D3SC: Nonlinear Optical Analysis of Proteins in Glasses

在化学系化学测量和成像计划的支持下，普渡大学的Garth Simpson教授和他的团队正在设计复杂的新光学方法和基于机器学习的数据分析工具，能够探测当蛋白质从溶液转变为玻璃基质时发生的结构和灵活性的变化。一些蛋白质在无定形玻璃中保持功能，即使在没有液态水的情况下也是如此。这种效应使一些生物即使在水的冰点以下也能存活。玻璃基质还可以支持治疗性蛋白质的长期储存和输送--在另一种情况下，了解这些基质中蛋白质的性质是很重要的。辛普森小组正在开发的方法旨在提高我们对蛋白质/基质在去除水分时相互作用的本质的基本理解。辛普森博士正在通过教学短片和NSF产业/大学合作研究中心的产业互动，向广大受众传达他的研究背后的原则。辛普森团队正在研究三种创新的方法来探索蛋白质和糖的结构和动态变化，这些变化伴随着冷冻干燥诱导的糖从水环境中的共溶质转变为蛋白质稳定的基质。首先，他们正在开发新的傅立叶变换方法，以大幅提高光漂白后荧光恢复(FRAP)中的信息含量，以便能够快速评估水溶液和冻干材料在低体积(~100 nL)范围内的迁移率/松弛。其次，他们正在设计一种方法，利用与阿贡国家实验室合作设计和建造的一种独一无二的仪器，在去除水分后绘制蛋白质内部结构进化的原子级图谱。该方法结合了非线性光学成像、同步辐射X射线衍射和基于非负矩阵分解的独特数据处理算法。第三，正在设计、训练和测试一种新的物理编码的人工神经网络(PEN)，作为一种改善对观察到的结构和迁移率变化的物理起源的理解的手段。长期目标是开发实验/计算方法，以提高对基础知识的理解，并提供预测玻璃基质中蛋白质动态变化的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Garth Simpson and his group at Purdue University are devising sophisticated new optical methods and machine learning-based data analysis tools capable of probing the changes of structure and flexibility that occur when proteins transition from solutions to glassy matrices. Some proteins retain function in amorphous glasses, even in the absence of liquid water. This effect enables some living creatures to survive even below the freezing point of water. Glassy matrices can also support long-term storage and delivery of therapeutic proteins - another context in which it is important to understand the properties of proteins in these matrices. The methods being developed by the Simpson group are designed to improve our fundamental understanding of the nature of protein/matrix interactions upon removal of water. Dr. Simpson is communicating the principles behind his research to broad audiences through short instructional videos and through industrial interactions in an NSF Industry/University Collaborative Research Center. The Simpson group is working on three innovative approaches to probing the protein and sugar structural and dynamic changes that accompany the lyophilization-induced transition of sugars from co-solutes in aqueous environments to protein-stabilizing matrices. First, they are developing new Fourier transform methods for substantially improving the information content in fluorescence recovery after photobleaching (FRAP) in order to enable rapid assessment of mobility/relaxation within low volumes (~100 nL) of both aqueous solutions and lyophilized materials. Second, they are devising means of atomic-level mapping of structural evolution within proteins upon water removal using a one-of-a-kind instrument designed and built in collaboration with Argonne National Laboratory. The approach combines nonlinear optical imaging, synchrotron X-ray diffraction, and unique data-handling algorithms based on non-negative matrix factorization. Third, a novel physics-encoded artificial neural network (PENN) is being designed, trained, and tested as a means of gaining improved understanding of the physical origins of observed structure and mobility changes. The long-term aim is to develop experimental/computation methods that can improve fundamental understanding and provide capabilities for predicting the dynamic transformations in proteins within glassy matrices.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.