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

基本信息

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
项目类别：
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

项目摘要

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.

在所有方法中，基于信使 RNA (mRNA) 的疫苗已成为一种快速且多功能的疫苗。快速应对病毒大流行的候选者，包括 2019 年冠状病毒病 (COVID-19)。但mRNA 疫苗面临着关键的潜在限制。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 主链的一次切割就可以使 mRNA 疫苗失效。目前，对于给定 RNA 主链中最容易降解的位置的细节知之甚少超稳定信使RNA分子的设计是一个紧迫的挑战。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。一个有前途的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计算机视觉、自然语言处理、蛋白质折叠、核酸特征等多个领域预测任务。在这个提案中，孙庆博士旨在结合深度学习和实验来预测mRNA 在室温下稳定的疫苗。通过采用包括自注意力在内的两种深度学习技术和卷积，她将创建可解释的端到端模型来预测 COVID-19 疫苗二次接种直接从序列信息中构建结构，最后，她将使用一种合成方法，可以快速生成 mRNA 疫苗来验证并进一步改进他们的深度学习模型。具体来说，研究该提案的目标是：1）使用自注意力和卷积开发深度学习模型，其中捕获长程依赖性，预测 RNA 二级结构并使用现有的模型训练模型高精度、高效率的RNA二级结构数据集； 2）对mRNA采用迁移学习疫苗稳定性预测； 3）使用实验验证并进一步提高模型性能基于需求的 mRNA 生产系统。她将生产数百个 mRNA 疫苗序列并进行测试他们在实验室中的稳定性可以作为数据集来验证和重新训练他们的模型。该项目将作为用于快速应对流行病的其他 mRNA 疫苗处理框架。二级结构该提案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。