Structure Elucidation Of Small Molecules Research Articles

Clean PDI-1 SQ: Suppression of HSQC artifacts in 2D proton-detected INADEQUATE spectra by pulse sequence redesign

Proton-detected INADEQUATE NMR experiments are widely used for structure elucidation of small molecules, in particular the implementations that display 13C single-quantum rather than double-quantum frequencies in the indirect dimension of 2D spectra. But unfortunately, such spectra in addition to the desired 1H–13C two-bond correlations also contain HSQC artifacts of comparable magnitude. The redesigned versatile experiment presented in this paper requires no compromise based on different 13C multiplicities and suppresses the HSQC artifacts that are a source of possible spectral misinterpretation. Demonstration of the new method is shown by applications to typical small molecules of different complexity.

Cutting without a Knife: A Slice-Selective 2D 1H-13C HSQC NMR Sequence for the Analysis of Inhomogeneous Samples.

Nuclear magnetic resonance (NMR) is a powerful technique with applications ranging from small molecule structure elucidation to metabolomics studies of living organisms. Typically, solution-state NMR requires a homogeneous liquid, and the whole sample is analyzed as a single entity. While adequate for homogeneous samples, such an approach is limited if the composition varies as would be the case in samples that are naturally heterogeneous or layered. In complex samples such as living organisms, magnetic susceptibility distortions lead to broad 1H line shapes, and thus, the additional spectral dispersion afforded by 2D heteronuclear experiments is often required for metabolite discrimination. Here, a novel, slice-selective 2D, 1H-13C heteronuclear single quantum coherence (HSQC) sequence was developed that exclusively employs shaped pulses such that only spins in the desired volume are perturbed. In turn, this permits multiple volumes in the tube to be studied during a single relaxation delay, increasing sensitivity and throughput. The approach is first demonstrated on standards and then used to isolate specific sample/sensor elements from a microcoil array and finally study slices within a living earthworm, allowing metabolite changes to be discerned with feeding. Overall, slice-selective NMR is demonstrated to have significant potential for the study of layered and other inhomogeneous samples of varying complexity. In particular, its ability to select subelements is an important step toward developing microcoil receive-only arrays to study environmental toxicity in tiny eggs, cells, and neonates, whereas localization in larger living species could help better correlate toxin-induced biochemical responses to the physical localities or organs involved.

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

Two-Dimensional Liquid Chromatography–Mass Spectrometry: Heart-Cutting Hydrogen/Deuterium Exchange as a Tool for Small Molecule Structure Elucidation

Two-Dimensional Liquid Chromatography–Mass Spectrometry: Heart-Cutting Hydrogen/Deuterium Exchange as a Tool for Small Molecule Structure Elucidation

MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing.

Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.

On the role of ”mental leaps” in small-molecule structure elucidation by NMR spectroscopy

On the role of ”mental leaps” in small-molecule structure elucidation by NMR spectroscopy

Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques.

Mass spectrometry (MS) has become the central technique that is extensively used for the analysis of molecular structures of unknown compounds in the gas phase. It manipulates the molecules by converting them into ions using various ionization sources. With high-resolution MS, accurate molecular weights (MW) of the intact molecular ions can be measured so that they can be assigned a molecular formula with high confidence. Furthermore, the application of tandem MS has enabled detailed structural characterization by breaking the intact molecular ions and protonated or deprotonated molecules into key fragment ions. This approach is not only used for the structural elucidation of small molecules (MW < 2000 Da), but also crucial biopolymers such as proteins and polypeptides; therefore, MS has been extensively used in multiomics studies for revealing the structures and functions of important biomolecules and their interactions with each other. The high sensitivity of MS has enabled the analysis of low-level analytes in complex matrices. It is also a versatile technique that can be coupled with separation techniques, including chromatography and ion mobility, and many other analytical instruments such as NMR. In this review, we aim to focus on the technical advances of MS-based structural elucidation methods over the past five years, and provide an overview of their applications in complex mixture analysis. We hope this review can be of interest for a wide range of audiences who may not have extensive experience in MS-based techniques.

MSNovelist: de novo structure generation from mass spectra

Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS2) spectra. In an evaluation with 3,863 MS2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.

Ultraviolet photodissociation facilitates mass spectrometry-based structure elucidation with pyrrolidine and piperidine containing compounds

In pharmaceutical development, structural elucidation of small molecules from process related impurities and degradation products is an essential component. As one of the most important methods in the toolbox, high resolution mass spectrometry (HRMS) and specifically tandem mass spectrometry (MS/MS) often provide fast and informative structural insights. However, many small molecule drugs containing certain biological relevant pharmacophores result in limited numbers of fragments when using traditional collision based fragmentation techniques, such as higher energy collisional dissociation (HCD), due to its inherent preference of cleaving the weakest bond first. As an alternative, ultraviolet photodissociation (UVPD), which irradiates the precursor ion with high energy photons, can lead to more extensive fragmentation from the readily UV absorbing small molecules. Here, we showcase the advantage of UVPD over HCD on pyrrolidine and piperidine containing molecules derivatized from a model compound, telmisartan. While HCD only yielded a single, highly abundant ion resulting from the pyrrolidine and pipieridine ring cleavage, UVPD generated rich and structurally informative fragment ions. UVPD is an attractive and powerful alternative for traditional fragmentation techniques for small molecule structural elucidation.

