New methods for computational modeling of RNA structures

RNA分子在几乎所有细胞过程中在基因表达水平上起着重要作用， 调控毫不奇怪，新兴的生物医学进步，如精确医学和合成生物学， 指出RNA是中央调节器和信息载体。最近，意识到利用RNA的潜力 为了干预基因表达，科学家们成功地开发了Onpattro，这是第一个FDA批准的基于RNA的 2018年8月的治疗。 了解RNA的功能和治疗应用需要了解RNA的结构。不幸的是， 目前，已知结构的数量只占需要确定的数量的一小部分。这一差距 必须通过计算方法来关闭。此外，RNA分子是高度带电的聚阴离子， 正电荷如金属离子与RNA结合并形成RNA结构的组成部分。哪里以及如何 与RNA相互作用的金属离子可以直接影响RNA的结构和功能以及RNA-药物相互作用。 在NIH超过15年的持续支持下，我们开发了系统的计算工具， RNA结构、折叠稳定性、动力学和金属离子效应的预测。这些工具带来了富有成效的 在病毒学、微生物学、基因治疗、RNA生物技术和各种基于RNA的治疗性疾病中的应用。 信号装置.然而，尽管经过了十多年的努力，计算RNA生物学中的许多关键问题仍然存在： 非Watson-Crick相互作用的从头预测，大RNA的结构预测，有效掺入 实验数据，如冷冻EM和NMR数据转化为结构预测，以及金属离子效应的建模。 感染。在这项资助中，经过15年的发展，我们提出了一个基于物理学的初始模型， 和其他紧迫问题，通过系统地开发数据驱动的方法， (such作为深度学习）或混合数据驱动/基于物理的模拟方法。新的方法是莫- 由于实验数据量的不断增加以及迫切需要更有效和可靠的 用于数据解释的计算工具，特别是用于结构测定实验。我们将使用前- 实验数据库，如RNA-Puzzles数据库，PDB，EMDataBank，BMRB，用于大规模基准测试 测试，生化和核磁共振数据收集我们的良好合作伙伴进行深入和互动 关于各种实验的信息，如HCV基因组RNA和HIV PBS系统。我们的目标，如果成功- 完全完成，将立即影响实验，如结构确定，包括冷冻EM和 基于NMR的结构测定，金属离子位点的鉴定，以及RNA结构的合理设计， 治疗应用。

RNA molecules play fundamental roles in nearly all cellular processes at the level of gene expression and regulation. Not surprisingly, emerging biomedical advances such as precision medicine and synthetic biology all point to RNA as the central regulators and information carriers. Recently, realizing the potential of using RNA to intervene gene expression, scientists successfully developed Onpattro, the first FDA-approved RNA-based therapy in August 2018. Understanding RNA function and therapeutic applications requires knowledge about RNA structure. Unfortu- nately, currently, the number of the known structures is a small fraction of what need to be determined. This gap has to be closed by computational methods. Furthermore, an RNA molecule is a highly charged polyanion and positive charges such as metal ions bind to an RNA and for an integral part of an RNA structure. Where and how metal ions interact with an RNA can directly impact RNA structure and function as well as RNA-drug interactions. Continuously supported by NIH for over 15 years, we have developed systematic computational tools for the predictions of RNA structures, folding stability, kinetics, and metal ion effects. These tools have led to fruitful applications in virology, microbiology, gene therapy, RNA biotechnology, and various RNA-based therapeutic de- signs. However, despite over decade of efforts, many critical issues in computational RNA biology still remain: de novo prediction of non-Watson-Crick interactions, structure prediction for large RNAs, effective incorporation of experimental data such as cryo-EM and NMR data into structure prediction, and modeling of metal ion ef- fects. In this grant, after 15 years of developing an initio physics-based models, we propose to target the above and other pressing issues using a fundamentally different approach by systematically developing data-driven (such as deep-learning) or hybrid data-driven/physics-based simulation methods. The new approaches are mo- tivated by the increasing amount of experimental data and the pressing need to have more efficient and reliable computational tools for data interpretation, especially for structure determination experiments. We will use ex- perimental database, such as RNA-Puzzles database, PDB, EMDataBank, BMRB, for large-scale benchmark tests, and biochemical and NMR data collected by our well-established collaborators for in-depth and interactive information about various experiments such as HCV genomic RNAs and HIV PBS systems. Our goal, if success- fully accomplished, will immediately impact experiments such as structure determination, including cryo-EM and NMR-based structure determination, identification of metal ion sites, and rational design of RNA structures for therapeutic applications.