Tsay Karen, Keller Timothy, Fichou Yann, Freed Jack H, Han Song-I, Srivastava Madhur
Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA - 93106.
Institute of Chemistry and Biology of Membranes and Nano-object, French National Centre for Scientific Research, Bordeaux, France.
bioRxiv. 2025 Jan 3:2025.01.02.631084. doi: 10.1101/2025.01.02.631084.
Pulsed Dipolar ESR Spectroscopy (PDS) is a uniquely powerful technique to characterize the structural property of intrinsically disordered proteins (IDPs) and polymers and the conformational evolution of IDPs and polymers, e.g. during assembly, by offering the probability distribution of segment end-to-end distances. However, it is challenging to determine distance distribution () of IDPs by PDS because of the uncertain and broad shape information that is intrinsic to the distance distribution of IDPs. We demonstrate here that the Srivastava-Freed Singular Value Decomposition (SF-SVD) point-wise mathematical inversion method along with wavelet denoising (WavPDS) can aid in obtaining reliable shapes for the distance distribution, (), for IDPs. We show that broad regions of () as well as mixed narrow and broad features within the captured distance distribution range can be effectively resolved and differentiated without knowledge. The advantage of SF-SVD and WavPDS is that the methods are transparent, requiring no adjustable parameters, the processing of the magnitude for the probability distribution is performed separately for each distance increment, and the outcome of the analysis is independent of the user's judgement. We demonstrate the performance and present the application of WavPDS and SF-SVD on model ruler molecules, model polyethylene glycol polymers with end-to-end spin labeling, and IDPs with pairwise labeling spanning different segments of the protein tau to generate the transparent solutions to the ()'s including their uncertainties and error analysis.
脉冲偶极电子顺磁共振光谱法（PDS）是一种极具威力的独特技术，可用于表征内在无序蛋白质（IDP）和聚合物的结构特性，以及IDP和聚合物的构象演变，例如在组装过程中，通过提供链段端到端距离的概率分布来实现。然而，由于IDP距离分布固有的不确定且宽泛的形状信息，通过PDS确定IDP的距离分布（）具有挑战性。我们在此证明，Srivastava-Freed奇异值分解（SF-SVD）逐点数学反演方法与小波去噪（WavPDS）相结合，有助于获得IDP距离分布（）的可靠形状。我们表明，在不了解的情况下，捕获的距离分布范围内的（）的宽泛区域以及混合的窄峰和宽峰特征都可以有效分辨和区分。SF-SVD和WavPDS的优势在于方法透明，无需可调参数，对概率分布幅度的处理针对每个距离增量分别进行，且分析结果与用户判断无关。我们展示了WavPDS和SF-SVD在模型标尺分子、带有端到端自旋标记的聚乙二醇模型聚合物以及具有跨越蛋白质tau不同片段的成对标记的IDP上的性能，并展示了它们的应用，以生成（）的透明解决方案，包括其不确定性和误差分析。
