Proton fingerprints portray molecular structures: enhanced description of the 1H NMR spectra of small molecules

Structural Fingerprinting of Antisense Oligonucleotide Therapeutics by Solution NMR Spectroscopy

PURPOSE

Antisense oligonucleotide (ASO) therapeutics are an emerging class of biopharmaceuticals to treat and prevent diseases, particularly those involving "undruggable" protein targets. Impurities generated throughout the ASO drug manufacturing and formulation pipeline can be detrimental to drug safety and efficacy. Therefore, analytical techniques are needed to rigorously characterize these molecules for quality assurance purposes.

METHODS

We demonstrate 1D and 2D nuclear magnetic resonance (NMR) spectroscopy methods that can generate high-resolution structural "fingerprints" of ASOs.

RESULTS AND CONCLUSIONS

1D 1H and 31P measurements are shown to provide rapid initial assessment of the ASO integrity. In particular, a well-resolved pair of 31P signals arising from the 5´-end of the phosphorodiamidate morpholino oligomer (PMO) are sensitive to complex formation and oligomerization state. 2D 1H-1H, 1H-13C, and 1H-15 N experiments, although less sensitive, are further shown to enable resonance assignment, which will allow the tracking of structural changes at high-resolution during the drug development and manufacturing processes. We further anticipate that the described NMR approaches will be broadly applicable to fully formulated ASO therapeutics, including modalities other than PMOs.

Problems, principles and progress in computational annotation of NMR metabolomics data

BACKGROUND

Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for ¹H 1-dimensional (¹H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards.

AIM OF REVIEW

This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

Quantum-Mechanical Driven 1H Iterative Full Spin Analysis Addresses Complex Peak Patterns of Choline Sulfate

Choline and choline esters are essential nutrients in biological systems for carrying out normal functions, such as the modulation of neurotransmission and the formation and maintenance of cell membranes. Choline sulfate is reportedly involved in the defense mechanism of accumulating sulfur resources against sulfur deficiency. Contrary to expectations, a full assignment of the ¹H NMR spectrum of choline sulfate has not been reported. The present study pioneered a full assignment by quantum-mechanical driven ¹H iterative full spin analysis. The complex peak patterns were analyzed in terms of heteronuclear and non-first-order coupling. The ¹H-¹⁴N coupling constants, including two-bond coupling, which can be neglected, were accurately determined by iterative optimization. Non-first-order splitting has been described to be due to the presence of magnetically non-equivalent geminal protons. Moreover, in the comparison of the methylene proton resonance patterns of choline sulfate with choline and choline phosphate, the differences in the geminal and vicinal coupling constants were further examined through spectral simulation excluding the heteronuclear coupling. The precise spectral interpretation provided in this study is expected to contribute to future ¹H NMR-based qualitative or quantitative studies of choline sulfate-containing sources.

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Combining NMR and MS to Describe Pyrrole-2-Carbaldehydes in Wheat Bran of Radiation

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are indispensable analytical tools to provide chemical fingerprints in metabolomics studies. The present study evaluated radiation breeding wheat lines for chemical changes by non-targeted NMR-based metabolomics analysis of bran extracts. Multivariate analysis following spectral binning suggested pyrrole-2-carbaldehydes as chemical markers of four mutant lines with distinct NMR fingerprints in a δ_H range of 9.28-9.40 ppm. Further NMR and MS data analysis, along with chromatographic fractionation and synthetic preparation, aimed at structure identification of marker metabolites and identified five pyrrole-2-carbaldehydes. Quantum-mechanical driven ¹H iterative full spin analysis (QM-HiFSA) on synthetic pyrrole-2-carbaldehydes provided a precise description of complex peak patterns. Biological evaluation of pyrrole-2-carbaldehydes was performed with nine synthetic products, and six compounds showed hepatoprotective effects via modulation of reactive oxygen species production. Given that three out of five identified in wheat bran of radiation were described for hepatoprotective activity, the value of radiation mutation to greatly enhance pyrrole-2-carbaldehyde production was supported.

Minimizing the risk of deducing wrong natural product structures from NMR data

There continues to be a disturbing number of natural products reported in the literature whose structures are incorrect. At least in part, this reflects the fact that many natural product chemists have limited formal nuclear magnetic resonance training. Gaps in training and lack of awareness regarding the challenges and ambiguities associated with two-dimensional nuclear magnetic resonance data interpretation can easily lead to errors in structure elucidation. The purpose of this tutorial is to point out some of these issues, highlight the kinds of errors that have been made and provide specific advice on how to avoid these missteps such that the risk of reporting a wrong structure is minimized.

