CAREER: CDS&E: Protein Structure Prediction from Covalent Labeling Mass Spectrometry Data

在化学系化学测量和成像项目的支持下，俄亥俄州立大学Steffen Lindert教授和他的团队正在努力将质谱（MS）的功能从简单地表征分子的组成（例如代谢物、蛋白质、小化学分子）扩展到帮助预测详细的结构（特别是蛋白质）。这是通过对蛋白质上具有特定化学标记的可接近位点进行化学反应，然后使用质谱来发现蛋白质上的哪些位点被标记来实现的。Lindert教授的“MS-Fold”软件然后从这个标签信息推断蛋白质结构。鉴于蛋白质在调节生命化学过程中的重要性，更好的探测蛋白质结构的工具有助于更好地理解健康和患病生物体中的化学过程。为了帮助将这些原则传达给普通受众，Lindert团队还在开发流行科学视频游戏Foldit的MS-Fold版本。这项工作旨在提高公众的科学素养，并扩大对STEM科学技术相关学科的兴趣和参与。最终目标是增加STEM的参与，特别是那些代表性不足的群体，并改善本科和研究生阶段的STEM教育。复杂的质谱（MS）技术结合共价标记的蛋白质残基可以获得关于蛋白质结构的重要信息。然而，将这些信息简单可靠地转化为精确的结构模型仍然是一个特别具有挑战性的问题。Lindert实验室研究的总体目标是开发先进的计算工具，可以自动将ms生成的共价标记数据转换为蛋白质结构模型。具体来说，研究目标是开发和验证一种名为“MS- fold”的软件工具，该工具将允许从共价标记MS研究（例如溶剂暴露的蛋白质残基）中生成的数据有效地用于指导蛋白质结构预测算法。MS- fold旨在为分析生物化学界提供一个用户友好的计算工具，通过该工具，共价标记MS数据可以集成到蛋白质结构和大分子相互作用的高分辨率分析中。这反过来将极大地提高质谱数据的可解释性，构成结构质谱领域的重大进步，并为研究人员从先进的质谱实验结果中提取有用信息提供新的机会。教育目标需要对复杂的、联合计算-实验化学方法进行跨学科训练的新方法。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。

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Prof. Steffen Lindert and his group at Ohio State University are working to significantly extend the capabilities of mass spectrometry (MS) from simply characterizing the composition of molecules (e.g. metabolites, proteins, small chemical molecules) to helping predict detailed structures (especially of proteins). This is achieved by chemically reacting accessible sites on a protein with specific chemical labels and subsequently using MS to discover which sites on the protein were labeled. Prof. Lindert's "MS-Fold" software then infers the protein structure from this labeling information. Given the importance of proteins in regulating the chemistry of life, better tools for probing protein structure supports better understanding of that chemistry in both healthy and diseased organisms. To help convey these principles to a general audience, the Lindert group is also developing an MS-Fold version of the popular scientific video game Foldit. This work aims to increase public scientific literacy, and to expand interest and engagement in STEM science and technology-related disciplines. The ultimate goal is to increase STEM participation, particularly of underrepresented groups, and to improve STEM education at the undergraduate and graduate level. Sophisticated mass spectrometry (MS) techniques in conjunction with covalently-labeled protein residues can yield important information about protein structure. However, easy and reliable translation of this information into accurate structural models remains particularly challenging. The overall goal of research in the Lindert lab is to develop advanced computational tools that can convert MS-generated covalent labeling data into protein structural models in an automated fashion. Specifically, the research objective is to develop and validate a software tool, termed "MS-Fold", that will allow data generated from covalent labeling MS studies (e.g. solvent-exposed protein residues) to be effectively used to guide protein structure prediction algorithms. MS-Fold is intended to provide the analytical biochemistry community with a user-friendly computational tool with which covalent labeling MS data can be integrated into high-resolution analysis of protein structure and macromolecular interactions. This in turn will dramatically improve interpretability of MS data, constituting a significant advance in the field of structural MS and providing new opportunities for investigators to extract useful information from results of advanced MS experiments. The educational objectives entail novel approaches to interdisciplinary training for complex, joint computational-experimental chemical methods.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.