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

项目摘要 HIV-1病毒复制的许多必不可少的过程是由调控的RNA元件在 形成生物活性复合体的逆转录病毒基因组和宿主/病毒蛋白。尽管在解决问题方面取得了进展 RNA-蛋白质复合体的三维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，描述性的，不是预测性的。 为了揭示作用力和构象状态，获得定量和预测性的理解是必要的 推动病毒进程，并充分定义治疗干预的机会格局。这个 这一建议的主要假设是，HIV-1 TAR RNA的细胞转录活性可以是 根据其构象倾向从ITS序列预测形成具有结合能力的构象 以及它对反式激活蛋白TAT和人类超延长复合体(SEC)TAT：SEC的亲和力。这个 该项目将(I)开发一套技术，以获得关于RNA集合、RNA-蛋白质的实验数据 在大量和常见的RNA上的高通量相互作用和细胞活动的定量 序列和结构空间(II)将核磁共振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)确定RNA系综的自由和 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； 以及(Iii)测试和改进预测细胞功能的热力学模型，该模型分析TAR·TAT：SEC结合 分子间接触和构象倾向对能量的贡献。从一个普通的 1000个TAR RNA变异体的文库，Aim 1将确定构象倾向并测量结合 与TAT和TAT的亲和力：跨溶液条件下的SEC，并测量细胞和 TAT浓度不同。这些数据将被用来开发一个定量的和预测的细胞模型 基于体外测量的转录激活，并反复改进模型。AIM 2将整合 使用MD模拟和FARFAR的核磁共振数据；确定20个焦油变体的原子分辨率系综，免费 并结合到TAT RNA结合域；使用系综来定义结合的构象状态和 提炼构象倾向；识别MD和FARFAR的优势和劣势；开发和测试 一种测定RNA系综并用于精制100s焦油倾向性的新方法(FARFAR-CS) 变种。目标3将扩展模型，以包括替代二级结构倾向，使用核磁共振 用实验测量100个焦油变异体的这些倾向，并将模型扩展到包括结合 20个小分子，并与7SK RNA竞争1000个RNA变体。建成后，这个项目 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-蛋白质结合的深远贡献，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的努力。

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.