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.

项目摘要 核磁共振（NMR）光谱学对于研究化合物的结构、动力学和功能至关重要。 蛋白质在近自然条件下NMR研究对治疗发展具有重要意义。然而，在这方面， 随着蛋白质中氨基酸数量的增加，NMR信号衰减（弛豫）更快，产生更低的 灵敏度和分辨率，而频谱变得更加拥挤。在这些情况下， 观察到蛋白质中特定核的信号（称为“共振分配”），以便有意义地 解释NMR数据。我们研究的首要目标是推动NMR的边界， 深入了解目前棘手的蛋白质的动力学和功能。该项目的目标是 设计一个NMR平台，由协调的下一代生物化学，生物物理，数学， 和计算技术。我们的平台是围绕NMR光谱学的原始方法构建的， 关于每个原子核局部环境的新信息都被编码在其核磁共振的形状和模式中 信号了其基本原理是，这些模式是一个“指纹”-一个复杂的和独特的签名，编码 关于哪个原子负责NMR光谱中的每个共振峰的关键信息。我们将 设计和实现指纹图案使用两种创新的方法：1）生物化学，通过选择性 - 将NMR活性同位素引入蛋白质样品中仔细选择的位置，和生物鉴定，和 2)通过使用专门的射频脉冲来精确控制量子相互作用， NMR谱。所得到的指纹将使用已建立的算法结构进行解码， 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治疗上重要的 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 蛋白质）。目前来自这些蛋白质的NMR数据显示出清晰的动力学和与其他蛋白质的相互作用， 还不能正确解释，因为很难将每个NMR峰与氨基酸中的氨基酸联系起来。 蛋白质序列我们的平台将提供两个重要的成果：1）NMR共振分配 有意义的分析以前棘手的系统。2)使非NMR专家能够轻松地从 表达他们的蛋白质样品，使用NMR通过指定的光谱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 组建了一个领先的专家团队，用他们自己的蛋白质系统测试我们的平台。