Collaborative Research: DMREF: Predicting Molecular Interactions to Stabilize Viral Therapies

合作研究：DMREF：预测分子相互作用以稳定病毒疗法

• 批准号: 2118638
• 负责人: Sapna Sarupria
• 金额: $ 56.77万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118638&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Predicting; Molecular

Non-technical Description: Many vaccine production and delivery systems remain dependent on a cold chain requirement, which prevents millions of people from receiving vaccines annually. To increase the availability of current and future vaccines, the vaccine cold chain needs to be eliminated. While sugars and bulking agents are being explored to increase the thermal stability of viral vaccines, the cold chain is still the main method to stabilize viral vaccines. This is not only an issue for developing countries; proper temperature storage of vaccines is also a challenge in the US, with an outbreak of influenza having been potentially linked to improper vaccine refrigeration. A more standard and promising method to stabilize vaccine formulations is to add stabilizing excipients. With excipients, vaccines can be stored under refrigeration conditions. However, this approach has suffered from both a lack of generalizability and the absence of a fundamental understanding of the mechanism whereby stabilization is achieved. Empirical evidence has identified several excipients such as sugars, amino acids, and bulking agents like gelatin, dextran, and cellulose that help to stabilize proteins/viruses in both wet and dry formulations. In addition, it has been demonstrated that complex combinations of excipients (mixtures) are often used in final formulations. Experimental observations suggest that many of the excipients help to structure water and/or replace hydrogen-bonding interactions with the surface of the protein/virus to provide stability. However, most of the work published in this area has been empirical and experimental in nature and would be difficult to perform at the scale needed to elucidate the subtle ways in which molecular structure affects water structure and thus stability. In this project, a combination of experiments, modeling, and machine learning will be used to identify molecular features/motifs that impart this stability and use this framework to discover excipient mixtures for vaccine formulations. This approach has the potential to shift the paradigm for vaccine formulation – allowing for tailoring of formulations based on knowledge of the virus itself, rather than through an iterative, Edisonian process.Technical Description: In this research, the team will use molecular dynamics simulations and machine learning in concert with a panel of experimental techniques to identify and understand the key molecular motifs needed for excipient molecules to create a stable virus-containing formulation. The interactions of both viruses and excipients with water is a critical design parameter for the creation of stable formulations; however, the complexity of these interactions represents a vast parameter space that is difficult to deconvolute and not suited to traditional materials design. This DMREF program will combine experimental measurements of excipient-virus interactions with a rapid computational scheme to design stabilizing formulations to enable the minimization of cold chain requirements for viral vaccines. The stability of viruses and other proteins is directly connected to interactions with water. However, the complexity of the available interactions has prevented bottom-up prediction. A materials design protocol will be developed that predicts how molecular motifs such as hydrogen bonding and electrostatic interactions give rise to the structuring of water and correlate with changes in virus stability. During the project, high school and community college student will be exposed to graduate level science and their interest piqued towards future careers in science and engineering. The goals of this project will be to (1) attain a comprehensive protocol for testing the potential effects of a new excipient molecule on virus stability and (2) use the resulting data to develop a machine-learning algorithm to enable the predictive design of more complex excipient formations.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.

非技术描述：许多疫苗生产和交付系统仍然依赖于冷链要求，这使得每年数百万人无法接受疫苗。为了增加当前和未来疫苗的可用性，需要消除疫苗冷链。尽管正在探索填充剂来提高病毒疫苗的热稳定性，但冷链仍然是稳定病毒疫苗的主要方法，这不仅是发展中国家的问题；在美国，疫苗的适当温度储存也是一个挑战。 ，流感的爆发可能与不当行为有关一种更标准、更有前景的稳定疫苗配方的方法是添加稳定赋形剂，疫苗可以在冷藏条件下储存。然而，这种方法缺乏通用性，而且缺乏对疫苗的基本了解。经验证据已经确定了几种有助于稳定的赋形剂，例如糖、氨基酸和填充剂（例如明胶、葡聚糖和纤维素）。此外，实验观察表明，许多赋形剂有助于结构化水和/或替代氢。然而，该领域发表的大多数工作本质上都是经验性和实验性的，并且很难以阐明分子结构的微妙方式所需的规模进行。影响在该项目中，将结合实验、建模和机器学习来识别赋予这种稳定性的分子特征/基序，并使用该框架来发现疫苗配方的赋形剂混合物。改变疫苗配方的范式——允许根据病毒本身的知识定制配方，而不是通过迭代的爱迪生过程。技术描述：在这项研究中，该团队将结合使用分子动力学模拟和机器学习一个面板识别和了解赋形剂分子创建稳定的含病毒制剂所需的关键分子基序的实验技术，病毒和赋形剂与水的相互作用是创建稳定制剂的关键设计参数；相互作用代表了一个巨大的参数空间，难以解卷积且不适合传统材​​料设计。该 DMREF 程序将把赋形剂-病毒相互作用的实验测量与快速计算方案结合起来，以设计稳定配方，从而最大限度地减少冷链。病毒和其他蛋白质的稳定性与与水的相互作用直接相关，但是，现有相互作用的复杂性阻碍了自下而上的预测，以预测分子基序（例如氢）的情况。结合和静电相互作用会引起水的结构，并与病毒稳定性的变化相关。在该项目中，高中生和社区学院的学生将接触到研究生水平的科学，并激发他们对未来科学和工程职业的兴趣。该项目的目标是 (1) 达到用于测试新赋形剂分子对病毒稳定性的潜在影响的综合方案，以及 (2) 使用所得数据开发机器学习算法，以实现更复杂的赋形剂形成的预测设计。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。