MFB: Better Homologous Folding using Computational Linguistics and Deep Learning

MFB：使用计算语言学和深度学习更好的同源折叠

• 批准号: 2330737
• 负责人: Liang Huang
• 金额: $ 145.31万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330737&HistoricalAwards=false
• 关键词: MFB; Better; Homologous; Folding; using

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.

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发挥着重要作用。此外，我们的世界最近被 RNA 病毒彻底颠覆，而与普通 RNA 疫苗相反，RNA 病毒被部分遏制。智慧，RNA不仅仅是众所周知的DNA和蛋白质之间的中间“信使”，而且它还可以具有深远的生物学功能，例如控制基因表达，这些功能是由RNA结构（RNA的“形状”）决定的。 ），因此准确建模这些结构对于理解 RNA 功能以及设计疫苗、检测试剂盒和药物至关重要。然而，现有的确定 RNA 结构的实验方法非常昂贵，并且通常仅限于短序列，而且现有的计算工具相当慢且不完全准确。这种缓慢性阻碍了它们在冠状病毒等全长病毒基因组（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更准确、更高效和可扩展的 RNA 结构。更长的序列，例如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、埃博拉病毒、脊髓灰质炎、麻疹等）的了解，并提高我们应对下一次流行病的准备。该项目开发高效用于预测多个相关（“同源”）RNA 序列（例如 SARS-CoV-2 变体）的结构的算法这些算法将在平均序列长度和序列数量上进行线性缩放，这种线性缩放将使全基因组应用成为可能。这研究人员旨在利用人工智能（AI）两个分支的思想来实现这些目标：自然语言处理和深度学习，具体而言，该项目将改进三种类型的同源折叠算法，并使它们适应结构发现：（1）align-then。 -fold：首先比对同源序列，然后预测比对序列的共有结构；（2）迭代比对和折叠：在序列比对和结构预测之间进行迭代；以及（3）同时比对和折叠：联合预测团队将调整和结构。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，并将有助于疫苗、检测试剂盒和药物的设计。由信息与智能系统部、化学部以及化学部的化学理论、模型和计算方法项目支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。