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起着至关重要的作用。此外，我们的世界最近被一种RNA病毒弄得天翻地覆，这种病毒后来被RNA疫苗部分遏制。与常识相反，RNA不仅仅是更知名的DNA和蛋白质之间的中间“信使”，而且它还可以具有深刻的生物功能，例如控制基因表达。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功能和设计疫苗，测试试剂盒和药物至关重要。然而，现有的确定RNA结构的实验方法极其昂贵，并且通常仅限于短序列，并且现有的计算工具相当慢且不完全准确。这种缓慢阻碍了它们在全长病毒基因组（如冠状病毒（约30，000个核苷酸或“字母”））中的应用。因此，迫切需要开发更好的计算方法来预测RNA结构，这些方法更准确，更有效，并且可扩展到更长的序列，如整个基因组。这一方向的进展可以提高我们对RNA病毒（包括普通感冒、流感、狂犬病、HIV、埃博拉病毒、脊髓灰质炎、麻疹等）的理解，并提高我们对抗下一次大流行的准备。该项目开发了有效的算法，用于预测多个相关（“同源”）RNA序列的结构，如SARS-CoV-2变体。这些算法将在平均序列长度和序列数量上线性缩放。这种线性缩放将使全基因组应用成为可能。研究人员的目标是通过人工智能（AI）的两个分支实现这些目标：自然语言处理和深度学习。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用于结构发现：（1）重复折叠：首先比对同源序列，然后预测比对序列的共有结构;（2）迭代折叠：在序列比对和结构预测之间进行重复;（3）同时折叠：联合预测比对和结构。该团队将采用这些快速方法，利用RNA病毒基因组和转录物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，并将有助于疫苗，测试试剂盒和药物的设计。该项目由信息和智能系统部门以及化学和化学理论，模型，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

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.