Resolving Entangled JH-H-Coupling Patterns for Steroidal Structure Determinations by NMR Spectroscopy

For decades, high-resolution ¹H NMR spectroscopy has been routinely utilized to analyze both naturally occurring steroid hormones and synthetic steroids, which play important roles in regulating physiological functions in humans. Because the ¹H signals are inevitably superimposed and entangled with various J_H–H splitting patterns, such that the individual ¹H chemical shift and associated J_H–H coupling identities are hardly resolved. Given this, applications of thess information for elucidating steroidal molecular structures and steroid/ligand interactions at the atomic level were largely restricted. To overcome, we devoted to unraveling the entangled J_H–H splitting patterns of two similar steroidal compounds having fully unsaturated protons, i.e., androstanolone and epiandrosterone (denoted as 1 and 2, respectively), in which only hydroxyl and ketone substituents attached to C3 and C17 were interchanged. Here we demonstrated that the J_H–H values deduced from 1 and 2 are universal and applicable to other steroids, such as testosterone, 3β, 21-dihydroxygregna-5-en-20-one, prednisolone, and estradiol. On the other hand, the ¹H chemical shifts may deviate substantially from sample to sample. In this communication, we propose a simple but novel scheme for resolving the complicate J_H–H splitting patterns and ¹H chemical shifts, aiming for steroidal structure determinations.

Targeting Trimeric and Tetrameric Proanthocyanidins of Cinnamomum verum Bark as Bioactives for Dental Therapies

The present study elucidated the structures of three A-type tri- and tetrameric proanthocyanidins (PACs) isolated from Cinnamomum verum bark to the level of absolute configuration and determined their dental bioactivity using two therapeutically relevant bioassays. After selecting a PAC oligomer fraction via a biologically diverse bioassay-guided process, in tandem with centrifugal partition chromatography, phytochemical studies led to the isolation of PAC oligomers that represent the main bioactive principles of C. verum: two A-type tetrameric PACs, epicatechin-(2β→O→7,4β→8)-epicatechin-(4β→6)-epicatechin-(2β→O→7,4β→8)-catechin (1) and parameritannin A1 (2), together with a trimer, cinnamtannin B1 (3). Structure determination of the underivatized proanthocyanidins utilized a combination of HRESIMS, ECD, 1D/2D NMR, and 1H iterative full spin analysis data and led to NMR-based evidence for the deduction of absolute configuration in constituent catechin and epicatechin monomeric units.

Quantum mechanical NMR full spin analysis in pharmaceutical identity testing and quality control

Issues related to pharmaceutical quality are arising at an alarming rate. Pharmaceutical quality concerns both the Active Pharmaceutical Ingredients (APIs) and the Finished Drug Product/ Formulation. Recently, there has been a significant increase in the number of reports of harmful impurities in marketed drug formulations. Impurities range from solvents, reactants, adulterants, and catalysts to synthetic byproducts. Quality concerns in commercial preparations may also arise due to shelf life stability. Furthermore, a number of falsified and substandard drug cases have been reported. Most of the techniques which are currently in place can, at best, detect the impurities, but cannot identify them unless they are already known and can be compared to a standard. On the other hand, ¹H NMR spectroscopy detects all the hydrogen containing species, typically provides information to elucidate structures partially or even completely, and through its absolute quantitative capabilities even can detect the presence hydrogen-free species indirectly. The structural properties that produce ¹H NMR signals as characteristic representations of a given molecule are the chemical shifts (δ in ppm) and coupling constants (J in Hz). Along with the line widths (ω_1/2 in Hz), these parameters are bound to both the molecule and the NMR experimental conditions by quantum mechanical (QM) principles. This means that the ¹H NMR spectra of APIs can be precisely calculated and compared to the experimental data. This review explains how ¹H NMR spectroscopy coupled with Full Spin Analysis can contribute towards the quality control of pharmaceuticals by improving structural dereplication and achieving simultaneous quantification of both APIs and their contaminants.

N-Acetyldopamine derivatives from Periostracum Cicadae and their regulatory activities on Th1 and Th17 cell differentiation

Bioassay-guided fractionation of a 90% ethanol extract of Periostracum Cicadae led to the isolation of two new N-acetyldopamine dimers (1a/1b) along with six known dimers (2a/2b, 3a/3b, and 4a/4b) and two monomers (5a/5b); compounds 2a/2b, 4a/4b and 5a/5b were newly isolated from this material. All compounds were isolated as enantiomeric mixtures and each enantiomer was successfully separated by chiral-phase HPLC. The structures including absolute configurations were confirmed by high-resolution electrospray ionization mass spectrometry (HR-ESIMS), 1D/2D nuclear magnetic resonance (NMR) spectroscopy, ¹H iterative Full Spin Analysis (HiFSA), and electronic circular dichroism (ECD) spectroscopy. Subsequently, the bioactivities of these isolates were evaluated via CD4⁺ T cell differentiations, which are critical for immune responses and inflammation. The results revealed that compound 5b was observed to enhance the IFN-γ⁺ Th1 differentiation, which may have a potential for cancer immunotherapy.

