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

职业：CDS

• 批准号: 1750666
• 负责人: Steffen Lindert
• 金额: $ 57.5万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750666&HistoricalAwards=false
• 关键词: CAREER; CDS&E; Protein; Structure; Prediction

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.

在化学系化学测量和成像项目的支持下，俄亥俄州州立大学的Steffen Lindert教授和他的团队正在努力显著扩展质谱（MS）的能力，从简单地表征分子（例如代谢物，蛋白质，小化学分子）的组成到帮助预测详细结构（特别是蛋白质）。这是通过将蛋白质上的可接近位点与特定的化学标记进行化学反应，然后使用MS发现蛋白质上的哪些位点被标记来实现的。 Lindert教授的“MS-Fold”软件然后从这些标记信息推断蛋白质结构。 鉴于蛋白质在调节生命化学中的重要性，更好的探测蛋白质结构的工具有助于更好地了解健康和患病生物体中的化学物质。 为了帮助向普通观众传达这些原则，Lindert团队还在开发流行的科学视频游戏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科学和技术相关学科的兴趣和参与。最终目标是提高STEM的参与度，特别是代表性不足的群体，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产生有关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准确的结构模型仍然特别具有挑战性。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。具体而言，研究目标是开发和验证一个软件工具，称为“MS-折叠”，这将允许从共价标记MS研究（例如溶剂暴露的蛋白质残基）生成的数据被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数据可以整合到蛋白质结构和大分子相互作用的高分辨率分析中。这反过来又将大大提高MS数据的可解释性，构成结构MS领域的重大进步，并为研究人员从先进的MS实验结果中提取有用的信息提供新的机会。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。

项目成果

Hybrid methods for combined experimental and computational determination of protein structure

• DOI: 10.1063/5.0026025
• 发表时间: 2020-12-28
• 期刊: JOURNAL OF CHEMICAL PHYSICS
• 影响因子: 4.4
• 作者: Seffernick, Justin T.;Lindert, Steffen
• 通讯作者: Lindert, Steffen

Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.

• DOI: 10.1021/acs.analchem.8b01624
• 发表时间: 2018-06-19
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Aprahamian ML;Chea EE;Jones LM;Lindert S
• 通讯作者: Lindert S