Qi Hangfei, Olson C Anders, Wu Nicholas C, Ke Ruian, Loverdo Claude, Chu Virginia, Truong Shawna, Remenyi Roland, Chen Zugen, Du Yushen, Su Sheng-Yao, Al-Mawsawi Laith Q, Wu Ting-Ting, Chen Shu-Hua, Lin Chung-Yen, Zhong Weidong, Lloyd-Smith James O, Sun Ren
Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America.
The Molecular Biology Institute, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS Pathog. 2014 Apr 10;10(4):e1004064. doi: 10.1371/journal.ppat.1004064. eCollection 2014 Apr.
Widely used chemical genetic screens have greatly facilitated the identification of many antiviral agents. However, the regions of interaction and inhibitory mechanisms of many therapeutic candidates have yet to be elucidated. Previous chemical screens identified Daclatasvir (BMS-790052) as a potent nonstructural protein 5A (NS5A) inhibitor for Hepatitis C virus (HCV) infection with an unclear inhibitory mechanism. Here we have developed a quantitative high-resolution genetic (qHRG) approach to systematically map the drug-protein interactions between Daclatasvir and NS5A and profile genetic barriers to Daclatasvir resistance. We implemented saturation mutagenesis in combination with next-generation sequencing technology to systematically quantify the effect of every possible amino acid substitution in the drug-targeted region (domain IA of NS5A) on replication fitness and sensitivity to Daclatasvir. This enabled determination of the residues governing drug-protein interactions. The relative fitness and drug sensitivity profiles also provide a comprehensive reference of the genetic barriers for all possible single amino acid changes during viral evolution, which we utilized to predict clinical outcomes using mathematical models. We envision that this high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.
广泛应用的化学遗传学筛选极大地促进了许多抗病毒药物的鉴定。然而，许多治疗候选药物的相互作用区域和抑制机制尚未阐明。先前的化学筛选确定了达卡他韦（BMS-790052）是一种针对丙型肝炎病毒（HCV）感染的强效非结构蛋白5A（NS5A）抑制剂，但其抑制机制尚不清楚。在此，我们开发了一种定量高分辨率遗传学（qHRG）方法，以系统地绘制达卡他韦与NS5A之间的药物-蛋白质相互作用图谱，并描绘达卡他韦耐药性的遗传屏障。我们将饱和诱变与下一代测序技术相结合，系统地量化药物靶向区域（NS5A的结构域IA）中每个可能的氨基酸取代对复制适应性和对达卡他韦敏感性的影响。这使得能够确定控制药物-蛋白质相互作用的残基。相对适应性和药物敏感性图谱还为病毒进化过程中所有可能的单氨基酸变化的遗传屏障提供了全面的参考，我们利用这些参考通过数学模型预测临床结果。我们设想，这种高分辨率分析方法将有助于下一代药物开发，以选择具有更高耐药适应性成本的药物，也有助于根据患者的病毒变异谱合理使用药物。