Quantum approximate Bayesian computation for NMR model inference

Recent technological advances may lead to the development of small scale quantum computers capable of solving problems that cannot be tackled with classical computers. A limited number of algorithms has been proposed and their relevance to real world problems is a subject of active investigation. Analysis of many-body quantum system is particularly challenging for classical computers due to the exponential scaling of Hilbert space dimension with the number of particles. Hence, solving problems relevant to chemistry and condensed matter physics are expected to be the first successful applications of quantum computers. In this paper, we propose another class of problems from the quantum realm that can be solved efficiently on quantum computers: model inference for nuclear magnetic resonance (NMR) spectroscopy, which is important for biological and medical research. Our results are based on three interconnected studies. Firstly, we use methods from classical machine learning to analyze a dataset of NMR spectra of small molecules. We perform a stochastic neighborhood embedding and identify clusters of spectra, and demonstrate that these clusters are correlated with the covalent structure of the molecules. Secondly, we propose a simple and efficient method, aided by a quantum simulator, to extract the NMR spectrum of any hypothetical molecule described by a parametric Heisenberg model. Thirdly, we propose a simple variational Bayesian inference procedure for extracting Hamiltonian parameters of experimentally relevant NMR spectra.

NMR reveals an undeclared constituent in custom synthetic peptides

Whereas generic, LC-based pharmaceutical control quality procedures depend largely on the detection mode and can be particularly 'blind' to certain impurities, NMR is a more versatile and, thus, often more judicious detector. While adulteration presents ever-evolving challenges for the analysis of active pharmaceutical ingredients (APIs) and finished products sold in the worldwide (online) marketplace, research chemicals are usually trusted rather than being considered flawed or even adulterated. This report shows how NMR analysis uncovered the unanticipated presence of substantial amounts of mannitol (20 and 43% w/w) as undeclared constituent in two custom synthetic peptides, DR and DRVYI, that were sourced commercially. Quantitative 1H NMR (qHNMR) readily detected the contaminant, even on a 60 MHz benchtop instrument, and quantified the highly polar and UV-transparent adulterant. Quantum-mechanical 1H iterative Full Spin Analysis (HiFSA) not only achieved unambiguous identification of both the mannitol and the peptides, but also confirmed the quantitative results. The cases show that experimental verification supersedes trust in both pharmaceutical and research QC. They also highlight the promising utility of both established high-field and recently re-evolving low-field benchtop qHNMR. The unanticipated findings remind manufacturers and researchers alike about the advantages of including/performing NMR and qNMR with routine CofA documentation and/or verification of research grade chemicals. Especially when done jointly, this can greatly improve confidence in research and help streamline the pharmaceutical QC toolbox.

Quality Control of Therapeutic Peptides by 1H NMR HiFSA Sequencing

Ensuring identity, purity, and reproducibility are equally essential during synthetic chemistry, drug discovery, and for pharmaceutical product safety. Many peptidic APIs are large molecules that require considerable effort for integrity assurance. This study builds on quantum mechanical ¹H iterative Full Spin Analysis (HiFSA) to establish NMR peptide sequencing methodology that overcomes the intrinsic limitations of principal compendial methods in identifying small structural changes or minor impurities that affect effectiveness and safety. HiFSA sequencing yields definitive identity and purity information concurrently, allowing for API quality assurance and control (QA/QC). Achieving full peptide analysis via NMR building blocks, the process lends itself to both research and commercial applications as 1D ¹H NMR (HNMR) is the most sensitive and basic NMR experiment. The generated HiFSA profiles are independent of instrument or software tools and work at any magnetic field strength. Pairing with absolute or 100% qHNMR enables quantification of mixtures and/or determination of peptide conformer populations. Demonstration of the methodology uses single amino acids (AAs) and peptides of increasing size, including the octapeptide, angiotensin II, and the nonapeptide, oxytocin. The feasibility of HiFSA coupled with automated NMR and qHNMR for use in QC/QA efforts is established through case-based examples and recommended procedures.

The role of computer-assisted structure elucidation (CASE) programs in the structure elucidation of complex natural products

Computer-assisted structure elucidation can help to determine the structures of complex natural products while minimizing the risk of structure errors.

