Room Temperature Protein Conformational Dynamics at Microsecond Timescales

项目概述：生物分子的结构、动力学和功能在决定生物分子的生物学行为中起着关键作用。 疾病机制，了解这一点对于早期诊断、药物开发和有效治疗至关重要。 治疗许多生物学研究都集中在确定生物分子的结构上， 对于了解疾病机制至关重要，包括它们的功能以及与环境的相互作用。 与其他生物物理方法相比，多频2D电子自旋共振（ESR）光谱是 研究蛋白质在生理温度下的结构动力学的强大方法， 时间尺度（亚千分之一至数十千分之一），并可以提供详细的运动描述，包括动态 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 和分辨率需要研究生物系统的时间尺度，因为信号是严重主导的， 具有单位信噪比（SNR）的噪声，因此几乎不可见。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，以消除噪声， 准确的信号恢复。所提出的研究旨在发展多维小波去噪， 多频2D-ESR信号在SNR ~ 1，扩展了一维小波去噪方法。小波变换 提供了一种强大的方法来消除噪声，因为他们专注于从信号中分离噪声， 在信号处理领域。该方法将包括信号的多维表示， 新小波的开发、小波域中信号分辨率的提高以及噪声的发展 基于定义明确的统计定理的阈值，所有这些都将有助于从信号中分离噪声。 还将制定和采用一个新的标准来量化噪音和不确定性。新的去噪 方法将被应用于揭示一个良好表征的T4溶菌酶蛋白的构象动力学， 了解脂质-跨膜相互作用，从生理上的1000到几十个1000的时间尺度 温度和浓度，以了解与疾病相关的信号通路。这将导致 详细了解蛋白质动力学的时间尺度之间的交换构象substates和 将创建一个平台，用于研究生物复合体的运动，这一点目前仍然难以实现 并且具有关键的功能重要性。在生理条件下测量汇率是一个新的 预期实验前沿和寿命在10000年范围内。这也将为使用 数据处理方法，从实验信号中去除噪声，并允许在数据处理过程中应用 实时处理的采集。数据处理方法价格低廉，易于实现， 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.