Novel Computational Techniques to Expand the Scope of Protein Nuclear Magnetic Resonance Spectroscopy

扩大蛋白质核磁共振波谱范围的新型计算技术

• 批准号: 9293124
• 负责人: Matthew Zambrello
• 金额: $ 3.92万
• 依托单位: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9293124
• 关键词: Binding; Biological; Biological Process; Biology; Bypass; Cancer Biology; Chemicals; Clinical; Complex; Computational Technique; Data; Dimensions; Discipline; Disease; Drug Compounding; Drug Design; Drug Targeting; Entropy; Enzymes; Etiology; Frequencies; Goals; Investigation; Isotope Labeling; Location; Malaria; Mathematics; Methods; Molecular; Molecular Structure; Molecular Weight; NMR Spectroscopy; Nanotechnology; Noise; Nuclear Protein; Nucleic Acid Binding; Outcome; Parasites; Pattern; Plasmodium falciparum; Preparation; Process; Proteins; Research; Resolution; Sampling; Science; Signal Transduction; Spectrum Analysis; Structure; Techniques; Technology; Testing; biomineralization; computerized data processing; design; drug development; experimental study; improved; innovation; macromolecule; molecular dynamics; novel; phosphoethanolamine methyltransferase; protein structure; reconstruction; signal processing; structural biology; three dimensional structure; tool

Project Summary: Understanding the structure and function of biological macromolecules is critical in countless biomedical disciplines, including cancer biology, drug design, and nanotechnology. It is often essential to understand molecular etiology to interpret a clinical presentation as well. Nuclear Magnetic Resonance Spectroscopy (NMR) is one of the principal techniques for investigation of protein structure and it is the primary technique for understanding the biology of proteins that lack fixed three-dimensional structures – termed intrinsically disordered proteins (IDPs) – a group that includes numerous proteins involved in biomineralization, cell signaling, and nucleic acid binding. However, NMR spectroscopy suffers from limitations that restrict the size and scope of proteins and IDPs that it can be used to investigate. The broad goal of this proposal is to develop and characterize improved techniques for analyzing NMR data to expand the set of feasible protein targets. One central limitation of NMR is the inherent resolution/sensitivity tradeoff in which resolution (the ability to discriminate signals with similar frequency) can be enhanced only by sacrificing sensitivity (the ability to distinguish signal from noise), or vice versa. Generally, an NMR spectroscopist may try to overcome these limitations through preparation of isotopically labeled samples or by using powerful spectrometers and sophisticated multidimensional experiments. Various mathematical manipulations can be applied to the raw data for further enhancement of sensitivity or resolution. Although useful, these techniques ultimately force a tradeoff between sensitivity and resolution in one way or another. Maximizing both resolution and sensitivity is critical in the biological applications of NMR, and therefore investigation of techniques with the potential to simultaneously enhance both is necessary. I have generated preliminary data, which strongly suggests that an innovative data processing technique called Maximum Entropy Reconstruction with linewidth deconvolution (deconvolution) may bypass the tradeoff by simultaneously enhancing resolution and sensitivity in multidimensional NMR spectra. Deconvolution functions by reducing signal overlap and scaling down spectral noise. This proposal details the first systematic comparison between conventional data processing techniques and deconvolution. I will conduct this comparison using a tripartite research strategy by first testing deconvolution in a precisely designed control scenario, in which the ideal outcome is known. Then I will quantify the abilities of deconvolution in unknown situations and finally I will use deconvolution to determine a protein structure and demonstrate its practical benefits. The quantitative results of these studies will definitively determine if deconvolution provides simultaneous enhancement of resolution and sensitivity. It would constitute a breakthrough for NMR spectroscopy and structural biology if deconvolution provides the benefits suggested by my data. Deconvolution is a cutting-edge technique that is inexpensive to implement and has the potential to provide the necessary spectral improvements for studying previously intractable proteins and IDPs.

项目摘要：了解生物大分子的结构和功能对于生物大分子的研究至关重要 无数的生物医学学科，包括癌症生物学、药物设计和纳米技术。常常是 对于理解分子病因学以解释临床表现也至关重要。核磁 共振光谱 (NMR) 是研究蛋白质结构的主要技术之一，它 是了解缺乏固定三维结构的蛋白质生物学的主要技术 - 称为本质无序蛋白 (IDP) – 一组包含许多参与 生物矿化、细胞信号传导和核酸结合。然而，核磁共振波谱法也存在局限性 限制了可用于研究的蛋白质和 IDP 的大小和范围。此次活动的总体目标是 该提案的目的是开发和表征用于分析 NMR 数据的改进技术，以扩展 可行的蛋白质目标。 NMR 的一个主要限制是固有的分辨率/灵敏度权衡，其中 分辨率（区分具有相似频率的信号的能力）只能通过牺牲来增强 灵敏度（区分信号和噪声的能力），反之亦然。一般来说，核磁共振波谱学家可以 尝试通过制备同位素标记样品或使用强大的技术来克服这些限制 光谱仪和复杂的多维实验。可以进行各种数学运算 应用于原始数据以进一步提高灵敏度或分辨率。这些技术虽然有用，但 最终以某种方式迫使灵敏度和分辨率之间进行权衡。最大化分辨率 灵敏度在 NMR 的生物应用中至关重要，因此对技术进行研究 同时增强两者的潜力是必要的。我已经生成了初步数据，这些数据强烈 建议采用一种称为线宽最大熵重建的创新数据处理技术 反卷积（devolving）可能会通过同时增强分辨率和灵敏度来绕过权衡 在多维核磁共振谱中。通过减少信号重叠和缩小比例来实现反卷积功能 频谱噪声。该提案详细介绍了传统数据处理之间的首次系统比较 技术和反卷积。我将通过首先测试使用三方研究策略进行比较 在精确设计的控制场景中进行反卷积，其中理想结果是已知的。那我就 量化未知情况下反卷积的能力，最后我将使用反卷积来确定 蛋白质结构并展示其实际益处。这些研究的定量结果将明确 确定反卷积是否可以同时增强分辨率和灵敏度。它会 如果反卷积提供了好处，那么将构成核磁共振波谱学和结构生物学的突破 根据我的数据建议。反卷积是一种尖端技术，实施成本低廉，并且具有 为研究以前难以处理的蛋白质和 IDP 提供必要的光谱改进。