Tools for Enhanced NMR-Based Metabolomics Analysis

Metabolomics is the study of profiles of small molecules in biological fluids, cells, or organs. These profiles can be thought of as the "fingerprints" left behind from chemical processes occurring in biological systems. Because of its potential for groundbreaking applications in disease diagnostics, biomarker discovery, and systems biology, metabolomics has emerged as a rapidly growing area of research. Metabolomics investigations often, but not always, involve the identification and quantification of endogenous and exogenous metabolites in biological samples. Software tools and databases play a crucial role in advancing the rigor, robustness, reproducibility, and validation of these studies. Specifically, the establishment of a robust library of spectral signatures with unique compound descriptors and atom identities plays a key role in profiling studies based on data from nuclear magnetic resonance (NMR) spectroscopy. Here, we discuss developments leading to a rigorous basis for unique identification of compounds, reproducible numbering of atoms, the compact representation of NMR spectra of metabolites and small molecules, tools for improved compound identification, quantification and visualization, and approaches toward the goal of rigorous analysis of metabolomics data.

The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

Covering: up to 2018With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.

Complete 1H NMR assignment of cholesteryl benzoate

The 750 MHz ¹H NMR spectrum of cholesteryl benzoate (1b) could be assigned completely, which means all chemical shifts and all coupling constants, including some long-range values, were established. This task was possible by extracting many approximate coupling constant values in the overlapped spectrum region from an HSQC experiment, and using these values in the ¹H iterative full spin analysis integrated in the PERCH NMR software. The task was facilitated using our published data for 3β-acetoxypregna-5,16-dien-20-one (3), the assignment data of the sesquiterpene benzoquinone dihydroperezone (2), also performed in the present study, which contains the same carbon atoms chain than cholesterol (1a), and an HSQC study of (25R)-27-deuteriocholesterol (1c) we prepared some 40 years ago. The HSQC values of 1c in combination with the coupling constants of 1b also allowed to completely assigning the spectrum of 1c. The complete assignment of 1b and 1c further provided the opportunity to estimate the hydrogen shifts induced upon benzoylation of cholesterol. Comparison of the experimental vicinal coupling constants of 1b with the values calculated using the Altona software provides an excellent correlation. In addition, a single crystal X-ray diffraction study of 1b provided the molecular conformation in the solid state, which revealed the side chain adopts an extended conformation.

Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening

The exceptionally rich information content of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular interactions in a wide range of applications, including clinical biomarker discovery, drug discovery, environmental chemistry, and metabolomics. The set of peak positions and intensities from a reference NMR spectrum generally serves as the identifying signature for a compound. Reference spectra normally are collected under specific conditions of pH, temperature, and magnetic field strength, because changes in conditions can distort the identifying signatures of compounds. A spin system matrix that parametrizes chemical shifts and coupling constants among spins provides a much richer feature set for a compound than a spectral signature based on peak positions and intensities. Spin system matrices expand the applicability of NMR spectral libraries beyond the specific conditions under which data were collected. In addition to being able to simulate spectra at any field strength, spin parameters can be adjusted to systematically explore alterations in chemical shift patterns due to variations in other experimental conditions, such as compound concentration, pH, or temperature. We present methodology and software for efficient interactive optimization of spin parameters against experimental 1D-¹H NMR spectra of small molecules. We have used the software to generate spin system matrices for a set of key mammalian metabolites and are also using the software to parametrize spectra of small molecules used in NMR-based ligand screening. The software, along with optimized spin system matrix data for a growing number of compounds, is available from http://gissmo.nmrfam.wisc.edu/ .

Structural Sequencing of Oligopeptides Aided by 1H Iterative Full-Spin Analysis

This report describes an approach using ¹H NMR iterative full-spin analysis (HiFSA) to extract definitive structural information on unknown peptides from 1D ¹H NMR data. By comparing the experimental data and HiFSA fingerprint of a known analogue, it is possible to isolate the characteristic ¹H subspectrum of the different amino acids and, thus, elucidate the structure of the peptide. To illustrate this methodology, a comprehensive analysis of five new anti-Mycobacterium tuberculosis peptides (2-6), all analogues of ecumicin (1), was carried out. The method was validated by demonstrating congruence of the HiFSA-based structures with all available data, including MS and 2D NMR. The highly reproducible HiFSA fingerprints of the new ∼1600 amu peptides were generated in this process. Besides oligo-peptides, the HiFSA sequencing approach could be extended to all oligomeric compounds consisting of chains of monomers lacking H-H spin-spin coupling across the moieties. HiFSA sequencing is capable of differentiating complex oligomers that exhibit minor structural differences such as shifted hydoxyl or methyl groups. Because it employs the basic and most sensitive 1D ¹H NMR experiment, HiFSA sequencing enables the exploration of peptide analogues up to at least 2000 amu, even with basic contemporary spectrometers and when only sub-milligram amounts of isolates are available.

