NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins

NMR 指纹图谱:利用最佳控制脉冲设计、定制同位素标记和机器学习来研究棘手的蛋白质

基本信息

  • 批准号:
    10377588
  • 负责人:
  • 金额:
    $ 44.57万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-06-01 至 2025-03-31
  • 项目状态:
    未结题

项目摘要

Project summary Nuclear magnetic resonance (NMR) spectroscopy is essential for the study structure, dynamics and function of proteins in near-native conditions. NMR studies have vital implications for therapeutic development. However, as the number of amino acids in the protein increases, NMR signals decay (relax) faster, yielding lower sensitivity and resolution, while the spectrum becomes more crowded. In these cases it is challenging to match observed signals to specific nuclei in the protein (called `resonance assignment') in order to meaningfully interpret NMR data. The overarching goal of our research is to push the boundaries of NMR enabling valuable insight about the dynamics and functions of currently intractable proteins. The objective of this project is to design an NMR platform consisting of coordinated, next-generation biochemical, biophysical, mathematical, and computational techniques. Our platform is built around an original approach to NMR spectroscopy in which new information about the local environment of each nucleus is encoded in the shape and pattern of its NMR signal. The rationale is that these patterns are a `fingerprint' – an intricate and unique signature that encodes key information about which atom is responsible for each resonance peak in the NMR spectrum. We will design and realize fingerprint patterns using two innovative approaches: 1) biochemically, by selectively introducing NMR-active isotopes into carefully chosen positions in the protein samples, and biophysically, and 2) by using specialized radiofrequency pulses to accurately control the quantum interactions that determine the NMR spectrum. The resulting fingerprints will be decoded using established algorithmic structures from machine learning, notably artificial neural networks. This will facilitate automated analyses that are accessible to non-NMR specialists. Our approach to spectroscopy holds promise in the study of therapeutically important proteins expressed in eukaryotic expression systems (e.g. G-protein coupled receptors and glycosylated proteins). Current NMR data from such proteins shows clear dynamics and interactions with other proteins, but cannot yet be properly interpreted because of the difficulty of relating each NMR peak to an amino acid in the protein sequence. Our platform will deliver two significant outcomes: 1) NMR resonance assignment for meaningful analyses of previously intractable systems. 2) Enable non-NMR specialists, to easily proceed from expressing their protein sample to using NMR to study dynamics and interactions via assigned spectra. This will have a positive impact on protein science and medical research. To support our mission we have assembled a team of leading experts to test our platform with their own protein systems.
项目总结

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Haribabu Arthanari其他文献

Haribabu Arthanari的其他文献

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{{ truncateString('Haribabu Arthanari', 18)}}的其他基金

NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins
NMR 指纹图谱:利用最佳控制脉冲设计、定制同位素标记和机器学习来研究棘手的蛋白质
  • 批准号:
    10591671
  • 财政年份:
    2020
  • 资助金额:
    $ 44.57万
  • 项目类别:
NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins
NMR 指纹图谱:利用最佳控制脉冲设计、定制同位素标记和机器学习来研究棘手的蛋白质
  • 批准号:
    10392661
  • 财政年份:
    2020
  • 资助金额:
    $ 44.57万
  • 项目类别:
NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins
NMR 指纹图谱:利用最佳控制脉冲设计、定制同位素标记和机器学习来研究棘手的蛋白质
  • 批准号:
    10387787
  • 财政年份:
    2020
  • 资助金额:
    $ 44.57万
  • 项目类别:
NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins
NMR 指纹图谱:利用最佳控制脉冲设计、定制同位素标记和机器学习来研究棘手的蛋白质
  • 批准号:
    10594969
  • 财政年份:
    2020
  • 资助金额:
    $ 44.57万
  • 项目类别:
NMR Fingerprinting: Leveraging optimal control pulse design, tailored isotope labeling, and machine learning to study intractable proteins
NMR 指纹图谱:利用最佳控制脉冲设计、定制同位素标记和机器学习来研究棘手的蛋白质
  • 批准号:
    10159285
  • 财政年份:
    2020
  • 资助金额:
    $ 44.57万
  • 项目类别:
Novel targets to regulate NF-kB & SREBP activity: an approach to combat diabetes
调节 NF-kB 的新靶点
  • 批准号:
    8150351
  • 财政年份:
    2010
  • 资助金额:
    $ 44.57万
  • 项目类别:
Novel targets to regulate NF-kB & SREBP activity: an approach to combat diabetes
调节 NF-kB 的新靶点
  • 批准号:
    8318211
  • 财政年份:
    2010
  • 资助金额:
    $ 44.57万
  • 项目类别:
Novel targets in the regulation of NF-kB and SREBP activity: A two-faceted approa
NF-kB 和 SREBP 活性调节的新目标:两方面的方法
  • 批准号:
    8045247
  • 财政年份:
    2010
  • 资助金额:
    $ 44.57万
  • 项目类别:

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