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: 10377588
• 负责人: Haribabu Arthanari
• 金额: $ 44.57万
• 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/10377588
• 关键词: Address; Algorithms; Amino Acid Sequence; Amino Acids; Biochemical; Biological Models; Biophysics; COSY; Cell Nucleus; Chemicals; Communities; Complement; Computational Technique; Computer software; Coupling; Crowding; Cryoelectron Microscopy; Data; Drug Design; Environment; Fingerprint; Frequencies; G-Protein-Coupled Receptors; Goals; HMQC; Hour; Investigation; Isotope Labeling; Isotopes; Label; Machine Learning; Mainstreaming; Mathematics; Measurement; Measures; Medical Research; Methods; Mission; Modernization; Molecular Weight; NMR Spectroscopy; Nuclear; Nuclear Magnetic Resonance; Outcome; Pattern; Physiologic pulse; Positioning Attribute; Protein Dynamics; Proteins; Pyruvate; Relaxation; Research; Research Proposals; Resolution; Sampling; Scheme; Science; Shapes; Side; Signal Transduction; Specialist; Spectrum Analysis; Structure; System; TOCSY; Testing; Textbooks; Therapeutic; Therapeutic Studies; Time; Training; Vertebral column; X-Ray Crystallography; artificial neural network; automated analysis; base; control theory; cost; design; experimental study; innovation; insight; instrument; methyl group; next generation; nonlinear regression; novel; novel strategies; nuclear power; protein function; quantum; radio frequency; reconstruction; 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）光谱对于研究结构，动力学和功能至关重要 蛋白质在近本条件下。 NMR研究对治疗发展具有重要意义。然而， 随着蛋白质中氨基酸的数量增加，NMR信号衰减（放松）速度更快，降低 灵敏度和解决方案，而频谱变得更加拥挤。在这些情况下，匹配是具有挑战性的 在蛋白质中观察到特定核的信号（称为“共振分配”），以便有意义 解释NMR数据。我们研究的总体目标是突破NMR的界限 关于当前棘手蛋白的动力学和功能的洞察力。该项目的目的是 设计一个NMR平台，该平台由协调的下一代生化，生物物理，数学，数学， 和计算技术。我们的平台围绕着一种原始的NMR光谱法建造 有关每个核的本地环境的新信息以其NMR的形状和模式进行编码 信号。理由是这些模式是“指纹” - 编码的复杂而独特的签名 有关哪个原子负责NMR频谱中每个共振峰的关键信息。我们将 使用两种创新方法设计并实现指纹图案：1）生物化学，通过有选择性地在生化上 将NMR活性同位素引入蛋白质样品中精心选择的位置，并以生物物理和生物物质和 2）使用专门的射频脉冲来准确控制确定的量子相互作用 NMR频谱。将使用已建立的算法结构来解码所得的指纹 机器学习，特别是人工神经网络。这将促进可访问的自动分析 致非NMR专家。我们的光谱法在理论研究中有望很重要 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）。当前来自此类蛋白质的NMR数据显示出明确的动态和与其他蛋白质的相互作用，但 由于难以将每个NMR峰与氨基酸相关的困难，因此无法正确解释 蛋白质序列。我们的平台将提供两个重要的结果：1）NMR共振分配 对以前棘手的系统的有意义分析。 2）启用非NMR专家，可以轻松从 通过分配的光谱来表达其蛋白质样品以使用NMR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 组建了一个领先的专家团队，使用自己的蛋白质系统测试我们的平台。