Temperature as an Extra Dimension in Multidimensional Protein NMR Spectroscopy

Supporting the assignment of NMR spectra with variable-temperature experiments

Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful tools in analytical chemistry. An important step in the analysis of NMR data is the assignment of resonance frequencies to the corresponding atoms in the molecule being investigated. The traditional approach considers the spectrum's characteristic parameters: chemical shift values, internuclear couplings, and peak intensities. In this paper, we show how to support the process of assigning a series of spectra of similar organic compounds by using temperature coefficients, that is, the rates of change in chemical shift values associated with given changes in temperature.

CAT on MOUSE: Control and automation of temperature for single-sided NMR instruments such as NMR-MOUSE

Temperature-dependent experiments are a rapidly growing area of interest for low-field NMR. In this work, we present a new device for wide-range temperature control for single-sided NMR instruments. The presented device, called CAT, is simple to build, inexpensive, and easy to modify to accommodate different samples. We present the capabilities of the device using a freezing temperature study of acetic acid/water mixtures. Additionally, we present the stability of the device over long measurement times. We believe that by introducing such a device with an open-source design, we allow researchers to use it in a wide range of applications and to fully incorporate variable-temperature studies in the world of single-sided instruments.

Optimizing measurements of linear changes of NMR signal parameters

Serial NMR experiments are commonly applied in variable-temperature studies, reaction monitoring, and other tasks. The resonance frequencies often shift linearly over the series, and the shift rates help to characterize the studied system. They can be determined using a classical fitting of peak positions or a more advanced method of Radon transform. However, the optimal procedure for data collection remains to be determined. In this paper, we discuss how to invest experimental time, i.e., whether to measure more scans at the expense of the number of spectra or vice versa. The results indicate that classical fitting provides slightly less error than the Radon transform, although the latter can be the method of choice for a low signal-to-noise ratio. We demonstrate this fact through theoretical consideration, simulations, and an experiment. Finally, we extend our considerations to the linear fitting of peak amplitudes. Interestingly, the optimal setup for measuring peak height changes differs from the one for resonance frequency changes - fewer spectra with more scans provide better results.

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals

Two-dimensional electron spin resonance (2D ESR) spectroscopy is a unique experimental technique for probing protein structure and dynamics, including processes that occur at the microsecond time scale. While it provides significant resolution enhancement over the one-dimensional experimental setup, spectral broadening and noise make extraction of spectral information highly challenging. Traditionally, two-dimensional Fourier transform (2D FT) is applied for the analysis of 2D ESR signals, although its efficiency is limited to stationary signals. In addition, it often fails to resolve overlapping peaks in 2D ESR. In this work, we propose a time-frequency analysis of 2D time-domain signals, which identifies all frequency peaks by decoupling a signal into its distinct constituent components via projection on the time-frequency plane. The method utilizes 2D undecimated discrete wavelet transform (2D UDWT) as an intermediate step in the analysis, followed by signal reconstruction and 2D FT. We have applied the method to a simulated 2D double quantum coherence (DQC) signal for validation and a set of experimental 2D ESR signals, demonstrating its efficiency in resolving overlapping peaks in the frequency domain, while displaying frequency evolution with time in case of non-stationary data.

Non-uniform sampling of similar NMR spectra and its application to studies of the interaction between alpha-synuclein and liposomes

The accelerated acquisition of multidimensional NMR spectra using sparse non-uniform sampling (NUS) has been widely adopted in recent years. The key concept in NUS is that a major part of the data is omitted during measurement, and then reconstructed using, for example, compressed sensing (CS) methods. CS requires spectra to be compressible, that is, they should contain relatively few "significant" points. The more compressible the spectrum, the fewer experimental NUS points needed in order for it to be accurately reconstructed. In this paper we show that the CS processing of similar spectra can be enhanced by reconstructing only the differences between them. Accurate reconstruction can be obtained at lower sampling levels as the difference is sparser than the spectrum itself. In many situations this method is superior to "conventional" compressed sensing. We exemplify the concept of "difference CS" with one such case-the study of alpha-synuclein binding to liposomes and its dependence on temperature. To obtain information on temperature-dependent transitions between different states, we need to acquire several dozen spectra at various temperatures, with and without the presence of liposomes. Our detailed investigation reveals that changes in the binding modes of the alpha-synuclein ensemble are not only temperature-dependent but also show non-linear behavior in their transitions. Our proposed CS processing approach dramatically reduces the number of NUS points required and thus significantly shortens the experimental time.

Sensitivity considerations on denoising series of spectra by singular value decomposition

When acquiring series of spectra ( T 1 , T 2 , CP buildup curves, etc.) on samples with poor SNR, we are usually faced with choosing between taking a few points with a large number of scans to maximize the SNR or more points with a smaller number of scans to maximize the information content. In this Letter, we show how low-rank decomposition can be used to denoise a series of spectra, reducing the trade-off between the number of scans and the number of experiments.

Variable-temperature NMR spectroscopy for metabolite identification in biological materials

Nuclear magnetic resonance is a "workhorse technique" used in metabolomics, complementary to mass spectrometry. Unfortunately, only the most basic NMR methods are sensitive enough to allow fast medical screening. The most common of them, a simple 1H NMR, suffers from low dispersion of resonance frequencies, which often hampers the identification of metabolites. In this article we show that 1H NMR spectra contain previously overlooked parameters potentially helpful in metabolite identification, namely the rates of temperature-induced changes of chemical shifts. We prove that they are reproducible between various metabolite mixtures and can be determined quickly when Radon transform is used to process the data.