Equipment Supplement: Analysis tools for quantifying protein conformational landscapes using DEER spectroscopy

设备补充：使用 DEER 光谱定量蛋白质构象景观的分析工具

• 批准号: 10385498
• 负责人: Stefan Stoll
• 金额: $ 9.17万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10385498
• 关键词: Address; Bayesian Analysis; Benchmarking; Biomedical Research; Computer Analysis; Computer Models; Cryoelectron Microscopy; Data; Data Analyses; Deer; Disease; Electrons; Ensure; Equipment; Goals; Human; Life; Membrane Proteins; Methods; Modeling; Molecular; Motion; Process; Protein Conformation; Proteins; Reproducibility; Research Personnel; Resolution; Sampling; Spectrum Analysis; Structure; System; Techniques; Testing; Uncertainty; X-Ray Crystallography; base; computerized tools; drug development; experimental analysis; fast protein liquid chromatography; insight; instrument; knowledge base; nanoscale; protein complex; protein purification; protein structure; tool

PROJECT SUMMARY/ABSTRACT This project develops new computational analysis and modeling methods for DEER (double electron-electron resonance) spectroscopy. DEER is a biostructural technique for the quantification of protein conformational landscapes and protein motions on the nanometer scale. Protein motions are crucial for many key molecular processes at the basis of human life and disease. Therefore, DEER provides important insights that contribute to the knowledge base necessary for drug development. In combination with X-ray crystallography, NMR and cryo-EM, DEER is part of a complementary set of integrative experimental biostructural tools. It is especially important for the study of membrane proteins. A major barrier in the field is the lack of integrated analysis tools for biomedical researchers. This project addresses this issue by (a) developing tools based on Bayesian statistics for the rigorous and reproducible analysis of experimental DEER data, providing comprehensive methods for uncertainty quantification (error bars) in DEER data, and (b) creating advanced computational tools to utilize DEER data in the refinement of protein structures based on atomic-resolution ensemble models. Overall, the goal of the project is to significantly accelerate the workflow of DEER data analysis, interpretation and modeling and increase its rigor, reproducibility and accessibility. This will enable the study of the structure and dynamics of larger and more complex proteins and protein assemblies, which are increasingly important in biomedical research.

项目总结/文摘

项目成果

Long-distance tmFRET using bipyridyl- and phenanthroline-based ligands.

使用联吡啶和菲咯啉配体进行长距离 tmFRET。

• DOI: 10.1101/2023.10.09.561591
• 发表时间: 2024
• 期刊: bioRxiv : the preprint server for biology
• 作者: Gordon,SharonaE;Evans,EricGB;Otto,ShaunaC;Tessmer,MaxxH;Shaffer,KyleD;Gordon,MosheT;Petersson,EJames;Stoll,Stefan;Zagotta,WilliamN
• 通讯作者: Zagotta,WilliamN

DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data.

• DOI: 10.5194/mr-1-209-2020
• 发表时间: 2020
• 期刊: Magnetic resonance (Gottingen, Germany)
• 作者: Fábregas Ibáñez L;Jeschke G;Stoll S
• 通讯作者: Stoll S

Bayesian Probabilistic Analysis of DEER Spectroscopy Data Using Parametric Distance Distribution Models.

• DOI: 10.1021/acs.jpca.0c05026
• 发表时间: 2020-07-30
• 期刊: The journal of physical chemistry. A
• 作者: Sweger SR;Pribitzer S;Stoll S
• 通讯作者: Stoll S

Dipolar pathways in dipolar EPR spectroscopy.

• DOI: 10.1039/d1cp03305k
• 发表时间: 2022-01-26
• 期刊: Physical chemistry chemical physics : PCCP
• 作者: Fábregas-Ibáñez L;Tessmer MH;Jeschke G;Stoll S
• 通讯作者: Stoll S

A novel approach to modeling side chain ensembles of the bifunctional spin label RX.

一种对双功能自旋标签 RX 侧链整体进行建模的新颖方法。

• DOI: 10.1101/2023.05.24.542139
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Tessmer,MaxxH;Stoll,Stefan
• 通讯作者: Stoll,Stefan