Sparse NMR Labeling Approach to Glycoprotein Structure and Function

糖蛋白结构和功能的稀疏 NMR 标记方法

基本信息

批准号：
10388355
负责人：
JAMES H. PRESTEGARD
金额：
$ 30.2万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10388355
关键词：
3-Dimensional Address Adopted Affect Amides Amino Acids Binding Proteins Biological Biology Cell Culture Techniques Chemicals Collection Communities Computer Models Computer software Crystallization Data Data Analyses Data Set Development Disease Equilibrium Exclusion Genetic Programming Glycoproteins Goals HMQC Hand Health Homology Modeling Human Isotope Labeling Isotopes Label Magnetism Mammalian Cell Manuals Measurement Measures Methods Modeling Motion Mutation Nuclear Nuclear Magnetic Resonance Output Physiology Procedures Process Production Program Efficiency Proteins Protocols documentation Protons Relaxation Research Personnel Residual state Resolution Route Scheme Site Source Structural Models Structure Surface System Technology Time Validation Weight Work X-Ray Crystallography base computer generated design experimental study glycoprotein structure glycosylation improved methyl group multiple data types mutant pathogen preservation prevent programs protein protein interaction protein structure simulation

项目摘要

SUMMARY Glycoproteins represent a class of mammalian proteins that presents challenges for structural and functional characterization, particularly if the native glycosylation, which affects structure, stability and interaction with other molecules, is to be preserved. The best route to proteins with native glycosylation is expression in mammalian cell cultures. Unfortunately, for X-ray crystallography, this produces proteins with heterogenous glycosylation, often preventing formation of suitable crystals. For traditional nuclear magnetic resonance (NMR) methods, this forces use of expensive substrates for isotopic labeling and prohibits the perdeuteration often required to maintain resolution for larger proteins. The investigators involved in this proposal have worked together to develop an efficient mammalian cell expression system that produces proteins sparsely labeled using a restricted set of less expensive isotope enriched amino acids and maintains resolution without the aid of perdeuteration. This has been accompanied by the development of resonance assignment programs and data analysis protocols that allow structural and functional characterization from basic, high sensitivity, two-(and three-)dimensional NMR experiments. This project is designed to turn those developments into an integrated protocol that can be adopted by an expanded community of users. It centers on the refinement of a software package that accomplishes NMR resonance assignment of sparsely labeled proteins. It will be bolstered by extensive validation of program output, introduction of new data types and data analysis methods, and extension of program capabilities to the refinement of computer-generated models for protein structure. The potential impact will be a new route to structure and function studies of a class of proteins intimately involved with human physiology and disease.

概括糖蛋白代表一类哺乳动物蛋白质，对结构和蛋白质结构提出了挑战。功能表征，特别是天然糖基化，它会影响结构、稳定性和与其他分子的相互作用，将被保留。获得具有天然糖基化的蛋白质的最佳途径是在哺乳动物细胞培养物中表达。不幸的是，对于 X 射线晶体学来说，这会产生具有异质糖基化，通常会阻止合适晶体的形成。对于传统核磁共振（NMR）方法，这迫使使用昂贵的底物进行同位素标记并禁止通常需要全氘化来维持较大蛋白质的分辨率。涉案调查人员该提案共同开发了一种高效的哺乳动物细胞表达系统，可产生使用一组有限的较便宜的同位素富集氨基酸稀疏地标记蛋白质，并保持无需置换的帮助即可解决。这伴随着共振的发展分配程序和数据分析协议，允许结构和功能表征基本、高灵敏度、二维（和三维）核磁共振实验。该项目旨在将这些开发成一个可以被扩大的用户社区采用的集成协议。它集中关于完成稀疏标记的 NMR 共振分配的软件包的改进蛋白质。它将通过对程序输出的广泛验证、新数据类型和数据的引入来支持分析方法，以及程序功能的扩展，以细化计算机生成的模型蛋白质结构。潜在的影响将是一类结构和功能研究的新途径与人类生理和疾病密切相关的蛋白质。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins.

DOI：
10.1016/j.jmr.2022.107336
发表时间：
2022-12
期刊：
JOURNAL OF MAGNETIC RESONANCE
影响因子：
2.2
作者：
V. Williams, Robert;Rogals, Monique J.;Eletsky, Alexander;Huang, Chin;Morris, Laura C.;Moremen, Kelley W.;Prestegard, James H.
通讯作者：
Prestegard, James H.

Glycan Conformation in the Heavily Glycosylated Protein, CEACAM1.

DOI：
10.1021/acschembio.2c00714
发表时间：
2022-12-16
期刊：
ACS CHEMICAL BIOLOGY
影响因子：
4
作者：
Rogals, Monique J.;Eletsky, Alexander;Huang, Chin;Morris, Laura C.;Moremen, Kelley W.;Prestegard, James H.
通讯作者：
Prestegard, James H.

Using molecular dynamics trajectories to predict nuclear spin relaxation behaviour in large spin systems.