there are currently no approved drug therapies. With limited knowledge of viral mechanisms, a network controllability method of prioritizing existing drugs for repurposing efforts is optimal for quickly moving through the drug approval pipeline using limited, available, virus-specific data. Based on network topology and controllability, 16 proteins involved in translation, cellular transport, cellular stress, and host immune response are predicted as regulators of the SARS-CoV-2 infected cell. Of the 16, eight are prioritized as possible drug targets where two, PVR and SCARB1, are previously unexplored. Known compounds targeting these genes are suggested for viral inhibition study. Prioritized proteins in agreement with previous analysis and viral inhibition studies verify the ability of network controllability to predict biologically relevant candidates.
在短时间内,COVID-19 大流行已使全球出现超过 2500 万例病例和惊人的死亡人数,且死亡人数仍在上升。由于目前尚无批准的药物疗法,因此急需治疗 SARS-CoV-2 感染的方法。由于对病毒机制的了解有限,使用现有的药物重新用于治疗的网络可控性方法是一种最佳方法,可利用有限的、可用的、针对病毒的特定数据快速通过药物批准渠道。基于网络拓扑结构和可控性,预测了参与翻译、细胞运输、细胞应激和宿主免疫反应的 16 种蛋白质是 SARS-CoV-2 感染细胞的调节剂。在这 16 种蛋白质中,有 8 种被优先考虑作为可能的药物靶点,其中两种,即 PVR 和 SCARB1,以前尚未被探索过。针对这些基因的已知化合物被建议用于病毒抑制研究。与先前分析和病毒抑制研究一致的优先蛋白质验证了网络可控性预测生物学上相关候选物的能力。