Institute of Biology, Eötvös Loránd University, Budapest, Hungary.
PLoS Comput Biol. 2013;9(6):e1003103. doi: 10.1371/journal.pcbi.1003103. Epub 2013 Jun 6.
Proteolytic processing of Gag and Gag-Pol polyproteins by the viral protease (PR) is crucial for the production of infectious HIV-1, and inhibitors of the viral PR are an integral part of current antiretroviral therapy. The process has several layers of complexity (multiple cleavage sites and substrates; multiple enzyme forms; PR auto-processing), which calls for a systems level approach to identify key vulnerabilities and optimal treatment strategies. Here we present the first full reaction kinetics model of proteolytic processing by HIV-1 PR, taking into account all canonical cleavage sites within Gag and Gag-Pol, intermediate products and enzyme forms, enzyme dimerization, the initial auto-cleavage of full-length Gag-Pol as well as self-cleavage of PR. The model allows us to identify the rate limiting step of virion maturation and the parameters with the strongest effect on maturation kinetics. Using the modelling framework, we predict interactions and compensatory potential between individual cleavage rates and drugs, characterize the time course of the process, explain the steep dose response curves associated with PR inhibitors and gain new insights into drug action. While the results of the model are subject to limitations arising from the simplifying assumptions used and from the uncertainties in the parameter estimates, the developed framework provides an extendable open-access platform to incorporate new data and hypotheses in the future.
Gag 和 Gag-Pol 多蛋白的蛋白水解加工由病毒蛋白酶(PR)完成,这对产生感染性 HIV-1 至关重要,病毒 PR 的抑制剂是当前抗逆转录病毒疗法的重要组成部分。该过程具有多个层次的复杂性(多个切割位点和底物;多种酶形式;PR 自身加工),这需要采用系统级方法来确定关键弱点和最佳治疗策略。在这里,我们提出了第一个完整的 HIV-1 PR 蛋白水解加工的全反应动力学模型,该模型考虑了 Gag 和 Gag-Pol 内的所有规范切割位点、中间产物和酶形式、酶二聚化、全长 Gag-Pol 的初始自身切割以及 PR 的自身切割。该模型使我们能够确定病毒成熟的限速步骤以及对成熟动力学影响最强的参数。使用建模框架,我们预测了个体切割率与药物之间的相互作用和补偿潜力,描述了该过程的时间过程,解释了与 PR 抑制剂相关的陡峭剂量反应曲线,并深入了解了药物作用。虽然模型的结果受到所使用的简化假设和参数估计不确定性的限制,但所开发的框架为将来纳入新数据和假设提供了一个可扩展的开放访问平台。