Evolution of antiviral resistance mutations and their biological and biophysical implications

摘要 接受抗逆转录病毒治疗的HIV-1选择与以下因素相关的遗传连锁突变： 限制蛋白质的结构稳定性和功能，这有助于健身。项目5研究是 涉及分析抗逆转录病毒耐药突变的成对（或更高阶）模式及其 结合生物物理、生物化学和结构对耐药性和病毒适应性的影响。在过去 在资助期间，开发了新的统计方法来确定存在于 遗传上未关联的Gag和蛋白酶深度测序数据。Potts Hamilton概率模型是 通过蛋白酶序列比对构建，以鉴定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的遗传连锁模式 来自进化枝B或非进化枝B HIV感染患者的全长单个病毒的突变， 抗逆转录病毒治疗为了研究HIV蛋白质中影响选择的结构限制， 耐药突变，Potts模型的蛋白质序列共变异将开发利用序列和 结构数据Torbett的新型全长测序方法和病毒学专业知识的结合 将由Levy的生物信息学和建模专业知识进行补充，以实现以下具体目标： 1)识别纵向患者中HIV的遗传连锁耐药突变（成对或更高阶） 利用多读段条形码辅助单分子测序（MrBASMS）对样品进行测序。协变 突变将使用先前描述的生物化学， 生物物理学和病毒学分析，以验证它们在耐药性上升中的作用。 2)来自1）的全长和来自数据库的HIV序列数据和结构信息将用于 构建未经药物治疗和药物治疗的蛋白酶、逆转录酶、整合酶和 恶心Potts模型将用于研究上位突变组合对适应性的影响，如 并预测HIV蛋白质残基对耐药性突变发展的风险。这些研究将 提供关键的洞察艾滋病毒的遗传障碍，必须克服发展耐药性的多种 抑制剂组合。 来自纵向患者样本的HIV准种的MrBASMS测序将由Torbett领导， 萨拉菲亚诺斯，沿着与外部合作者劳斯（UTMB）。生物化学，结构和病毒学 突变协变的验证将由Torbett、Sarafianos和Levy领导，沿着由 核心2。Levy将从HIV序列数据和获得的蛋白质结构信息中开发Potts模型 项目1、2和核心1。

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.