Mechanisms of HIV fitness and drug resistance inferred from high-resolution molecular dynamics and sequence co-variation models

从高分辨率分子动力学和序列共变模型推断出 HIV 适应性和耐药性的机制

• 批准号: 10750627
• 负责人: Ronald Levy
• 金额: $ 69.11万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10750627
• 关键词: Active Sites; Affect; Affinity; Amino Acids; Antiviral Agents; Base Sequence; Benchmarking; Binding; Biophysics; Calibration; Capsid; Capsid Proteins; Chemicals; Clinical; Complement; Cryoelectron Microscopy; Data; Defect; Development; Drug resistance; Drug resistance pathway; Environment; Free Energy; Genetic Epistasis; Goals; HIV; HIV Genome; HIV Integrase; HIV Integrase Inhibitors; HIV-1; Integrase; Knowledge; Laboratories; Lead; Ligand Binding; Liquid substance; Machine Learning; Maps; Measurement; Methods; Modeling; Molecular; Molecular Conformation; Mutation; Pathway interactions; Pattern; Persons; Pharmaceutical Preparations; Physics; Play; Proteins; Protocols documentation; Publications; Resistance; Resolution; Role; Sampling; Solvents; Structure; System; Techniques; Therapeutic; Thermodynamics; Time; Viral; Viral Proteins; Water; Work; antiretroviral therapy; biophysical properties; clinically relevant; computer studies; computerized tools; drug discovery; drug resistance development; experience; experimental study; fitness; improved; inhibitor; interfacial; machine learning model; molecular dynamics; mutant; mutation screening; next generation; novel; novel therapeutics; particle; pressure; public health relevance; response; simulation; structural biology; tool

ABSTRACT There are ~40 million people world-wide infected by the Human Immunodeficiency Virus Type 1 (HIV-1, commonly referred to as HIV). As currently there is no cure, antiretroviral treatment is the primary treatment option. Yet antiretroviral treatment eventually fails over time due to the development of drug resistance. We will develop new computational tools for forecasting HIV evolutionary trajectories under therapeutic selection pressure leading to drug resistance, using high resolution all atom molecular dynamics simulations together with physics-based machine learning models of sequence co-variation. The computational studies will be complemented by structural, biophysical, and virological studies on two HIV protein multimeric targets: HIV integrase (IN), and capsid (CA). The modeling and experiments will be employed in an iterative manner, with the experimental results being used to validate, parameterize and improve the models; and the molecular dynamics simulations used to guide new experiments and also to develop new tools for high resolution cryo-EM refinement of multiple binding modes of HIV inhibitors and interfacial solvent. The common theme of our proposed work is to provide structural interpretations for the observed fitness and resistance effects of mutations, with the goal of developing holistic structure-function models which can be used to predict viral mutation trajectories under drug selection pressure and give a mechanistic explanation for them. There are three specific aims: (1) determine the physical mechanisms underlying mutational epistasis under varied drug environments, and use MD simulations and virological data to parameterize drug specific landscapes for HIV IN and CA under a novel theoretical framework; (2) use high resolution, large scale alchemical molecular dynamics free energy simulations based on advanced sampling methods to analyze the effects of protein mutations on the stability of protein-protein interfaces that constitute the intasome and the capsid particle assemblies; (3) determine the molecular basis for multiple binding modes of inhibitors of HIV IN, and the role of solvation in the strong binding of these inhibitors. These aims seek to achieve a molecular understanding of the cooperative effects (epistasis) of multi-residue mutation patterns on the binding of inhibitors to their viral protein targets (IN, CA) and their effects on the stability of the multimers (intasome and capsid particle). We anticipate that this work will lead to the development of surveillance tools to forecast the response of viral systems to the selection pressure of antiviral therapeutics.

抽象的 全球约有 4000 万人感染 1 型人类免疫缺陷病毒（HIV-1、 通常称为艾滋病毒）。由于目前尚无治愈方法，抗逆转录病毒治疗是主要治疗方法 选项。然而，随着时间的推移，由于耐药性的发展，抗逆转录病毒治疗最终会失败。我们将 开发新的计算工具来预测治疗选择下的艾滋病毒进化轨迹 压力导致耐药性，使用高分辨率全原子分子动力学模拟 基于物理的序列共变机器学习模型。计算研究将是 辅以针对两个 HIV 蛋白多聚体靶标的结构、生物物理和病毒学研究：HIV 整合酶 (IN) 和衣壳 (CA)。建模和实验将以迭代的方式进行， 实验结果用于验证、参数化和改进模型；和分子 动力学模拟用于指导新实验并开发用于高分辨率冷冻电镜的新工具 HIV抑制剂和界面溶剂的多种结合模式的细化。我们的共同主题 拟议的工作是为观察到的突变的适应性和抵抗力效应提供结构解释， 目标是开发可用于预测病毒突变的整体结构功能模型 药物选择压力下的轨迹并对其给出机械解释。具体有以下三点 目的：（1）确定不同药物环境下突变上位性的物理机制， 并使用 MD 模拟和病毒学数据参数化 HIV IN 和 CA 下的药物特异性景观 新颖的理论框架； (2)利用高分辨率、大规模炼金分子动力学自由能 基于先进采样方法的模拟，分析蛋白质突变对稳定性的影响 构成嵌体和衣壳颗粒组件的蛋白质-蛋白质界面； (3) 确定 HIV IN抑制剂多种结合模式的分子基础，以及溶剂化在强结合中的作用 这些抑制剂。这些目标寻求实现对协同效应（上位性）的分子理解 抑制剂与其病毒蛋白靶标（IN、CA）结合的多残基突变模式及其影响 多聚体（内体和衣壳颗粒）的稳定性。我们预计这项工作将导致 开发监测工具来预测病毒系统对抗病毒药物选择压力的反应 疗法。