COMPUTATIONAL AND BIOINFORMATIC MODELING AND ANALYSIS OF HIV DRUG RESISTANCE

这项研究的长期目标是开发一个有效的和可扩展的计算模型 艾滋病毒药物治疗有助于抗击日益普遍的耐药性问题。利用一种 从原子水平的分子协同进化到患者的生物信息学分析的方法层次 数据，我们将描述病毒在药物选择压力和压力下可用的突变情况 开发基于结构的设计方法，可以有效地生产抑制剂和治疗策略 战败逃脱。我们的工作将主要集中在具有良好特性的HIV蛋白酶上，但也将扩展到其他 用HIV药物靶标来测试我们方法的通用性。在这项研究中，我们将评估两个基本的 假设： 1)存在相对较少数量的可定义类别的艾滋病毒蛋白酶突变，从而 给定类别内的突变株将对给定的抑制剂表现出很强的交叉抗性，而在 不同的阶级不会表现出交叉抗性。 2)基于片段的设计与蛋白质柔韧性的计算建模相结合将识别 扩大蛋白水解酶“靶向空间”的抑制剂，包括鉴定外显子和设计 柔韧的楔形物，可以阻止HIV蛋白酶的关键功能运动。 与项目2中进行的基于碎片的结晶学研究和 在项目3中，我们将开发和应用计算方法 基于片段的药物设计。利用HIV蛋白酶野生型和突变型OUR的动力学模型 FightAIDS@Home Internet分布式计算网络将提供所需的计算能力 筛选大片段文库并评估有希望的链接片段的对接。基于组合 项目4的体外耐药性演变的实验结果和患者治疗数据的时间进程 从核心C开始，通过运行和分析大量计算协同进化实验，我们将 描述可能突变的空间，将可能可行的突变划分为离散的范围 结构类，并确定每类中用于药物设计的关键突变结构。我们会 开发包含这些多个数据来源的病毒适应性模型，然后使用这些模型 在药物治疗过程中对病毒进化进行更大规模的模拟。

The long term goal of this research is to develop an effective and extensible computational model of HIV drug therapy to help in fighting the increasingly prevalent problem of drug resistance in. Utilizing a hierarchy of methods that range from atomic level molecular co-evolution to bioinformatics analysis of patient data, we will characterize the mutational landscape available to the virus under drug selection pressure and develop structure-based design methods that can produce inhibitors and therapeutic strategies to efficiently defeat escape. Our work will focus primarily on the well-characterized HIV protease, but will extend to other HIV drug targets to test the generality of our methods. In this research we will evaluate two basic hypotheses: 1) There exists a relatively small number of definable classes of HIV protease mutations, such that mutants within a given class will show strong cross resistance to a given inhibitor, and mutants in different classes wil not show cross-resistance. 2) Fragment-based design combined with computational modeling of protein flexibility will identify inhibitors that expand the protease "target space" including identification of exosites and design of flexibility wedges that block critical functional motions of HIV protease. Working in conjunction with the fragment-based crystallographic studies undertaken in Project 2 and the combinatorial chemical syntheses in Project 3 we will develop and apply computational methods for fragment-based drug design. Utilizing dynamic models of HIV protease wild type and mutants our FightAIDS@Home Internet distributed computing network will provide the needed computational power to screen large fragment libraries and evaluate dockings of promising linked fragments. Based on combination of experimental results on ex vivo resistance evolution from Project 4 and time course patient treatment data from Core C and by running and analyzing massive computational coevolution experiments, we will characterize the space of possible mutants, separating the range of possible viable mutants into discrete structural classes and identifying key mutant structures within each class for use in drug design. We will develop models of viral fitness that incorporate these multiple sources of data, and then use these models within larger simulations of viral evolution during the course of drug therapy.