An analysis of double-quantum coherence ESR in an N-spin system: Analytical expressions and predictions.

Electron spin resonance pulsed dipolar spectroscopy (PDS) has become popular in protein 3D structure analysis. PDS studies yield distance distributions between a pair or multiple pairs of spin probes attached to protein molecules, which can be used directly in structural studies or as constraints in theoretical predictions. Double-quantum coherence (DQC) is a highly sensitive and accurate PDS technique to study protein structures in the solid state and under physiologically relevant conditions. In this work, we have derived analytical expressions for the DQC signal for a system with N-dipolar coupled spin-1/2 particles in the solid state. The expressions are integrated over the relevant spatial parameters to obtain closed form DQC signal expressions. These expressions contain the concentration-dependent "instantaneous diffusion" and the background signal. For micromolar and lower concentrations, these effects are negligible. An approximate analysis is provided for cases of finite pulses. The expressions obtained in this work should improve the analysis of DQC experimental data significantly, and the analytical approach could be extended easily to a wide range of magnetic resonance phenomena.

Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR.

Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.

Analysis of Small-Molecule Mixtures by Super-Resolved 1H NMR Spectroscopy.

Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology, and many other areas of interest. Current barriers preclude from identifying the constituent molecules in a mixture as overlapping clusters of NMR lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. We demonstrate the efficacy of the method on the model mixtures of saccharides and amino acids in the presence of significant noise.