Stereochemistry of a Second Riolozane and Other Diterpenoids from Jatropha dioica

The dichloromethane extract of the roots of Jatropha dioica afforded riolozatrione (1) and a C-6 epimer of riolozatrione, 6-epi-riolozatrione (2), as a new structure and only the second reported riolozane diterpenoid. The two known diterpenoids jatrophatrione (3) and citlalitrione (4) were also isolated and characterized. Both epimers 1 and 2 are genuine plant constituents, with 2 likely being the biosynthesis precursor of 1 due to the tendency for the quantitative transformation of 2 into 1 under base catalysis. The structural characterization and distinction of the stereoisomers utilized ¹H iterative full-spin analysis, yielding complete J-correlation maps that were represented as quantum interaction and linkage tables. The absolute configuration of compounds 1-4 was established by means of vibrational circular dichroism and via X-ray diffraction analysis for 1, 2, and 4. Additionally, the cytotoxic and antiherpetic in vitro activities of the isolates were evaluated.

Complete 1 H NMR assignment of cedranolides

Complete and unambiguous ¹ H NMR chemical shift assignment of α-cedrene (2) and cedrol (9), as well as for α-pipitzol (1), isocedrol (10), and the six related compounds 3-8 has been established by iterative full spin analysis using the PERCH NMR software (PERCH Solutions Ltd., Kuopio, Finland). The total sets of coupling constants are described and correlated with the conformational equilibria of the five-membered ring of 1-10, which were calculated using the complete basis set method. Copyright © 2015 John Wiley & Sons, Ltd.

QM-HiFSA-Aided Structure Determination of Succinilenes A-D, New Triene Polyols from a Marine-Derived Streptomyces sp

Based on profiles of secondary metabolites produced by marine bacteria obtained using LC/MS, succinilenes A-D (1-4), new triene polyols, were discovered from a culture of a Streptomyces strain SAK1, which was collected in the southern area of Jeju Island, Republic of Korea. The gross structures of 1-4 were primarily determined through analysis of NMR spectra. The double bond geometries of the succinilenes, which could not be established from conventional ¹H NMR spectra because of the highly overlapped olefinic signals, were successfully deciphered using the recently developed quantum-mechanics-driven ¹H iterative full spin analysis (QM-HiFSA). Succinilenes A-C (1-3) displayed inhibitory effects against lipopolysaccharide (LPS)-induced nitric oxide (NO) production, indicating their anti-inflammatory significance. These three compounds (1-3) commonly bear a succinic acid moiety, although succinilene D (4), which did not inhibit NO production, does not have this moiety in its structure. The absolute configurations of succinilenes A-D (1-4) were established through J-based configuration analysis, the modified Mosher's method following methanolysis, and CD spectral analysis.

Absolute Configuration of Native Oligomeric Proanthocyanidins with Dentin Biomodification Potency

The structurally complex oligomeric proanthocyanidins (OPACs) are promising biomimetic agents, capable of strengthening the macromolecular backbone of teeth via intermolecular and intermicrofibrillar cross-linking. This study establishes analytical methods capable of determining the absolute configuration of the catechin-type monomeric units of underivatized OPACs. This preserves the capacity of their biological evaluation, aimed at understanding the inevitably stereospecific interactions between the OPACs and dentin collagen. Guided by dental bioassays (modulus of elasticity, long-term stability), two new trimeric and tetrameric A-type OPACs were discovered as dentin biomodifiers from pine (Pinus massoniana) bark: epicatechin-(2β→O→7,4β→8)-epicatechin-(2β→O→7,4β→8)-catechin (5) and epicatechin-(2β→O→7,4β→8)-epicatechin-(2β→O→7,4β→6)-epicatechin-(2β→O→7,4β→8)-catechin (6), respectively. Combining 1D/2D NMR, HRESIMS, ECD, 1H iterative full spin analysis (HiFSA), and gauge-invariant atomic orbital (GIAO) δ calculations, we demonstrate how 13C NMR chemical shifts (diastereomeric building blocks (A-type dimers)) empower the determination of the absolute configuration of monomeric units in the higher oligomers 5 and 6. Collectively, NMR with ECD reference data elevates the level of structural information achievable for these structurally demanding molecules when degradation analysis is to be avoided. Considering their numerous and deceptively subtle, but 3D impactful, structural variations, this advances the probing of OPAC chemical spaces for species that bind selectively to collagenous and potentially other biologically important biomacromolecules.

