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起着重要作用。此外，我们的世界最近被RNA病毒颠倒了，然后由RNA疫苗部分含有。与共同的智慧相反，RNA不仅是更知名的DNA和蛋白质之间的中间“信使”，而且还可以具有强烈的生物学功能，例如控制基因表达。这些功能由RNA结构（RNA的“形状”）确定，因此，这些结构的准确建模对于理解RNA功能以及设计疫苗，测试试剂盒和药物至关重要。但是，确定RNA结构的现有实验方法非常昂贵，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对全长病毒基因组（例如冠状病毒）（约30,000个核卫星或“字母”）的应用。因此，迫切需要开发更好的计算方法来预测对更准确，更有效和可扩展到更长序列（例如整个基因组）的RNA结构。在这个方向上的进步可以提高我们对RNA病毒的理解（包括普通感冒，影响，狂犬病，艾滋病毒，埃博拉病毒，脊髓灰质炎，麻疹等），并提高我们准备与下一个大流行作斗争的准备。该项目开发了有效的算法，以预测多个相关（“同型”）诸如sars-cov-2 variants之类的多个相关（“同型”序列）。这些算法将以平均序列长度和序列数量线性扩展。该线性缩放将使整个基因组应用。研究人员的目的是通过两个人工智能分支（AI）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，该项目将改善三种类型的同源折叠算法并使其适应结构发现：（1）对齐 - 然后对同源序列进行排列，然后预测对齐序列的共识结构； （2）迭代对齐：迭代序列比对和结构预测之间； （3）同时对齐：共同预测对准和结构。该团队将使用RNA病毒基因组和转录本的全局结构预测来调整这些快速方法，以发现构造。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，测试套件和药物。该项目得到信息和智能系统，化学和化学理论，化学理论，模型和计算方法计划的划分的支持。化学裁决反映了NSF的法定任务和构成诚实的良好的支持。