Room Temperature Protein Conformational Dynamics at Microsecond Timescales

项目摘要：生物分子的结构、动力学和功能在决定 疾病机制，其知识对于早期诊断、药物开发和有效是必不可少的 治疗。许多生物学研究侧重于生物分子的结构测定，而对动力学的研究较少。 对于了解疾病机制，包括它们的功能和与环境的相互作用至关重要。 与其他生物物理方法相比，多频二维电子自旋共振(ESR)谱具有 研究大范围生理温度下蛋白质结构动力学的有效方法 时间尺度(子𝑛𝑠到数十𝜇𝑠)，可以提供运动的详细描述，包括两种动力学 以及局部结构排序。尽管取得了重大进展，但多频2D-ESR缺乏足够的灵敏度 在𝜇𝑠时间尺度上研究生物系统所需的分辨率，因为信号在很大程度上由 信噪比(SNR)为单位的噪声几乎看不见。为了解决这个问题， 拟议的研究将开发基于小波变换的计算方法来消除噪声 准确的信号恢复。所提出的研究旨在发展多维小波去噪 对信噪比为1的多频2D-ESR信号进行去噪处理，扩展了一维小波去噪方法。小波变换 提供一种强大的方法来消除噪声，因为他们专注于将噪声从活动对象信号中分离出来 在信号处理领域。该方法将包括信号的多维表示， 新小波的发展，小波域信号分辨率的提高，以及噪声的发展 基于明确定义的统计定理的阈值，所有这些都将有助于从信号中分离噪声。 还将制定和采用一种新的标准来量化噪音和不确定性。一种新的去噪方法 这些方法将被用来揭示具有良好特性的T4溶菌酶蛋白的构象动力学，并 了解生理状态下从𝑛𝑠到数十𝜇𝑠时间尺度的脂质-跨膜相互作用 温度和浓度，以了解与疾病相关的信号通路。这将导致一个 对构象亚态和构象亚态之间交换时间尺度上的蛋白质动力学的详细理解 将创造一个可以研究生物复合体运动的平台，目前仍难以捉摸 并具有关键的功能重要性。在生理条件下测量汇率是一种新的方法 实验前沿和寿命预计在𝜇𝑠的范围内。它还将为使用 从实验信号中去除噪声并允许其在数据过程中应用的数据处理方法 用于实时处理的采集。数据处理方法成本低廉，易于实现，而且容易实现 可扩展到现有仪器。

Project Summary: The structure, dynamics and function of a biomolecule play a key role in determining disease mechanisms, knowledge of which is essential for early diagnosis, drug development and effective treatment. Many biological studies focus on structure determination of biomolecules but the study of dynamics is vital to understand disease mechanisms, including their function and interaction with their environment. Compared to other biophysical methods, multi-frequency 2D Electron Spin Resonance (ESR) spectroscopy are powerful methods for studying structural dynamics of proteins at physiological temperatures for a wide range of time scales (sub-𝑛𝑠 to tens of 𝜇𝑠) and can provide a detailed description of motion that includes both dynamics as well as local structural ordering. Despite major advances, multi-frequency 2D-ESR lack sufficient sensitivity and resolution needed to study biological systems at 𝜇𝑠 timescales, because the signals are heavily dominated by noise with Signal-to-Noise Ratios (SNRs) of unity and so are hardly visible. To address this problem, the proposed research will develop computational methods based on wavelet transforms to remove noise for accurate signal recovery. The proposed research is aimed at developing multidimensional wavelet denoising for multi-frequency 2D-ESR signals at SNR ~ 1, extending the 1D wavelet denoising approach. Wavelet transforms provide a powerful approach to remove noise as they focus on separating noise from the signal, an active subject in the field of signal processing. The methods will include multi-dimensional representation of signals, development of new wavelets, enhancement in signal resolution in the wavelet domain, and development of noise thresholds based on well-defined statistical theorems, all of which will contribute to separate noise from signals. A new criterion will also be developed and adopted to quantify noise and uncertainty. The new denoising methods will be applied to reveal conformational dynamics of a well-characterized T4 Lysozyme protein and to understand lipid-transmembrane interactions ranging from 𝑛𝑠 to tens of 𝜇𝑠 time scales at physiological temperatures and concentrations for understanding signaling pathways related to diseases. This will lead to a detailed understanding of protein dynamics at the time scale of exchange between conformational substates and will create a platform for which motions of biological complexes can be studied, which currently remains elusive and are of key functional importance. Measurement of exchange rates under physiological conditions is a new experimental frontier and lifetimes in the range of 𝜇𝑠 are anticipated. It will also lay the foundation for using data processing methods to remove noise from experimental signals and permit their application during data acquisition for real-time processing. Data processing methods are inexpensive, easy-to-implement, and easily scalable to existing instruments.