Complex mixtures by NMR and complex NMR for mixtures: experimental and publication challenges

Untargeted strategies have changed the rules of the game in complex mixture analysis, introducing an amazing potential for medical and biological applications that is just starting to be tapped. But with great power come great challenges; although untargeted mixture analysis opens the road for many exciting possibilities, the road is still full of perils. On the one hand, this article highlights some of the difficulties that need to be sorted for mixture analysis by NMR to fulfill its potential, along with insight on how they may be managed. Highlighted key points include the need for 'computer friendly' solutions for sharing data, experimental design and algorithm to facilitate the steady growth of knowledge and modeling ability in the field, and the need for large-scale studies to improve confidence in newly identified biomarkers. On the other hand, the second part of this article presents some breakthroughs in NMR experiments that, when combined, may modify the landscape of mixture analysis. Copyright © 2016 John Wiley & Sons, Ltd.

Human- and computer-accessible 2D correlation data for a more reliable structure determination of organic compounds. Future roles of researchers, software developers, spectrometer managers, journal editors, reviewers, publisher and database managers toward artificial-intelligence analysis of NMR spectra

The introduction of a universal data format to report the correlation data of 2D NMR spectra such as COSY, HSQC and HMBC spectra will have a large impact on the reliability of structure determination of small organic molecules. These lists of assigned cross peaks will bridge signals found in NMR 1D and 2D spectra and the assigned chemical structure. The record could be very compact, human and computer readable so that it can be included in the supplementary material of publications and easily transferred into databases of scientific literature and chemical compounds. The records will allow authors, reviewers and future users to test the consistency and, in favorable situations, the uniqueness of the assignment of the correlation data to the associated chemical structures. Ideally, the data format of the correlation data should include direct links to the NMR spectra to make it possible to validate their reliability and allow direct comparison of spectra. In order to take the full benefits of their potential, the correlation data and the NMR spectra should therefore follow any manuscript in the review process and be stored in open-access database after publication. Keeping all NMR spectra, correlation data and assigned structures together at all time will allow the future development of validation tools increasing the reliability of past and future NMR data. This will facilitate the development of artificial intelligence analysis of NMR spectra by providing a source of data than can be used efficiently because they have been validated or can be validated by future users. Copyright © 2016 John Wiley & Sons, Ltd.

Ligand-Controlled Synthesis of Azoles via Ir-Catalyzed Reactions of Sulfoxonium Ylides with 2-Amino Heterocycles

An iridium-catalyzed method was developed for the synthesis of imidazo-fused pyrrolopyrazines. The presence or absence of a nitrogenated ligand controlled the outcome of the reaction, leading to simple β-keto amine products in the absence of added ligand and the cyclized 7- and 8-substituted-imidazo[1,2-a]pyrrolo[2,3-e]pyrazine products in the presence of ligand. This catalyst control was conserved across a variety of ylide and amine coupling partners. The substrate was shown to act as a ligand for the iridium catalyst in the absence of other ligands via NMR spectroscopy. Kinetic studies indicated that formation of the Ir-carbene was reversible and the slow step of the reaction. These mechanistic investigations suggest that the β-keto amine products form via an intramolecular carbene N-H insertion, and the imidazopyrrolopyrazines form via an intermolecular carbene N-H insertion.

Toward Structural Correctness: Aquatolide and the Importance of 1D Proton NMR FID Archiving

The revision of the structure of the sesquiterpene aquatolide from a bicyclo[2.2.0]hexane to a bicyclo[2.1.1]hexane structure using compelling NMR data, X-ray crystallography, and the recent confirmation via full synthesis exemplify that the achievement of “structural correctness” depends on the completeness of the experimental evidence. Archived FIDs and newly acquired aquatolide spectra demonstrate that archiving and rigorous interpretation of 1D ¹H NMR data may enhance the reproducibility of (bio)chemical research and curb the growing trend of structural misassignments. Despite being the most accessible NMR experiment, 1D ¹H spectra encode a wealth of information about bonds and molecular geometry that may be fully mined by ¹H iterative full spin analysis (HiFSA). Fully characterized 1D ¹H spectra are unideterminant for a given structure. The corresponding FIDs may be readily submitted with publications and collected in databases. Proton NMR spectra are indispensable for structural characterization even in conjunction with 2D data. Quantum interaction and linkage tables (QuILTs) are introduced for a more intuitive visualization of 1D J-coupling relationships, NOESY correlations, and heteronuclear experiments. Overall, this study represents a significant contribution to best practices in NMR-based structural analysis and dereplication.

