New methods for computational modeling of RNA structures

项目摘要 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，如精准医学和合成 生物学，都指向RNA作为中央调节器和信息载体。最近，意识到潜力 利用RNA干预基因表达，科学家们成功地开发了Onpattro，第一个FDA- 2018年8月批准的基于RNA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，已知结构的数量是需要的一小部分， 测定这个差距必须通过计算方法来弥补。此外，RNA分子是 高电荷聚阴离子和正电荷如金属离子与RNA结合并形成一个整体部分 一种RNA结构。金属离子在何处以及如何与RNA相互作用可以直接影响RNA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。这些工具导致了 在病毒学、微生物学、基因治疗、RNA生物技术和各种基于RNA的 治疗设计。然而，尽管经过了十多年的努力，计算RNA中的许多关键问题仍然存在， 生物学仍然存在：从头预测非沃森-克里克相互作用，结构预测大 RNA，有效地将实验数据，如冷冻EM和NMR数据纳入结构预测， 以及金属离子效应的建模。在这项资助中，经过15年的开发， 我们建议以一个完全不同的方法，针对上述及其他迫切的问题， 系统地开发数据驱动（如深度学习）或混合数据驱动/基于物理 模拟方法实验数据量的增加推动了新方法的发展 以及迫切需要更有效和可靠的数据解释计算工具， 特别是用于结构测定实验。我们将使用实验数据库，如RNA- Puzzles数据库、PDB、EMDataBank、BMRB，用于大规模基准测试、生化和NMR 我们的合作伙伴收集的数据，用于深入和互动的信息， 实验，如HCV基因组RNA和HIV PBS系统。我们的目标，如果成功-完全实现， 将立即影响结构测定等实验，包括基于cryo-EM和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。

PROJECT SUMMARY 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. Unfortunately, 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.