COMPUTATIONAL AND BIOINFORMATIC MODELING AND ANALYSIS OF HIV DRUG RESISTANCE

HIV 耐药性的计算和生物信息学建模与分析

• 批准号: 7434203
• 负责人: ARTHUR J. OLSON
• 金额: $ 48.57万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-02-18 至 2012-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7434203
• 关键词: AIDS therapy; Acquired Immunodeficiency Syndrome; Active Sites; Binding; Binding Sites; Bioinformatics; Biological; Chemistry; Class; Clinical; Clinical Data; Computer Simulation; Computing Methodologies; Crystallization; Data; Data Set; Data Sources; Databases; Development; Disease; Docking; Drug Delivery Systems; Drug Design; Drug Interactions; Drug resistance; Drug-sensitive; Endopeptidases; Environment; Enzymes; Epidemic; Evolution; Extinction (Psychology); Freezing; Goals; Growth; HIV; HIV Protease; HIV drug resistance; HIV-1 protease; Highly Active Antiretroviral Therapy; Individual; Informatics; Internet; Intervention; Libraries; Link; Medical; Methods; Modeling; Molecular; Molecular Conformation; Motion; Mutate; Mutation; Numbers; Patients; Pattern; Peptide Hydrolases; Pharmaceutical Preparations; Pharmacotherapy; Planets; Play; Pliability; Population; Prevalence; Principal Investigator; Process; Proteins; Range; Recording of previous events; Research; Resistance; Resources; Role; Running; Side; Site; Source; Structure; Surgical Flaps; System; Testing; Therapeutic; Time; Training; Treatment Protocols; Viral; Virus; Work; base; chemical synthesis; cluster computing; cohort; combinatorial; concept; design; fight against; fighting; fitness; inhibitor/antagonist; markov model; mutant; novel strategies; predictive modeling; pressure; programs; research study; response; simulation; size; treatment planning

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.

这项研究的长期目标是开发一个有效且可扩展的计算模型 艾滋病毒药物疗法有助于抵抗日益普遍的耐药性问题。 从原子水平分子共同进化到患者的生物信息学分析的方法的层次结构 数据，我们将在药物选择压力和 开发基于结构的设计方法，可以产生抑制剂和治疗策略以有效 击败逃脱。我们的工作将主要关注良好的艾滋病毒蛋白酶，但会扩展到其他 HIV药物靶标测试我们方法的一般性。在这项研究中，我们将评估两个基本 假设： 1）存在相对少数可确定类的HIV蛋白酶突变，这样 给定类别内的突变体将显示出对给定抑制剂的强烈抗抗性，而突变体中的突变体 不同的班级将不显示交叉抗性。 2）基于碎片的设计与蛋白质灵活性的计算建模相结合将确定 扩展蛋白酶“目标空间”的抑制剂，包括鉴定外粒子和设计 柔韧性楔形块阻止HIV蛋白酶的关键功能运动。 与项目2中进行的基于碎片的晶体学研究一起工作 在项目3中，我们将开发和应用计算方法的组合化学合成。 基于碎片的药物设计。利用HIV蛋白酶野生型和突变体的动态模型我们 Fighataids@Home Internet分发计算网络将为您提供所需的计算能力 筛选大型碎片库，并评估有希望的链接片段的对接。基于组合 从项目4和时间课程患者治疗数据中进行体内耐药性演变的实验结果 从核心C到运行和分析大规模的计算协同进化实验，我们将 表征可能的突变体的空间，将可能的活体突变体的范围分开为离散 结构类别并识别每个类中的关键突变结构，以用于药物设计。我们将 开发病毒适应性的模型，这些模型包含这些多个数据源，然后使用这些模型 在药物治疗过程中，在较大的病毒进化模拟中。