Total (1)H NMR assignment of 3β-acetoxypregna-5,16-dien-20-one

This work describes the total and unambiguous assignment of the 750 MHz (1)H NMR spectrum of 3β-acetoxypregna-5,16-dien-20-one or 16-DPA (1), the well-known intermediate utilized in the synthesis of biological important commercial steroids. The task was accomplished by extracting the coupling constant values in the overlapped spectrum region by HSQC, and using these values in the (1)H iterative full spin analysis integrated in the PERCH NMR software. Comparison of the experimental vicinal coupling constants of 1 with the values calculated using Altona provides an excellent correlation. The same procedure, when applied to the published data of progesterone (2) and testosterone (3), afforded an acceptable correlation for 2 and a poor correlation for 3. In the last case, this suggested the reassignment of all four vicinal coupling constants for the methylene signals at the C-15 and C-16 positions, demonstrating the utility of this methodology.

Subtle Chemical Shifts Explain the NMR Fingerprints of Oligomeric Proanthocyanidins with High Dentin Biomodification Potency

The ability of certain oligomeric proanthocyanidins (OPACs) to enhance the biomechanical properties of dentin involves collagen cross-linking of the 1.3-4.5 nm wide space via protein-polyphenol interactions. A systematic interdisciplinary search for the bioactive principles of pine bark has yielded the trimeric PAC, ent-epicatechin-(4β→8)-epicatechin-(2β→O→7,4β→8)-catechin (3), representing the hitherto most potent single chemical entity capable of enhancing dentin stiffness. Building the case from two congeneric PAC dimers, a detailed structural analysis decoded the stereochemistry, spatial arrangement, and chemical properties of three dentin biomodifiers. Quantum-mechanics-driven (1)H iterative full spin analysis (QM-HiFSA) of NMR spectra distinguished previously unrecognized details such as higher order J coupling and provided valuable information about 3D structure. Detection and quantification of H/D-exchange effects by QM-HiFSA identified C-8 and C-6 as (re)active sites, explain preferences in biosynthetic linkage, and suggest their involvement in dentin cross-linking activity. Mapping of these molecular properties underscored the significance of high δ precision in both (1)H and (13)C NMR spectroscopy. Occurring at low- to subppb levels, these newly characterized chemical shift differences in ppb are small but diagnostic measures of dynamic processes inherent to the OPAC pharmacophores and can help augment our understanding of nanometer-scale intermolecular interactions in biomodified dentin macromolecules.

Digital NMR profiles as building blocks: assembling ¹H fingerprints of steviol glycosides

This report describes a fragment-based approach to the examination of congeneric organic compounds by NMR spectroscopy. The method combines the classic interpretation of 1D- and 2D-NMR data sets with contemporary computer-assisted NMR analysis. Characteristic NMR profiles of key structural motifs were generated by (1)H iterative full spin analysis and then joined together as building blocks to recreate the (1)H NMR spectra of increasingly complex molecules. To illustrate the methodology described, a comprehensive analysis of steviol (1), seven steviol glycosides (2-8) and two structurally related isosteviol compounds (9, 10) was carried out. The study also assessed the potential impact of this method on relevant aspects of natural product research including structural verification, chemical dereplication, and mixture analysis.

K-targeted metabolomic analysis extends chemical subtraction to DESIGNER extracts: selective depletion of extracts of hops (Humulus lupulus)

This study introduces a flexible and compound targeted approach to Deplete and Enrich Select Ingredients to Generate Normalized Extract Resources, generating DESIGNER extracts, by means of chemical subtraction or augmentation of metabolites. Targeting metabolites based on their liquid-liquid partition coefficients (K values), K targeting uses countercurrent separation methodology to remove single or multiple compounds from a chemically complex mixture, according to the following equation: DESIGNER extract = total extract ± target compound(s). Expanding the scope of the recently reported depletion of extracts by immunoaffinity or solid phase liquid chromatography, the present approach allows a more flexible, single- or multi-targeted removal of constituents from complex extracts such as botanicals. Chemical subtraction enables both chemical and biological characterization, including detection of synergism/antagonism by both the subtracted targets and the remaining metabolite mixture, as well as definition of the residual complexity of all fractions. The feasibility of the DESIGNER concept is shown by K-targeted subtraction of four bioactive prenylated phenols, isoxanthohumol (1), 8-prenylnaringenin (2), 6-prenylnaringenin (3), and xanthohumol (4), from a standardized hops (Humulus lupulus L.) extract using specific solvent systems. Conversely, adding K-targeted isolates allows enrichment of the original extract and hence provides an augmented DESIGNER material. Multiple countercurrent separation steps were used to purify each of the four compounds, and four DESIGNER extracts with varying depletions were prepared. The DESIGNER approach innovates the characterization of chemically complex extracts through integration of enabling technologies such as countercurrent separation, K-by-bioactivity, the residual complexity concepts, as well as quantitative analysis by (1)H NMR, LC-MS, and HiFSA-based NMR fingerprinting.

