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.

项目摘要 HIV-1病毒复制所必需的许多过程是由HIV-1基因中调控RNA元件的结合驱动的。 逆转录病毒基因组和形成生物活性复合物的宿主/病毒蛋白质。尽管在解决 RNA-蛋白质复合物的3D结构以及在体外和细胞中测量RNA-蛋白质相互作用， 目前对RNA-蛋白质相互作用的理解是定性的，而不是定量的，描述性的，而不是预测性的。 获得定量和预测性的理解是必要的，以揭示力和构象状态 驱动病毒过程，并充分定义治疗干预的机会前景。的 这一提议的首要假设是，HIV-1 TAR RNA的细胞转录活性可以被 基于其形成结合活性构象的构象倾向，从其序列预测 及其对反式激活蛋白达特和人超延伸复合物（SEC）的亲和力达特：SEC。的 该项目将（i）开发一套技术，以获得关于RNA集合、RNA蛋白质 相互作用和细胞活性定量地在大的和共同的RNA扩展的高通量 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），以确定RNA系综自由， 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; 和（iii）测试和改进预测细胞功能的热力学模型，其剖析TAR·达特：SEC结合 从分子间接触和构象倾向的贡献。从共同 1000个TAR RNA变体的文库，Aim 1将确定构象倾向并测量结合 跨溶液条件对达特和达特的亲和力：SEC，并测量细胞中的转录激活， 达特浓度变化。这些数据将用于开发一个定量和预测模型， 基于体外测量的转录激活，并迭代地完善模型。Aim 2将整合 核磁共振数据与MD模拟和FARFAR;确定原子分辨率合奏20 TAR变种，免费 并与达特RNA结合结构域结合;使用系综来定义结合的构象状态， 优化构象倾向;确定MD和FARFAR的优点和缺点;并开发和测试 一种确定RNA集合的新方法（FARFAR-CS）并使用它来改进100 s TAR的倾向 变体。目标3将扩展该模型，以包括替代二级结构倾向，使用NMR 实验来测量100个TAR变体的这些倾向，并扩展模型以包括结合 20个小分子的竞争，并与7SK RNA竞争1000个RNA变体。完成后，该项目 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-蛋白质结合的深刻贡献，并将为未来的研究提供路线图。 这些努力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。