Vladislav Yu. Orekhov

arXiv2020

Resolution Enhancement in Protein NMR Spectra by Deconvolution with Compressed Sensing Reconstruction

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.

physics.data-an↗

arXiv2014

Causality principle in reconstruction of sparse NMR spectra

Rapid development of sparse sampling methodology offers dramatic increase in power and efficiency of magnetic resonance techniques in medicine, chemistry, molecular structural biology, and other fields. We suggest to use available yet usually unexploited prior knowledge about the phase and the causality of the sparsely detected NMR signal as a general approach for a major improvement of the spectra quality. The work gives a theoretical framework of the method and demonstrates notable improvement of the protein spectra reconstructed with two commonly used state-of-the-art signal processing algorithms, compressed sensing and SIFT.

physics.chem-ph↗