MFB: Better Homologous Folding using Computational Linguistics and Deep Learning

核糖核酸（RNA）在我们的日常生活中至关重要，因为它在每个活细胞中都起着至关重要的作用。此外，我们的世界最近被一种RNA病毒搅得天翻地覆，当时一种RNA疫苗部分遏制了这种病毒。与普遍认知相反，RNA不仅是更广为人知的DNA和蛋白质之间的中间“信使”，而且还具有深远的生物学功能，如控制基因表达。这些功能是由RNA结构（RNA的“形状”）决定的，因此这些结构的准确建模对于理解RNA功能以及设计疫苗、检测试剂盒和药物至关重要。然而，现有的用于确定RNA结构的实验方法非常昂贵，并且通常仅限于短序列，并且现有的计算工具相当缓慢且不完全准确。这种缓慢阻碍了它们在冠状病毒（约3万个核苷酸或“字母”）等全长病毒基因组中的应用。因此，迫切需要开发更好的计算方法来预测更准确、更有效的RNA结构，并可扩展到更长的序列（如全基因组）。这方面的进展可以提高我们对RNA病毒（包括普通感冒、流感、狂犬病、艾滋病毒、埃博拉、脊髓灰质炎、麻疹等）的了解，并增强我们对抗下一次大流行的准备。该项目开发了有效的算法来预测多个相关（“同源”）RNA序列的结构，如SARS-CoV-2变体。这些算法在平均序列长度和序列数量上都呈线性扩展。这种线性扩展将使全基因组应用成为可能。研究人员旨在通过人工智能（AI）的两个分支：自然语言处理和深度学习来实现这些目标。具体而言，本项目将改进三种类型的同源折叠算法，并使其适应于结构发现：(1)align-then-fold：首先对同源序列进行比对，然后预测比对序列的一致结构；(2)迭代比对折叠：在序列比对和结构预测之间进行迭代；(3)同时对准和折叠：共同预测走向和结构。该团队将采用这些快速方法，利用RNA病毒基因组和转录本的全局结构预测来发现保守结构。这项研究将使发现新的RNA结构和功能成为可能，并将有助于设计疫苗、检测试剂盒和药物。本项目由信息与智能系统学部、化学学部和化学学部化学理论、模型与计算方法项目支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Ribonucleic acid (RNA) is of utmost importance in our daily life because it plays essential roles in every living cell. Furthermore, our world was recently turned upside down by an RNA virus, which was then partially contained by an RNA vaccine. Contrary to common wisdom, RNA is not just an intermediate “messenger” between the more well-known DNA and protein, but it can also have profound biological functions such as controlling gene expression. These functions are determined by RNA structures (the “shapes” of the RNAs), and therefore accurate modeling of these structures is critical for understanding RNA functions and for designing vaccines, test kits, and drugs. However, existing experimental methods for determining RNA structure are extremely expensive and often limited to short sequences, and existing computational tools are rather slow and not completely accurate. This slowness hinders their applications to full-length viral genomes such as coronavirus (about 30,000 nucleotides or “letters”). Therefore, there is a critical need to develop better computational methods to predict RNA structures that are more accurate and more efficient and scalable to longer sequences such as whole genomes. Advances in this direction could improve our understanding of RNA viruses (which include common cold, influenza, Rabies, HIV, Ebola, polio, measles, and more) and increase our readiness to fight the next pandemic.This project develops efficient algorithms for predicting the structures of multiple related (“homologous”) RNA sequences such as SARS-CoV-2 variants. These algorithms will scale linearly in both the average sequence length and the number of sequences. This linear scaling will enable whole genome applications. The researchers aim to achieve these goals with ideas from two branches of artificial intelligence (AI): natural language processing and deep learning. Specifically, this project will improve three types of homologous folding algorithms and adapt them to structure discovery: (1) align-then-fold: first align the homologous sequences and then predict the consensus structure for the aligned sequences; (2) iteratively align-and-fold: iterate between sequence alignment and structure prediction; and (3) simultaneous align-and-fold: jointly predict alignment and structures. The team will adapt these fast methods to discover conserved structures using global structure prediction for RNA viral genomes and transcripts. This research will make it possible to discover new RNA structures and functions, and will help the design of vaccines, test kits, and drugs.This project is supported by the Divisions of Information and Intelligent Systems and of Chemistry and the Chemical Theory, Models, and Computational Methods Program in the Division of Chemistry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.