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

项目总结：了解生物大分子的结构和功能在 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。它经常是 了解分子病因学以解释临床表现也是必不可少的。核磁 共振谱是研究蛋白质结构的主要技术之一。 是理解缺乏固定三维结构的蛋白质生物学的主要技术-- 被称为内在无序蛋白(IDPs)-一组包括许多参与 生物矿化、细胞信号和核酸结合。然而，核磁共振光谱学受到限制。 这限制了它可以用来调查的蛋白质和国内流离失所者的大小和范围。这样做的总体目标是 建议开发和表征用于分析核磁共振数据的改进技术，以扩大 可行的蛋白质靶标。核磁共振的一个主要限制是固有的分辨率/灵敏度折衷，其中 分辨率(区分相似频率信号的能力)只有通过牺牲才能提高 灵敏度(区分信号和噪声的能力)，反之亦然。通常，核磁共振光谱学家可以 尝试通过制备同位素标记的样品或通过使用强大的 光谱仪和复杂的多维实验。各种数学操作可以是 应用于原始数据以进一步提高灵敏度或分辨率。虽然很有用，但这些技术 最终以这样或那样的方式在敏感性和分辨率之间进行权衡。最大化两种分辨率 在核磁共振的生物学应用中，灵敏度是至关重要的，因此，研究核磁共振技术 同时提升两者的潜力是必要的。我已经生成了初步数据，这很明显 提出了一种创新的数据处理技术，称为线宽最大熵重建 反卷积(反卷积)可以通过同时提高分辨率和灵敏度来绕过权衡 在多维核磁共振波谱中。通过减少信号重叠和缩小来实现反卷积功能 频谱噪声。该提案详细说明了常规数据处理与传统数据处理之间的首次系统比较 技术和反卷积。我将通过首先测试来使用三方研究策略来进行此比较 在精确设计的控制方案中的反卷积，其中理想的结果是已知的。那我就会 量化未知情况下的反卷积能力，最后我将使用反卷积来确定一个 蛋白质结构，并展示其实际益处。这些研究的量化结果将最终 确定反褶积是否可同时提高分辨率和灵敏度。它会 如果反褶积提供了好处，则构成对核磁共振波谱和结构生物学的突破 从我的数据来看。反卷积是一种实现成本低廉的尖端技术，具有 有可能为研究以前难以处理的蛋白质和内分泌蛋白提供必要的光谱改进。

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.