Differentiating Unimodal and Multimodal Distributions in Pulsed Dipolar Spectroscopy Using Wavelet Transforms
Differentiating Unimodal and Multimodal Distributions in Pulsed Dipolar Spectroscopy Using Wavelet Transforms
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DOI：
10.1007/s00723-023-01616-w
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发表时间：
2023-09-22
影响因子：
1
通讯作者：
Srivastava，Madhur
中科院分区：
文献类型：
--
作者：
Sinha Roy，Aritro;Freed，Jack H.;Srivastava，Madhur
定点自旋标记已经使得能够使用电子自旋共振脉冲偶极光谱（PDS）来确定蛋白质结构。距离分布中的小细节可能是理解重要蛋白质结构-功能关系的关键。一个主要的挑战是区分单峰和重叠的多峰距离分布。它们通常产生相似的分布和偶极信号。目前的无模型距离重建技术，如Srivastava-Freed奇异值分解和Tikhonov正则化，可以抑制这些小特征的不确定性和/或误差范围，尽管存在。在这项工作中，我们证明了连续小波变换（CWT）可以区分PDS信号的单峰和多峰的距离分布。我们表明，CWT表示的周期性反映了单峰分布，这是掩盖了多峰的情况。这项工作意味着交叉验证技术的先驱，它可以指示距离分布的模态。
Site-directed spin labeling has enabled protein structure determination using electron spin resonance pulsed dipolar spectroscopy (PDS). Small details in a distance distribution can be key to understanding important protein structure–function relationships. A major challenge has been to differentiate unimodal and overlapped multimodal distance distributions. They often yield similar distributions and dipolar signals. Current model-free distance reconstruction techniques, such as Srivastava-Freed singular value decomposition and Tikhonov regularization, can suppress these small features in uncertainty and/or error bounds, despite being present. In this work, we demonstrate that continuous wavelet transform (CWT) can distinguish PDS signals from unimodal and multimodal distance distributions. We show that periodicity in CWT representation reflects unimodal distributions, which is masked for multimodal cases. This work is meant as a precursor to a cross-validation technique, which could indicate the modality of the distance distribution.