Importance of purity evaluation and the potential of quantitative ¹H NMR as a purity assay

In any biomedical and chemical context, a truthful description of chemical constitution requires coverage of both structure and purity. This qualification affects all drug molecules, regardless of development stage (early discovery to approved drug) and source (natural product or synthetic). Purity assessment is particularly critical in discovery programs and whenever chemistry is linked with biological and/or therapeutic outcome. Compared with chromatography and elemental analysis, quantitative NMR (qNMR) uses nearly universal detection and provides a versatile and orthogonal means of purity evaluation. Absolute qNMR with flexible calibration captures analytes that frequently escape detection (water, sorbents). Widely accepted structural NMR workflows require minimal or no adjustments to become practical ¹H qNMR (qHNMR) procedures with simultaneous qualitative and (absolute) quantitative capability. This study reviews underlying concepts, provides a framework for standard qHNMR purity assays, and shows how adequate accuracy and precision are achieved for the intended use of the material.

Stereospecific (5) JHortho,OMe couplings in methoxyindoles, methoxycoumarins, and methoxyflavones

Long-range coupling constants (5) JHortho,OMe were measured in series of methoxyindoles, methoxycoumarins, and methoxyflavones by the modified J doubling in the frequency domain method. The COSY and NOESY spectra revealed the coupling of the -OMe group with a specific proton at the ortho position and its preferred conformation. Homonuclear (1) H-(1) H couplings were confirmed by irradiation of the -OMe signal. Density functional theory calculations of (5) JHortho,OMe using the modified aug-cc-pVTZ basis set evidenced that the Fermi contact term shows good agreement with the experimental J values. Accurate chemical shift and coupling constant values followed after iterative quantum mechanical spectral analysis using the PERCH software.

Essential parameters for structural analysis and dereplication by (1)H NMR spectroscopy

The present study demonstrates the importance of adequate precision when reporting the δ and J parameters of frequency domain (1)H NMR (HNMR) data. Using a variety of structural classes (terpenoids, phenolics, alkaloids) from different taxa (plants, cyanobacteria), this study develops rationales that explain the importance of enhanced precision in NMR spectroscopic analysis and rationalizes the need for reporting Δδ and ΔJ values at the 0.1-1 ppb and 10 mHz level, respectively. Spectral simulations paired with iteration are shown to be essential tools for complete spectral interpretation, adequate precision, and unambiguous HNMR-driven dereplication and metabolomic analysis. The broader applicability of the recommendation relates to the physicochemical properties of hydrogen ((1)H) and its ubiquity in organic molecules, making HNMR spectra an integral component of structure elucidation and verification. Regardless of origin or molecular weight, the HNMR spectrum of a compound can be very complex and encode a wealth of structural information that is often obscured by limited spectral dispersion and the occurrence of higher order effects. This altogether limits spectral interpretation, confines decoding of the underlying spin parameters, and explains the major challenge associated with the translation of HNMR spectra into tabulated information. On the other hand, the reproducibility of the spectral data set of any (new) chemical entity is essential for its structure elucidation and subsequent dereplication. Handling and documenting HNMR data with adequate precision is critical for establishing unequivocal links between chemical structure, analytical data, metabolomes, and biological activity. Using the full potential of HNMR spectra will facilitate the general reproducibility for future studies of bioactive chemicals, especially of compounds obtained from the diversity of terrestrial and marine organisms.

2D NMR barcoding and differential analysis of complex mixtures for chemical identification: the Actaea triterpenes

The interpretation of NMR spectroscopic information for structure elucidation involves decoding of complex resonance patterns that contain valuable molecular information (δ and J), which is not readily accessible otherwise. We introduce a new concept of 2D-NMR barcoding that uses clusters of fingerprint signals and their spatial relationships in the δ-δ coordinate space to facilitate the chemical identification of complex mixtures. Similar to widely used general barcoding technology, the structural information of individual compounds is encoded as a specifics pattern of their C,H correlation signals. Software-based recognition of these patterns enables the structural identification of the compounds and their discrimination in mixtures. Using the triterpenes from various Actaea (syn. Cimicifuga) species as a test case, heteronuclear multiple-bond correlation (HMBC) barcodes were generated on the basis of their structural subtypes from a statistical investigation of their δH and δC data in the literature. These reference barcodes allowed in silico identification of known triterpenes in enriched fractions obtained from an extract of A. racemosa (black cohosh). After dereplication, a differential analysis of heteronuclear single-quantum correlation (HSQC) spectra even allowed for the discovery of a new triterpene. The 2D barcoding concept has potential application in a natural product discovery project, allowing for the rapid dereplication of known compounds and as a tool in the search for structural novelty within compound classes with established barcodes.