New methods for computational modeling of RNA structures

项目总结 RNA分子在几乎所有的细胞过程中都在基因表达和 监管。毫不奇怪，新兴的生物医学进步，如精确医学和合成 生物学方面，所有人都指出RNA是中央调节器和信息载体。最近，意识到了 在利用RNA干预基因表达方面，科学家成功地开发出了第一个FDA-Onpattro- 2018年8月批准了基于RNA的治疗。 了解RNA的功能和治疗应用需要有关RNA结构的知识。 不幸的是，目前已知结构的数量只是所需结构的一小部分 下定决心。这一差距必须通过计算方法来弥合。此外，RNA分子是一种 高电荷聚阴离子和正电荷(如金属离子)与RNA结合，形成一个不可分割的部分 指一种RNA结构。金属离子与RNA相互作用的位置和方式可以直接影响RNA结构 与RNA和药物的相互作用一样起作用。 在美国国立卫生研究院15年的持续支持下，我们开发了系统的计算工具 对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生物技术以及各种基于RNA的 治疗性设计。然而，尽管经过了十多年的努力，计算RNA中的许多关键问题 生物学仍然存在：非Watson-Crick相互作用的从头预测，大分子的结构预测 RNA，有效地将实验数据如低温EM和核磁共振数据纳入结构预测， 以及金属离子效应的模拟。在这笔赠款中，经过15年的初始物理模型开发， 我们建议使用一种根本不同的方法来解决上述和其他紧迫问题，具体方法是 系统地开发数据驱动(如深度学习)或混合数据驱动/基于物理的 模拟方法。随着实验数据量的增加，新的方法受到了启发。 而且迫切需要有更高效、更可靠的计算工具来解释数据， 特别是在结构测定实验中。我们将使用实验数据库，例如RNA- Proules数据库，PDB，EMDataBank，BMRB，用于大规模基准测试，以及生化和核磁共振 由我们久负盛名的合作伙伴收集的数据，以获得有关各种 丙型肝炎病毒基因组RNA和HIV PBS系统等实验。我们的目标，如果成功--完全实现， 将立即影响结构确定等实验，包括低温EM和基于核磁共振的 结构确定、金属离子位置的确定和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.