A deep learning and experiment integrated platform for stable mRNA vaccines development

用于稳定mRNA疫苗开发的深度学习和实验集成平台

• 批准号: 10334939
• 负责人: qing sun
• 金额: $ 36.12万
• 依托单位: TEXAS ENGINEERING EXPERIMENT STATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-22 至 2026-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10334939
• 关键词: Adopted; Anti-Bacterial Agents; Attention; COVID-19; COVID-19 vaccine; Chemicals; Code; Cold Chains; Communicable Diseases; Computer Vision Systems; Data; Data Set; Dependence; Developing Countries; Engineering; Enzyme-Linked Immunosorbent Assay; Face; Feedback; Fluorescence; Genetic Transcription; Half-Life; Hydrolysis; Immunogenetics; In Vitro; Knowledge; Length; Mammalian Cell; Messenger RNA; Methods; Modeling; Natural Language Processing; Natural Sciences; Nucleic Acids; Performance; Physics; Preparation; Process; Production; Property; Proteins; RNA; RNA Degradation; RNA Instability; RNA Sequences; RNA Stability; RNA vaccine; Research; Research Personnel; Rest; Role; Ships; Structure; System; Techniques; Temperature; Testing; The Sun; Training; Translations; Untranslated RNA; Update; Vaccine Production; Vaccines; Validation; Variant; Vertebral column; Viral; Virus; Western Blotting; Work; base; cost; data-driven model; deep learning; deep learning model; design; experimental study; fight against; improved; in vivo; novel; pandemic disease; protein folding; research and development; response; rural area; supervised learning; therapeutic RNA; transfer learning; unsupervised learning; vaccine development

Among all approaches, messenger RNA (mRNA)-based vaccines have emerged as a rapid and versatile candidate to quickly respond to virus pandemics, including coronavirus disease 2019 (COVID-19). But mRNA vaccines face key potential limitations. Researchers have observed that RNA molecules tend to spontaneously degrade, which is a serious limitation - a single cut in the mRNA backbone can nullify the mRNA vaccine. Currently, little is known on the details of where in the backbone of a given RNA is most prone to degradation and design of super stable messenger RNA molecules is an urgent challenge. Without this knowledge, mRNA vaccines against COVID-19 will require stringent conditions for preparation, storage, and transport. A promising potential solution is deep learning, a general class of data-driven modeling approach, which has proved dominant in many fields including computer vision, natural language processing, protein folding, and nucleic acid feature prediction tasks. In this proposal, Dr. Qing Sun aims to combine deep learning and experiments to predict mRNA vaccines that are stable at room temperature. By adapting two deep learning techniques including self-attention and convolutions, she will create interpretable end to end models to predict COVID-19 vaccine secondary structures directly from sequence information and in the end, she will use a synthetic approach that rapidly generates mRNA vaccine to validate and further improve their deep learning model. Specifically, the research objectives of this proposal are: 1) to develop the deep learning model using self-attention and convolution, which capture long-range dependencies, to predict RNA secondary structures and to train the model using existing RNA secondary structure dataset with high accuracy and efficiency; 2) to employ transfer learning for mRNA vaccine stability predictions; and 3) to validate and further improve the model performance using experimental demand-based mRNA production system. She will produce hundreds of mRNA vaccines sequences and test their stabilities in the lab to serve as dataset to validate and retrain their model. This project will serve as a framework for other mRNA vaccine processing for rapid response to pandemics. The secondary structure prediction knowledge from this proposal will also help characterize natural mRNA and synthetic mRNA for natural science and engineering purposes.

在所有的方法中，基于信使RNA（mRNA）的疫苗已经成为一种快速和通用的疫苗。 候选人迅速应对病毒大流行，包括2019年冠状病毒病（COVID-19）。但是mRNA 疫苗面临关键的潜在限制。研究人员观察到RNA分子倾向于自发地 降解，这是一个严重的限制-mRNA骨架中的单一切割可以使mRNA疫苗无效。 目前，对于给定RNA的主链中何处最容易降解的细节知之甚少 设计超稳定的信使RNA分子是一个紧迫的挑战。如果没有这些知识，mRNA 针对COVID-19的疫苗需要严格的制备、储存和运输条件。一个有前途 潜在的解决方案是深度学习，这是一种通用的数据驱动建模方法，已被证明占主导地位 在许多领域，包括计算机视觉、自然语言处理、蛋白质折叠和核酸特征 预测任务。在这项提案中，孙庆博士旨在将联合收割机深度学习和实验相结合来预测mRNA 疫苗在室温下是稳定的。通过采用两种深度学习技术，包括自我注意力， 她将创建可解释的端到端模型来预测COVID-19疫苗的二级 最后，她将使用一种合成方法， 生成mRNA疫苗，以验证并进一步改进其深度学习模型。具体来说，研究 该提案的目标是：1）使用自我注意和卷积来开发深度学习模型， 捕获长程依赖性，预测RNA二级结构，并使用现有的 RNA二级结构数据集，具有高精度和高效率; 2）对mRNA采用迁移学习 疫苗稳定性预测;以及3）使用实验验证并进一步改善模型性能 基于需求的mRNA生产系统。她将生产数百种mRNA疫苗序列， 他们在实验室中的稳定性作为数据集来验证和重新训练他们的模型。该项目将作为一个 其他mRNA疫苗处理的框架，以快速应对大流行病。二级结构 来自该提议的预测知识也将有助于表征天然mRNA和合成mRNA， 科学和工程目的。