Pitfalls in the structural elucidation of small molecules. A critical analysis of a decade of structural misassignments of marine natural products.

Covering: July 2010 to August 2021This article summarizes more than 200 cases of misassigned marine natural products reported between July 2010 and August 2021, sorting out errors according to the structural elements. Based on a comparative analysis of the original and the revised structures, major pitfalls still plaguing the structural elucidation of small molecules were identified, emphasizing the role of total synthesis, crystallography, as well as chemical- and biosynthetic logic to complement spectroscopic data. Distinct "trends" in natural product misassignment are evident between compounds of marine and plant origin, with an overall much lower incidence of "impossible" structures within misassigned marine natural products.

Recent Developments Toward Integrated Metabolomics Technologies (UHPLC-MS-SPE-NMR and MicroED) for Higher-Throughput Confident Metabolite Identifications.

Metabolomics has emerged as a powerful discipline to study complex biological systems from a small molecule perspective. The success of metabolomics hinges upon reliable annotations of spectral features obtained from MS and/or NMR. In spite of tremendous progress with regards to analytical instrumentation and computational tools, < 20% of spectral features are confidently identified in most untargeted metabolomics experiments. This article explores the integration of multiple analytical instruments such as UHPLC-MS/MS-SPE-NMR and the cryo-EM method MicroED to achieve large-scale and confident metabolite identifications in a higher-throughput manner. UHPLC-MS/MS-SPE allows for the simultaneous automated purification of metabolites followed by offline structure elucidation and structure validation by NMR and MicroED. Large-scale study of complex metabolomes such as that of the model plant legume Medicago truncatula can be achieved using an integrated UHPLC-MS/MS-SPE-NMR metabolomics platform. Additionally, recent developments in MicroED to study structures of small organic molecules have enabled faster, easier and precise structure determinations of metabolites. A MicroED small molecule structure elucidation workflow (e.g., crystal screening, sample preparation, data collection and data processing/structure determination) has been described. Ongoing MicroED methods development and its future scope related to structure elucidation of specialized metabolites and metabolomics are highlighted. The incorporation of MicroED with a UHPLC-MS/MS-SPE-NMR instrumental ensemble offers the potential to accelerate and achieve higher rates of metabolite identification.

Crystalline sponge affinity screening: A fast tool for soaking condition optimization without the need of X-ray diffraction analysis

Structural elucidation of small molecules only available in low quantity (nanogram) is one of the big advantages of the crystalline sponge method. The optimization of various soaking parameters is crucial for effective analyte absorption and repetitive positioning in the pores of the crystal. Time-consuming X-ray diffraction measurements are necessary for data collection and confirmation of successful guest inclusion. In this work, we report a screening method to select optimal soaking conditions without the need of single-crystal X-ray diffraction analysis for individual compounds and mixtures. 14 substances were chosen as test compounds. Parallel guest soaking of individual compounds and mixtures was conducted using various soaking conditions. After evaporation of solvent, excessive material was removed, and guest molecules released through dissolution of the framework. Liquid chromatography-tandem mass spectrometry allowed the estimation of analyte trapped in the pores and the selection of optimal soaking condition dependent on the highest amount of analyte to crystal size (affinity factor). The tool allowed subsequent crystallographic analysis of ten compounds with minimal experiment time. Additionally, a study to examine the lower limit of detection of the crystalline sponge method was conducted. Determination of two target analytes was possible using only 5 ng of sample. Our study shows the potential of an affinity screening to prioritize soaking parameters by estimation of the guest concentration in a single crystal for one or multiple target compounds within a short period of time.

Gas-Phase Adsorption of N&lt;sub&gt;2&lt;/sub&gt; on Protonated Molecules and Its Application to the Structural Elucidation of Small Molecules

The gas-phase adsorption of N2 on protonated serine (Ser, C3H7NO3), threonine (Thr, C4H9NO3), glycine (Gly, C2H5NO2), and 2-aminoethanol (C2H7NO) was investigated using a tandem mass spectrometer equipped with an electrospray ionization source and a cold ion trap. N2 molecules were adsorbed on the free X–H (X=O and N) groups of protonated molecules. Gas-phase N2 adsorption-mass spectrometry detected the presence of free X–H groups in the molecular structures, and was applied to the structural elucidation of small molecules. When the 93 structures with an elemental composition of C3H7NO3 were filtered using the gas-phase N2 adsorption-mass spectrometry results for Ser, the number of possible molecular structures was reduced to 8 via the quantification of the X–H groups. Restricting and minimizing the number of possible candidates were effective steps in the structural elucidation process. Gas-phase N2 adsorption-mass spectrometry combined with mass spectrometry-based techniques has the potential for being useful for elucidating the molecular structures of a variety of molecules.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation.

Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation. Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search. In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

NMR spectroscopy: the swiss army knife of drug discovery.

Nuclear magnetic resonance (NMR) spectroscopy has evolved into a powerful tool within drug discovery over the last two decades. While traditionally being used by medicinal chemists for small molecule structure elucidation, it can also be a valuable tool for the identification of small molecules that bind to drug targets, for the characterization of target-ligand interactions and for hit-to-lead optimization. Here, we describe how NMR spectroscopy is integrated into the Pfizer drug discovery pipeline and how we utilize this approach to identify and validate initial hits and generate leads.

Computational methods for small molecule identification

Abstract Identification of small molecules remains a central question in analytical chemistry, in particular for natural product research, metabolomics, environmental research, and biomarker discovery. Mass spectrometry is the predominant technique for high-throughput analysis of small molecules. But it reveals only information about the mass of molecules and, by using tandem mass spectrometry, about the mass of molecular fragments. Automated interpretation of mass spectra is often limited to searching in spectral libraries, such that we can only dereplicate molecules for which we have already recorded reference mass spectra. In my thesis “Computational methods for small molecule identification” we developed SIRIUS, a tool for the structural elucidation of small molecules with tandem mass spectrometry. The method first computes a hypothetical fragmentation tree using combinatorial optimization. By using a Bayesian statistical model, we can learn parameters and hyperparameters of the underlying scoring directly from data. We demonstrate that the statistical model, which was fitted on a small dataset, generalizes well across many different datasets and mass spectrometry instruments. In a second step the fragmentation tree is used to predict a molecular fingerprint using kernel support vector machines. The predicted fingerprint can be searched in a structure database to identify the molecular structure. We demonstrate that our machine learning model outperforms all other methods for this task, including its predecessor FingerID. SIRIUS is available as commandline tool and as user interface. The molecular fingerprint prediction is implemented as web service and receives over one million requests per month.

Experiments with direct detection of multiple FIDs.

Pulse schemes with direct observation of multiple free induction decays (FIDs) offer a dramatic increase in the spectral information content of NMR experiments and often yield substantial improvement in measurement sensitivity per unit time. Availability of multiple receivers on the state-of-the-art commercial spectrometers allows spectra from different nuclear species to be recorded in parallel routinely. Experiments with multi-FID detection have been designed with both, homonuclear and multinuclear acquisition. We provide a brief overview of such techniques designed for applications in liquid- and solid- state NMR as well as in hyperpolarized samples. Here we show how these techniques have led to design of experiments that allow structure elucidation of small molecules and resonance assignment in proteins from a single measurement. Probes with multiple RF micro-coils routed to multiple NMR receivers provide an alternative way of increasing the throughput of modern NMR systems. Solid-state NMR experiments have also benefited immensely from both parallel and simultaneous FID acquisition in a variety of multi-dimensional pulse schemes. We believe that multi-FID detection will become an essential component of the future NMR methodologies effectively increasing the information content of NMR experiments and reducing the cost of NMR analysis.

Unraveling the unknown areas of the human metabolome: the role of infrared ion spectroscopy

The identification of molecular biomarkers is critical for diagnosing and treating patients and for establishing a fundamental understanding of the pathophysiology and underlying biochemistry of inborn errors of metabolism. Currently, liquid chromatography/high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy are the principle methods used for biomarker research and for structural elucidation of small molecules in patient body fluids. While both are powerful techniques, several limitations exist that often make the identification of unknown compounds challenging. Here, we describe how infrared ion spectroscopy has the potential to be a valuable orthogonal technique that provides highly-specific molecular structure information while maintaining ultra-high sensitivity. Here, we characterize and distinguish two well-known biomarkers of inborn errors of metabolism, glutaric acid for glutaric aciduria and ethylmalonic acid for short-chain acyl-CoA dehydrogenase deficiency, using infrared ion spectroscopy. In contrast to tandem mass spectra, in which ion fragments can hardly be predicted, we show that the prediction of an IR spectrum allows reference-free identification in the case that standard compounds are either commercially or synthetically unavailable. Finally, we illustrate how functional group information can be obtained from an IR spectrum for an unknown and how this is valuable information to, for example, narrow down a list of candidate structures resulting from a database query. Early diagnosis in inborn errors of metabolism is crucial for enabling treatment and depends on the identification of biomarkers specific for the disorder. Infrared ion spectroscopy has the potential to play a pivotal role in the identification of challenging biomarkers.

Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS.

In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI- mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI-, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods. Graphical abstract Consensus workflow for combining fragmentation and retention prediction in LC-HRMS-based micropollutant identification.

Enhanced measurement of residual chemical shift anisotropy for small molecule structure elucidation.

A method is introduced to measure residual chemical shift anisotropies conveniently and accurately in the mesophase of poly-γ-(benzyl-L-glutamate). The alignment amplitude is substantially enhanced over common methods which greatly benefits measurements particularly on sp3 carbons. The approach offers significant improvements in data accuracy and utility for small molecule structure determination.