Evolution of antiviral resistance mutations and their biological and biophysical implications

抗病毒耐药突变的演变及其生物学和生物物理意义

• 批准号: 10242909
• 负责人: Ronald Levy
• 金额: $ 117.56万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242909
• 关键词: Address; Affect; Amino Acid Sequence; Amino Acids; Antiretroviral resistance; Antiviral resistance; Bar Codes; Biochemical; Bioinformatics; Biological; Biological Assay; Biophysics; Capsid; Characteristics; Clinical; Collaborations; Complement; Data; Development; Drug Modelings; Drug resistance; Enzymes; Evolution; Failure; Funding; Gag PR; Genetic; Goals; HIV; HIV Infections; HIV-1; Individual; Integrase; Integration Host Factors; Investigation; Lead; Length; Link; Linkage Disequilibrium; Location; Measures; Methodology; Modeling; Mutation; National Institute of General Medical Sciences; Nature; Patients; Pattern; Peptide Hydrolases; Pharmaceutical Preparations; Pharmacotherapy; Positioning Attribute; Probability; Proteins; RNA-Directed DNA Polymerase; Resistance; Resistance development; Risk; Role; Sampling; Sequence Alignment; Statistical Methods; Statistical Models; Structure; Technology; Time; Treatment Failure; Validation; Viral; Viral Proteins; Virus; antiretroviral therapy; base; database structure; deep sequencing; experience; fitness; gag Gene Products; inhibitor/antagonist; innovative technologies; insight; kinetic model; next generation sequencing; novel; patient population; pol Gene Products; pressure; prospective; resistance mutation; response; single molecule; small molecule; success; therapy resistant; treatment response; viral fitness; virology

ABSTRACT HIV-1 under antiretroviral treatment selects for genetically-linked mutations that are correlated due to constraints on protein structural stability and function, which contribute to fitness. Project 5 studies are concerned with analyzing pairs (or higher-order) patterns of antiretroviral resistance mutations and their combined biophysical, biochemical, and structural effects on drug-resistance and viral fitness. During the past funding period, new statistical methods were developed to identify correlative mutational patterns present in genetically unlinked Gag and protease deep sequencing data. Potts Hamiltonian probabilistic models were constructed from protease sequence alignments to identify mutational patterns that lead to drug-resistance. To extend the past findings, it is proposed to identify genetically-linked patterns of antiretroviral resistance mutations from full-length, individual viruses from clade B or non-clade B HIV-infected patients during antiretroviral treatment. To investigate structural constraints in HIV proteins that influence selection of resistance mutations, Potts models of protein sequence covariation will be developed utilizing sequence and structural data. The combination of a novel full-length sequencing approach and virology expertise by Torbett will be complemented by bioinformatics and modeling expertise of Levy to serve the following specific aims: 1) Identify genetically-linked drug-resistance mutations (pairs or higher order) from HIV in longitudinal patient samples utilizing Multi-read Barcode-Assisted Single Molecule Sequencing (MrBASMS). Covariant mutations will be functionally and structurally characterized using previously described biochemical, biophysical and virological assays to validate their role in the rise of drug resistance. 2) Both full-length, from 1), and HIV sequence data from databases and structural information will be utilized to construct Potts models of drug naïve and drug-experienced protease, reverse transcriptase, integrase and Gag. Potts models will be used to investigate the effects of epistatic mutational combinations on fitness, as well as predict HIV protein residues at risk for drug-resistance mutation development. These studies will provide critical insight into HIV genetic barriers that must be overcome to develop resistance to multiple inhibitor combinations. The MrBASMS sequencing of HIV quasispecies from longitudinal patient samples will be led by Torbett and Sarafianos, along with outside collaborator Routh (UTMB). The biochemical, structural and virological validation of mutational covariants will be led by Torbett, Sarafianos and Levy, along with assistance from Core 2. Levy will develop Potts models from HIV sequence data and protein structural information obtained from Projects 1, 2, and Core 1.

抽象的 抗逆转录病毒治疗下的 HIV-1 选择与遗传相关的突变，这些突变与以下因素相关： 蛋白质结构稳定性和功能的限制，有助于项目 5 的研究。 涉及分析抗逆转录病毒耐药突变的成对（或高阶）模式及其 过去，生物物理、生化和结构对耐药性和病毒适应性的综合影响。 资助期间，开发了新的统计方法来识别存在的相关突变模式 遗传上不相关的 Gag 和蛋白酶深度测序数据是 Potts Hamiltonian 概率模型。 由蛋白酶序列比对构建，以识别导致耐药性的突变模式。 为了扩展过去的发现，建议确定抗逆转录病毒耐药性的遗传相关模式 来自 B 分支或非 B 分支 HIV 感染者的全长单个病毒的突变 研究影响 HIV 蛋白选择的结构限制。 抗性突变，蛋白质序列共变的 Potts 模型将利用序列和 Torbett 的新型全长测序方法和病毒学专业知识的结合。 将得到 Levy 的生物信息学和建模专业知识的补充，以服务于以下具体目标： 1) 识别纵向患者中 HIV 的基因相关耐药突变（成对或更高阶） 利用多重读取条形码辅助单分子测序 (MrBASMS) 的样本。 突变将使用先前描述的生物化学在功能和结构上进行表征， 生物物理和病毒学测定，以验证它们在耐药性上升中的作用。 2) 来自 1) 的全长序列数据和来自数据库的 HIV 序列数据和结构信息将用于 构建未接触过药物和经历过药物的蛋白酶、逆转录酶、整合酶和 Gag.Potts 模型将用于研究上位突变组合对适应性的影响，如 以及预测存在耐药突变发展风险的 HIV 蛋白残基。 提供对艾滋病毒遗传障碍的重要见解，必须克服这些障碍才能对多种病毒产生抵抗力 抑制剂组合。 对纵向患者样本中的 HIV 准种进行 MrBASMS 测序将由 Torbett 和 Sarafianos 与外部合作者 Routh (UTMB) 进行生化、结构和病毒学研究。 突变协变体的验证将由 Torbett、Sarafianos 和 Levy 领导，并得到来自 核心2. Levy将根据获得的HIV序列数据和蛋白质结构信息开发Potts模型 来自项目 1、2 和核心 1。