课题基金 / 基金详情

Characterising structure, interactions and dynamics of large molecular machines and intrinsically disordered proteins using novel carbon-detected NMR

Characterising structure, interactions and dynamics of large molecular machines and intrinsically disordered proteins using novel carbon-detected NMR
使用新型碳检测 NMR 表征大分子机器和本质无序蛋白质的结构、相互作用和动力学
批准号:
BB/R000255/1
负责人:
Flemming Hansen
金额:
$26.47万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

项目摘要

项目成果

Flemming Hansen的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
In nuclear magnetic resonance (NMR) spectroscopy nuclear magnetisations, such as proton, carbon and nitrogen, are excited using specifically designed radio waves (pulse sequences) and the resulting radio wave transmitted from the sample provides the researcher with information about the local environment of the nucleus in question and its motional properties. NMR spectroscopy is therefore often the preferred tool to elucidate molecular structure, flexibility, and interactions since NMR provides descriptions of the molecular dynamics and interactions at atomic resolution. The main aim of the proposed research is to explore and develop a relative avenue in biomolecular NMR spectroscopy, that it, carbon-detected NMR spectroscopy. Our new applications will allow us to, among others, visualise the motions and mechanisms of macromolecular machines.Within the research areas of protein complexes, macromolecular machines and intrinsically disordered proteins the main body of NMR spectroscopic applications rely on detection of proton magnetisations. This is because the magnetic strength of the proton is four times stronger than that of carbon and ten times stronger than that of nitrogen, thereby giving raise to stronger NMR signals that are easier to detect. However, the spin-physics and the nuclear interactions that govern the linewidth of the NMR signals also depend on the magnetic strength of the interacting nuclei and researchers have therefore been diluting the protons out in molecular machines to sharpen the NMR signals, avoid overlaps of signals, and thereby facilitate new characterisations.Others and the applicants of this proposal have recently shown the potential in using carbon-detected NMR spectroscopy for the investigation of large proteins and also intrinsically disordered proteins. Although the carbon-detected NMR signals are weaker than the proton detected signals there are many benefits that we are planning on exploiting and developing further with the new equipment that we are applying for. Firstly, carbon signals are in general sharper than the corresponding proton signals - because of their lower magnetic strength. Secondly, the average distance between carbon signals are much higher than that of proton signals, thereby leading to significantly less overlap of signals and facilitating analyses of the underlying parameters. In general terms, this means that we will be able to monitor motions and interactions of proteins during the formations of their structure (protein folding), macromolecular machines such as the cells protein producing machinery (Ribosome) and DNA remodelling enzymes (histone deacetylases). We all have excellent track-records in applying and developing new methodologies for NMR spectroscopy also within the field of carbon-detected NMR.The research will be carried out at the Institute of Structural and Molecular Biology (ISMB) at University College London (UCL). A state-of-the-art and stimulating research environment with dedicated NMR machines that are optimised for detection of carbon nuclei, and the highly collaborative environment and world-class expertise open up the possibility for many fruitful collaborations across disciplines and many novel applications to come.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
FID-Net: A Versatile Deep Neural Network Architecture for NMR Spectral Reconstruction and Virtual Decoupling
FID-Net:用于 NMR 谱重建和虚拟解耦的多功能深度神经网络架构
DOI: 10.26434/chemrxiv.13295888.v1
发表时间: 2020
期刊:
影响因子: --
作者: [Karunanithy G]
通讯作者: Karunanithy G
Virtual Homonuclear Decoupling in Direct Detection NMR Experiments using Deep Neural Networks
使用深度神经网络直接检测 NMR 实验中的虚拟同核解耦
DOI: 10.26434/chemrxiv-2021-zs4pl-v2
发表时间: 2021
期刊:
影响因子: --
作者: [Karunanithy G]
通讯作者: Karunanithy G
Small-molecule binding to an intrinsically disordered protein revealed by experimental NMR 19 F transverse spin-relaxation
实验 NMR 19 F 横向自旋弛豫揭示了小分子与本质上无序的蛋白质的结合
DOI: 10.1101/2023.05.03.539297
发表时间: 2023
期刊:
影响因子: --
作者: [Heller G]
通讯作者: Heller G
Cover Feature: A Chemical Biology Approach to Understanding Molecular Recognition of Lipid II by Nisin(1-12): Synthesis and NMR Ensemble Analysis of Nisin(1-12) and Analogues (Chem. Eur. J. 64/2019)
封面专题:了解乳链菌肽 (1-12) 对脂质 II 分子识别的化学生物学方法:乳链菌肽 (1-12) 和类似物的合成和 NMR 整体分析(Chem. Eur. J. 64/2019)
DOI: 10.1002/chem.201903848
发表时间: 2019
期刊: Chemistry - A European Journal
影响因子: --
作者: [Dickman R]
通讯作者: Dickman R
DeepNMR: Unleashing the full potential of NMR spectroscopy with artificial intelligence and deep learning
  • 批准号:
    EP/X036782/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $274.36万
  • 财政年份:
    2023
  • 负责人:
    Flemming Hansen
  • 依托单位:
Developing Artificial Intelligence and Deep Learning for the analysis of correlation spectroscopy data
  • 批准号:
    BB/T011831/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $16.98万
  • 财政年份:
    2020
  • 负责人:
    Flemming Hansen
  • 依托单位:
Dynamic post-translational histone modifications studied by NMR spectroscopy
  • 批准号:
    BB/H022570/1
  • 项目类别:
    Fellowship
  • 资助金额:
    $166.39万
  • 财政年份:
    2010
  • 负责人:
    Flemming Hansen
  • 依托单位:
国内基金
海外基金
Rh-N4位点催化醇类氧化反应的微观机制与构效关系研究
  • 批准号:
    22302208
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30.00万元
  • 批准年份:
    2023
  • 负责人:
    王翔
  • 依托单位:
体内亚核小体图谱的绘制及其调控机制研究
  • 批准号:
    32000423
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    温增麒
  • 依托单位:
水稻H3K27me3标记基因的三维基因组结构解析及其调控抽穗期的机理研究
  • 批准号:
    32070612
  • 项目类别:
    面上项目
  • 资助金额:
    58.0万元
  • 批准年份:
    2020
  • 负责人:
    李兴旺
  • 依托单位:
稻瘟病菌中蛋白激酶MoCK2参与附着胞极性生长影响致病性的初步探索
  • 批准号:
    32060597
  • 项目类别:
    地区科学基金项目
  • 资助金额:
    35.0万元
  • 批准年份:
    2020
  • 负责人:
    张连虎
  • 依托单位: