Development of a Simple Cost Effective Oxygenation System for In Vivo Solution State NMR in 10 mm NMR Tubes.

In vivo NMR is evolving into an important tool to understand biological processes and environmental responses. Current approaches use flow systems to sustain the organisms with oxygenated water and food (e.g., algae) inside the NMR. However, such systems have the potential to leak and clog (potentially damaging costly hardware), require large volumes of media, and multiple expensive HPLC pumps. The proposed "oxygenation system", uses a simple "double slit" adapter and a single air/oxygen flow line into the NMR. The design is especially suited to larger diameter probes given that standard flow systems would require higher flow rates thus amplifying the potential and impact of leaks/clogs. Traditionally, in vivo NMR of small organisms (e.g., Daphnia) have required 2D NMR in combination with 13C enrichment to overcome susceptibility distortions and provide information rich metabolic profiles. Here Daphnia magna, Eisenia fetida and Artemia franciscana are used to demonstrate the potential of the oxygenation system. Survivability tests and 1H time-resolved monitoring were first performed on D. magna, while E. fetida contained enough biomass to permit 1H-13C HSQC, 13C-1H HETCOR and 31P NMR without isotopic enrichment. Finally, STOCSY of 1D 13C NMR was used to follow the growth of A. franciscana (without 13C enrichment) for 48 h after birth, which helps visualize trends across a series of 1D in vivo data. In summary, application of the oxygenation system toward larger diameter probes allows the collection of NMR data without enrichment, offering a promising solution to better understand processes in vivo.

Molecular Melodies: Unraveling the Hidden Harmonies of NMR Spectroscopy.

This work explores the evolution of auditory analysis in NMR spectroscopy, tracing its journey from a supplementary tool to visual methods such as oscilloscopes, to a technique sidelined due to technological advancements. Despite its renaissance in the late 1990s with artistic and scientific applications, widespread adoption was hindered by the necessity for hardware modifications and reliance on specialized software. Addressing these barriers, this paper introduces a new feature in Mnova NMR software that facilitates the easy auditory interpretation of NMR signals. We discuss new applications of this tool, emphasizing its utility in aiding the identification of specific functional groups by auditory analysis of the spectrum's multiplets, such as distinguishing between aromatic, olefinic, or aliphatic protons, thereby enriching the interpretative capabilities of NMR data.

Efficiently driving protein-based fragment screening and lead discovery using two-dimensional NMR.

Fragment-based drug discovery (FBDD) and validation of small molecule binders using NMR spectroscopy is an established and widely used method in the early stages of drug discovery. Starting from a library of small compounds, ligand- or protein-observed NMR methods are employed to detect binders, typically weak, that become the starting points for structure-activity relationships (SAR) by NMR. Unlike the more frequently used ligand-observed 1D NMR techniques, protein-observed 2D 1H-15N or 1H-13C heteronuclear correlation (HSQC or HMQC) methods offer insights that include the mechanism of ligand engagement on the target and direct binding affinity measurements in addition to routine screening. We hereby present the development of a set of software tools within the MestReNova (Mnova) package for analyzing 2D NMR for FBDD and hit validation purposes. The package covers three main tasks: (1) unsupervised profiling of raw data to identify outlier data points to exclude in subsequent analyses; (2) batch processing of single-point spectra to identify and rank binders based on chemical shift perturbations or spectral peak intensity changes; and (3) batch processing of multiple titration series to derive binding affinities (KD) by tracing the changes in peak locations or measuring global spectral changes. Toward this end, we implemented and evaluated a set of algorithms for automated peak tracing, spectral binning, and variance analysis by PCA, and a new tool for spectral data intensity comparison using ECHOS. The accuracy and speed of the tools are demonstrated on 2D NMR binding data collected on ligands used in the development of potential inhibitors of the anti-apoptotic MCL-1 protein.

NMR signal processing, prediction, and structure verification with machine learning techniques.

Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power. These algorithms are successfully employed for classification, regression, clustering, or dimensionality reduction tasks of large data sets and have been intensively applied in different areas of NMR including metabonomics, clinical diagnosis, or relaxometry. In this article, we concentrate on the various applications of ML/DL in the areas of NMR signal processing and analysis of small molecules, including automatic structure verification and prediction of NMR observables in solution.

An algorithm for discovery and determination of exponentially decaying components in nuclear magnetic resonance relaxometry data.

In many branches of physics, the time evolution of various quantities measured in systems passing from excited to equilibrium states, while theoretically very complex, can be in practice well approximated by a sum of exponential decays. Multiexponential relaxometry data analysis is about determining the number of exponential components and their corresponding amplitudes and decay rates, starting from noisy recorded time series, under the assumption of the discreteness of the number of components present. A technique for decomposing a signal modelled as a sum of exponential decays into its components is introduced, consisting of a modified version of the algorithm minimum description length (MDL) + matrix pencil, originally proposed by Lin et al. for the analysis of nuclear magnetic resonance spectroscopy data. The procedure starts by denoising the discrete time-domain signal, and then a number of different decimations are applied, each being followed by an MDL + matrix pencil detection-estimation step, and finally, a postprocessing of the intermediate outcomes is done. The comprised model order estimator eliminates the need of providing prior estimates of the number of components present.

Applications of the Whittaker smoother in NMR spectroscopy.

The Whittaker smoother, a special case of penalized least square, is a multipurpose algorithm that has proven to be very useful in many scientific fields, including image processing, chromatography, and optical spectroscopy. It shares many similarities with the Savitzky-Golay algorithm, but it is significantly faster and easier to automate. Its use in nuclear magnetic resonance, however, is not widespread although several applications have recently been published. In this review, the mathematical background of the method and its main applications in nuclear magnetic resonance spectroscopy will be discussed.

Enabling Fast Pseudo-2D NMR Spectral Acquisition for Broadband Homonuclear Decoupling: The EXACT NMR Approach.

Pseudo-2D NMR spectroscopy provides a means of acquiring broadband homonuclear decoupled spectra useful for structural characterization of complex molecules. However, data points concatenated in the direct dimension in these experiments are acquired over incremented time periods-leading to long acquisition times with no sensitivity benefits due to the absence of signal averaging between scans. Herein, the concept of EXACT NMR spectroscopy ("burst" non-uniform sampling of data points) is explored in pseudo-2D experiments with results revealing little or no loss in spectral quality or signal intensity despite the acceleration of acquisition-up to 400 % in some cases.

1JCH NMR Profile: Identification of Key Structural Features and Functionalities by Visual Observation and Direct Measurement of One-Bond Proton-Carbon Coupling Constants.

A user-friendly NMR interface for the visual and accurate determination of experimental one-bond proton-carbon coupling constants (1JCH) in small molecules is presented. This intuitive 1JCH profile correlates directly to δ(1H), and 1JCH facilitates the rapid identification and assignment of 1H signals belonging to key structural elements and functional groups. Illustrative examples are provided for some target molecules, including terminal alkynes, strained rings, electronegative substituents, or lone-pair-bearing heteronuclei.

A robust, general automatic phase correction algorithm for high-resolution NMR data.

The trends towards rapid NMR data acquisition, automated NMR spectrum analysis, and data processing and analysis by more naïve users combine to place a higher burden on data processing software to automatically process these data. Downstream data analysis is compromised by poor processing, and the automated processing algorithms must therefore be robust and accurate. We describe a new algorithm for automatic phase correction of frequency-domain, high-resolution NMR spectra. We show this to be reliable for data derived from a wide variety of typical NMR usages. We therefore conclude that the method will have wide-spread applicability and a positive impact on automated spectral processing and analysis. Copyright © 2017 John Wiley & Sons, Ltd.

Improving the Performance of High-Precision qNMR Measurements by a Double Integration Procedure in Practical Cases.

Quantitative (1)H NMR (qNMR) is a widely applied technique for compound concentration and purity determinations. The NMR spectrum will display signals from all species in the sample, and this is generally a strength of the method. The key spectral determination is the full and accurate determination of one or more signal areas. Accurate peak integration can be an issue when unrelated peaks resonate in an important integral region. We describe a "hybrid" approach to signal integration that provides an accurate estimation of signal area, removing the component(s) that may arise from unrelated peaks. This is achieved by using the most accurate integration method for the region and removing unwanted contributions. The key to this performing well, and in almost all cases, is the use of areas from deconvolved peaks. We describe this process and show that it can be very successfully applied to cases where the highest precision is required and for more common cases of NMR-based quantitation.

EXtended ACquisition Time (EXACT) NMR-A Case for 'Burst' Non-Uniform Sampling.

A strong case exists for the introduction of burst non-uniform sampling (NUS) in the direct dimension of NMR spectroscopy experiments. The resulting gaps in the NMR free induction decay can reduce the power demands of long experiments (by switching off broadband decoupling for example) and/or be used to introduce additional pulses (to refocus homonuclear coupling, for example). The final EXtended ACquisition Time (EXACT) spectra are accessed by algorithmic reconstruction of the missing data points and can provide higher resolution in the direct dimension than is achievable with existing non-NUS methods.

Application of (19) F time-domain NMR to measure content in fluorine-containing drug products.

It is necessary to show that the active content in the dosage form of drugs is within a certain narrow range of the label claim. In case of fluorinated drugs, the active content can be measured by high field solid state NMR because the excipients lack fluorine. To make NMR reachable to any laboratory, simple to use, and at a low cost, measurement of (19) F nucleus using a 23 MHz (for (1) H) low field benchtop time-domain (TD) NMR was investigated. Three fluorinated drug products, cinacalcet, lansoprazole, and ciprofloxacin, were chosen for this study. The doses for these drug products range from 15 to 500 mg. The average drug content measured using (19) F TD-NMR compares well with the reported label claims for the three drugs tested. (19) F TD-NMR is a simple and non-destructive technique to measure drug content in tablets. In addition, the accessibility and simplicity of the technique makes it an excellent process analytical technology tool for development and manufacturing in the pharmaceutical industry. Copyright © 2015 John Wiley & Sons, Ltd.

NMR data visualization, processing, and analysis on mobile devices.

Touch-screen computers are emerging as a popular platform for many applications, including those in chemistry and analytical sciences. In this work, we present our implementation of a new NMR 'app' designed for hand-held and portable touch-controlled devices, such as smartphones and tablets. It features a flexible architecture formed by a powerful NMR processing and analysis kernel and an intuitive user interface that makes full use of the smart devices haptic capabilities. Routine 1D and 2D NMR spectra acquired in most NMR instruments can be processed in a fully unattended way. More advanced experiments such as non-uniform sampled NMR spectra are also supported through a very efficient parallelized Modified Iterative Soft Thresholding algorithm. Specific technical development features as well as the overall feasibility of using NMR software apps will also be discussed. All aspects considered the functionalities of the app allowing it to work as a stand-alone tool or as a 'companion' to more advanced desktop applications such as Mnova NMR.

Optimization and automation of quantitative NMR data extraction.

NMR is routinely used to quantitate chemical species. The necessary experimental procedures to acquire quantitative data are well-known, but relatively little attention has been applied to data processing and analysis. We describe here a robust expert system that can be used to automatically choose the best signals in a sample for overall concentration determination and determine analyte concentration using all accepted methods. The algorithm is based on the complete deconvolution of the spectrum which makes it tolerant of cases where signals are very close to one another and includes robust methods for the automatic classification of NMR resonances and molecule-to-spectrum multiplets assignments. With the functionality in place and optimized, it is then a relatively simple matter to apply the same workflow to data in a fully automatic way. The procedure is desirable for both its inherent performance and applicability to NMR data acquired for very large sample sets.

Direct deduction of chemical class from NMR spectra.

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. The method identified as suitable for classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found to be suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to spectral interpretation problems.

Application of multiplet structure deconvolution to extract scalar coupling constants from 1D nuclear magnetic resonance spectra.

Multiplet structure deconvolution provides a robust method to determine the values of the coupling constants in first-order 1D nuclear magnetic resonance (NMR) spectra. Functions simplifying the coupling structure for partners with spin larger than 1/2 and for doublets with unequal amplitudes were introduced. The chemical shifts of the coupling partners causing mild second-order effects can, in favourable cases, be calculated from the slopes measured in doublet structures. Illustrations demonstrate that deconvolution can straightforwardly analyse multiplet posing difficulties to humans and, in some cases, extract coupling constants from unresolved multiplets.

A KNIME Workflow for Automated Structure Verification.

Adequate characterization of chemical entities made for biological screening in the drug discovery context is critical. Incorrectly characterized structures lead to mistakes in the interpretation of structure-activity relationships and confuse an already multidimensional optimization problem. Mistakes in the later use of these compounds waste money and valuable resources in a discovery process already under cost pressure. Left unidentified, these errors lead to problems in project data packages during quality review. At worst, they put intellectual property and patent integrity at risk. We describe a KNIME workflow for the early and automated identification of these errors during registration of a new chemical entity into the corporate screening catalog. This Automated Structure Verification workflow provides early identification (within 24 hours) of missing or inconsistent analytical data and therefore reduces any mistakes that inevitably get made. Automated identification removes the burden of work from the chemist submitting the compound into the registration system. No additional work is required unless a problem is identified and the submitter alerted. Before implementation, 14% of samples within the existing sample catalog were missing data on initial pass. A year after implementation, only 0.2% were missing data.

Automatic phase correction of 2D NMR spectra by a whitening method.

A new method for the automatic phase correction of multidimensional NMR spectra is described. It is based on the whitening concept formulated as the 'maximization of the number of white pixels into a bitmap that corresponds to the spectrum'. This process of maximization can be factorized along the individual axes of the spectrum and this property makes the method robust and fast. It employs a statistic measure based on a large number of spectral data points and, for this reason, is very tolerant to low signal-to-noise ratio (SNR) and local artifacts. The algorithm can efficiently phase either homonuclear or heteronuclear experiments and, unlike other previous methods, it can also process automatically spectra containing positive or negative peaks so that it is not necessary to deal with individual or special cases.

Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data.

There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse. One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed. In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines. Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort. Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository. Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.

LocMAP: A new localization method for the parametric processing of high resolution NMR data.

High resolution NMR spectroscopy offers a large number of data points that enable close peaks to be resolved. Data processing algorithms, however, have not yet been able to capitalize on this offering to achieve the highest permissible resolution. Although singular value decomposition (SVD) based methods such as matrix pencil (MPM) are theoretically able to achieve this, their onerous computational cost makes them impractical. In this work, we address this problem and propose a localized MPM method that we refer to as LocMaP, which is capable of delivering the promised high resolution while at the same time taking advantage of the computational efficiency of the FFT. We present the derivation of LocMaP and offer an efficient implementation of it. Evaluation using both Monte Carlo runs and a simulated FID establish the great potential of the proposed method.