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

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，这使每年数百万人无法获得疫苗。为了增加现有和未来疫苗的可得性，需要消除疫苗冷链。虽然正在探索糖和填充剂来增加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项挑战，流感爆发可能与疫苗冷藏不当有关。稳定疫苗制剂的更标准和更有前途的方法是添加稳定赋形剂。有了辅料，疫苗可以在冷藏条件下储存。然而，这种方法既缺乏通用性，又缺乏对实现稳定的机制的基本理解。经验证据已经确定了几种赋形剂，如糖、氨基酸和填充剂，如明胶、葡聚糖和纤维素，它们有助于稳定湿式和干式配方中的蛋白质/病毒。此外，已证明在最终配方中经常使用赋形剂的复杂组合（混合物）。实验观察表明，许多赋形剂有助于构建水和/或取代与蛋白质/病毒表面的氢键相互作用，以提供稳定性。然而，在这一领域发表的大多数工作本质上都是经验和实验性质的，很难在阐明分子结构影响水结构及其稳定性的微妙方式所需的规模上进行。在这个项目中，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，并使用这个框架来发现疫苗配方的赋形剂混合物。这种方法有可能改变疫苗配方的模式——允许根据对病毒本身的了解来定制配方，而不是通过迭代的爱迪生过程。技术描述：在这项研究中，团队将使用分子动力学模拟和机器学习与一组实验技术相结合，以识别和理解赋形剂分子所需的关键分子基序，以创建稳定的含病毒制剂。病毒和赋形剂与水的相互作用是创造稳定配方的关键设计参数；然而，这些相互作用的复杂性代表了一个巨大的参数空间，难以解卷积，不适合传统的材料设计。该DMREF项目将结合辅料-病毒相互作用的实验测量与快速计算方案，以设计稳定配方，使病毒疫苗的冷链需求最小化。病毒和其他蛋白质的稳定性与它们与水的相互作用直接相关。然而，可用交互的复杂性阻碍了自下而上的预测。将开发一种材料设计方案，预测诸如氢键和静电相互作用等分子基序如何引起水的结构并与病毒稳定性的变化相关联。在这个项目中，高中生和社区大学生将接触到研究生水平的科学，并对未来的科学和工程职业产生兴趣。该项目的目标将是：(1)获得一项全面的协议，用于测试一种新的赋形剂分子对病毒稳定性的潜在影响；(2)使用结果数据开发一种机器学习算法，以实现更复杂赋形剂结构的预测设计。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.