D3SC: Nonlinear Optical Analysis of Proteins in Glasses

在化学系化学测量和成像项目的支持下，普渡大学的加思·辛普森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，这些工具能够探测蛋白质从溶液过渡到玻璃基质时发生的结构和灵活性变化。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。这种效应使一些生物即使在水的冰点以下也能生存。玻璃基质还可以支持治疗性蛋白质的长期储存和递送-另一个重要的背景是了解这些基质中蛋白质的性质。辛普森小组正在开发的方法旨在提高我们对去除水后蛋白质/基质相互作用性质的基本理解。 辛普森博士通过简短的教学视频和NSF工业/大学合作研究中心的工业互动，向广大观众传达他的研究背后的原则。Simpson小组正在研究三种创新方法，以探索蛋白质和糖的结构和动态变化，这些变化伴随着冻干诱导的糖从水性环境中的共溶质转变为蛋白质稳定基质。首先，他们正在开发新的傅立叶变换方法，用于大幅提高光漂白后荧光恢复（FRAP）中的信息含量，以便能够快速评估低体积（~100 nL）水溶液和冻干材料中的迁移率/弛豫。 第二，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独一无二的仪器，在去除水分后对蛋白质内的结构进化进行原子级映射。 该方法结合了非线性光学成像，同步加速器X射线衍射，和独特的数据处理算法的基础上非负矩阵分解。 第三，正在设计、训练和测试一种新的物理编码人工神经网络（PENN），以更好地理解观察到的结构和流动性变化的物理起源。长期目标是开发实验/计算方法，以提高对玻璃基质中蛋白质动态变化的基本理解和预测能力。该奖项反映了NSF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。

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.