Development and application of a quantitive model for HIV-1 transcriptional activation driven by TAR RNA conformational dynamics

TAR RNA构象动力学驱动的HIV-1转录激活定量模型的开发和应用

• 批准号: 10750552
• 负责人: Hashim M Al-Hashimi
• 金额: $ 84.6万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-17 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10750552
• 关键词: Acceleration; Affinity; Arginine; Automobile Driving; Base Sequence; Behavior; Binding; Binding Proteins; Biochemical; Biochemistry; Biological Process; Biophysics; Cell model; Cell physiology; Cells; Communities; Complex; Computer Models; Data; Development; Dissection; Elements; Feedback; Future; Genetic Transcription; Genome; HIV-1; Human; In Vitro; Libraries; Link; Maps; Measurement; Measures; Mediating; Methods; Modeling; Molecular; Molecular Computations; Molecular Conformation; Process; Property; Protein Conformation; Proteins; Public Health; RNA; RNA Conformation; RNA Databases; RNA Recognition Motif; RNA Sequences; RNA-Binding Proteins; RNA-Protein Interaction; Research; Resolution; Rest; Role; Science; Structure; Technology; Testing; Therapeutic Intervention; Thermodynamics; Trans-Activators; Transactivation; Transcriptional Activation; Variant; Viral; Viral Proteins; Virus Replication; Work; biophysical properties; cost; design; driving force; experimental study; improved; molecular dynamics; prediction algorithm; predictive modeling; protein complex; protein data bank; small molecule; three dimensional structure; viral RNA

Project Summary Many processes essential for HIV-1 viral replication are driven by the association of regulatory RNA elements in the retroviral genome and host/viral proteins that form biologically active complexes. Despite advances in solving the 3D structures of RNA-protein complexes and in measuring RNA-protein interactions in vitro and in cells, our current understanding of RNA-protein interactions is qualitative and not quantitative, descriptive, not predictive. Attaining a quantitative and predictive understanding is necessary to reveal the forces and conformational states driving viral processes and to fully define the landscape of opportunities for therapeutic intervention. The overarching hypothesis of this proposal is that the cellular transcriptional activity of HIV-1 TAR RNA can be predicted from its sequence based on its conformational propensity to form the binding-competent conformation and its affinity for the transactivator protein Tat and the human super elongation complex (SEC) Tat:SEC. The project will (i) develop a suite of technologies to obtain experimental data on RNA ensembles, RNA-protein interactions, and cellular activity quantitatively in high throughput over a large and common expanse of RNA sequence and structure space (ii) closely integrate NMR data with computational molecular dynamics (MD) simulations and empirical RNA structure prediction algorithms (FARFAR) to determine RNA ensembles free and bound to proteins and to test and guide refinement of the computational models through a community wide effort; and (iii) test and refine a thermodynamic model predicting cellular function that dissects TAR•Tat:SEC binding energetics into contributions from intermolecular contacts and conformational propensities. From a common library of 1000s of TAR RNA variants, Aim 1 will determine conformational propensities and measure binding affinities to Tat and Tat:SEC across solution conditions and measure transcriptional activation in cells and with Tat concentration varied. The data will be used to develop a quantitative and predictive model for cellular transcriptional activation based on in vitro measurements and iteratively refine the model. Aim 2 will integrate NMR data with MD simulations and FARFAR; determine atomic-resolution ensembles for 20 TAR variants, free and bound to the Tat RNA binding domain; use the ensembles to define the bound conformational states and refine conformational propensities; identify strengths and weaknesses of MD and FARFAR; and develop and test a new method (FARFAR-CS) for determining RNA ensembles and use it to refine propensities for 100s of TAR variants. Aim 3 will extend the model to include alternative secondary structure propensities, use NMR experiments to measure these propensities for 100s of TAR variants, and extend the model to include binding of 20 small molecules and competition with 7SK RNA for 1000s of RNA variants. When completed, this project will make it possible to quantitatively predict cellular transcriptional activity from TAR sequences, will reveal the profound contribution of conformational propensities to RNA-protein binding, and will provide a roadmap for future efforts that link biochemical and biophysical properties to molecular behavior and function in cells.

项目概要 HIV-1 病毒复制所必需的许多过程是由调控 RNA 元件的关联驱动的 尽管在解决问题方面取得了进展，但形成生物活性复合物的逆转录病毒基因组和宿主/病毒蛋白。 RNA-蛋白质复合物的 3D 结构以及在体外和细胞内测量 RNA-蛋白质相互作用，我们的 目前对 RNA-蛋白质相互作用的理解是定性的，而不是定量的，是描述性的，而不是预测性的。 获得定量和预测性的理解对于揭示力和构象状态是必要的 推动病毒过程并充分界定治疗干预的机会前景。 该提议的总体假设是 HIV-1 TAR RNA 的细胞转录活性可以 根据其形成具有结合能力的构象的构象倾向从其序列预测 及其对反式激活蛋白 Tat 和人超伸长复合物 (SEC) Tat:SEC 的亲和力。 项目将 (i) 开发一套技术来获取 RNA 整体、RNA-蛋白质的实验数据 在大范围的常见 RNA 上以高通量定量相互作用和细胞活动 序列和结构空间 (ii) 将 NMR 数据与计算分子动力学 (MD) 紧密结合 模拟和经验 RNA 结构预测算法 (FARFAR) 来确定自由的 RNA 整体 与蛋白质结合，并通过社区范围的努力测试和指导计算模型的改进； (iii) 测试和完善预测细胞功能的热力学模型，剖析 TAR•Tat:SEC 结合 将能量转化为分子间接触和构象倾向的贡献。 包含数千个 TAR RNA 变体的文库，目标 1 将确定构象倾向并测量结合 在不同溶液条件下对 Tat 和 Tat:SEC 的亲和力，并测量细胞中的转录激活 Tat 浓度各不相同，这些数据将用于开发细胞的定量和预测模型。 基于体外测量的转录激活和迭代完善 Aim 2 将整合模型。 通过 MD 模拟和 FARFAR 获得 NMR 数据；免费确定 20 个 TAR 变体的原子分辨率系综 并结合到 Tat RNA 结合结构域；使用集合来定义结合的构象状态和 完善构象倾向；确定 MD 和 FARFAR 的优点和缺点并进行开发和测试； 一种用于确定 RNA 整体的新方法 (FARFAR-CS)，并用它来细化数百个 TAR 的倾向 目标 3 将扩展模型以包括替代二级结构倾向，使用 NMR。 测量数百个 TAR 变体的这些倾向的实验，并将模型扩展到包括结合 20 个小分子并与 7SK RNA 竞争数千个 RNA 变体，该项目完成后。 将使从 TAR 序列定量预测细胞转录活性成为可能，将揭示 构象倾向对 RNA-蛋白质结合的广泛贡献，并将为未来提供路线图 将生物化学和生物物理特性与细胞中的分子行为和功能联系起来。