论文标题
通过压缩感应重建，通过反向卷积来增强蛋白质NMR光谱的分辨率增强
Resolution Enhancement in Protein NMR Spectra by Deconvolution with Compressed Sensing Reconstruction
论文作者
论文摘要
多维NMR光谱是确定生物分子结构的基本工具之一。不幸的是，光谱的分辨率通常受到核间耦合的限制。不能通过增加分辨率的通用方式，即不均匀采样（NUS），然后进行压缩感应（CS）重建，无法克服这种限制。在本文中，我们展示了如何通过虚拟解耦来丰富CS处理，从而提高了NUS重建的分辨率，灵敏度和整体质量。对解卷积方法对解耦的数学描述解释了噪声的影响，采样时间表的调节，并揭示了与CS的基本假设的关系。对于用于蛋白质主链分配的基本实验的分辨率和敏感性的增长3D HNCA应用于两个大型蛋白质系统：本质上无序的441个残留物tau和509个残留的球状球状细菌性植物植物色素片段。
Multidimensional NMR spectroscopy is one of the basic tools for determining the structure of biomolecules. Unfortunately, the resolution of the spectra is often limited by inter-nuclear couplings. This limitation cannot be overcome by common ways of increasing resolution, i.e. non-uniform sampling (NUS) followed by compressed sensing (CS) reconstruction. In this paper, we show how to enrich CS processing with virtual decoupling leading to an increase in resolution, sensitivity, and overall quality of NUS reconstruction. A mathematical description of the decoupling by deconvolution approach explains the effects of noise, modulation of the sampling schedule, and reveals relation with the underlying assumption of the CS. The gain in resolution and sensitivity is demonstrated for the basic experiment used for protein backbone assignment 3D HNCA applied to two large protein systems: intrinsically disordered 441-residue Tau and a 509-residue globular bacteriophytochrome fragment.
