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
• 负责人: Haribabu Arthanari
• 金额: $ 1.33万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10392661
• 关键词: Algorithms; Amino Acid Sequence; Amino Acids; Biochemical; Biophysics; Cell Nucleus; Computational Technique; Crowding; Data; Environment; Fingerprint; G-Protein-Coupled Receptors; Goals; Isotope Labeling; Isotopes; Machine Learning; Mathematics; Medical Research; Methods; Mission; NMR Spectroscopy; Nuclear Magnetic Resonance; Outcome; Pattern; Physiologic pulse; Positioning Attribute; Protein Dynamics; Proteins; Research; Resolution; Sampling; Science; Shapes; Signal Transduction; Specialist; Spectrum Analysis; Structure; System; Testing; Therapeutic Studies; artificial neural network; automated analysis; design; innovation; insight; next generation; protein function; quantum; radio frequency; therapeutic development

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 核磁共振波谱对于研究细胞的结构、动力学和功能是必不可少的 蛋白质在近乎天然的条件下。核磁共振研究对治疗开发具有重要意义。然而， 随着蛋白质中氨基酸数量的增加，核磁共振信号衰减(松弛)更快，产生更低的 灵敏度和分辨率，而光谱变得更加拥挤。在这些情况下，匹配是具有挑战性的 观察到蛋白质中特定原子核的信号(称为“共振分配”)，以便有意义地 解释核磁共振数据。我们研究的首要目标是推动核磁共振的边界，使有价值的 对目前难以处理的蛋白质的动态和功能的洞察。这个项目的目标是 设计一个由协调的、下一代生化、生物物理、数学、 和计算技术。我们的平台是围绕核磁共振波谱的原创方法而建立的，其中 关于每个原子核的局部环境的新信息被编码在其核磁共振的形状和图案中 信号。其基本原理是，这些图案是一个“指纹”--一种复杂而独特的编码签名 核磁共振谱中每个共振峰由哪个原子负责的关键信息。我们会 使用两种创新方法设计和实现指纹模式：1)生物化学，通过有选择地 将核磁共振活性同位素引入蛋白质样品中精心选择的位置，并在生物物理上，以及 2)通过使用特殊的射频脉冲来精确控制决定 核磁共振波谱。生成的指纹将使用建立的算法结构从 机器学习，特别是人工神经网络。这将促进可访问的自动化分析 给非核磁共振专家。我们的光谱学方法在研究治疗上的重要性方面很有希望 在真核表达系统中表达的蛋白质(例如G蛋白偶联受体和糖基化的 蛋白质)。目前来自这类蛋白质的核磁共振数据显示了明显的动力学和与其他蛋白质的相互作用，但 目前还不能正确解释，因为很难将每个核磁共振峰与 蛋白质序列。我们的平台将提供两个重要的结果：1)核磁共振指定 对以前难以处理的系统进行有意义的分析。2)使非核磁共振专家能够轻松地从 表达他们的蛋白质样品，使用核磁共振通过指定的光谱来研究动力学和相互作用。这 将对蛋白质科学和医学研究产生积极影响。为了支持我们的使命，我们有 组建了一个由一流专家组成的团队，用他们自己的蛋白质系统测试我们的平台。