Towards unbiased and more versatile NMR-based structure elucidation: A powerful combination of CASE algorithms and DFT calculations

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Enhancing Efficiency of Natural Product Structure Revision: Leveraging CASE and DFT over Total Synthesis

Natural products remain one of the major sources of coveted, biologically active compounds. Each isolated compound undergoes biological testing, and its structure is usually established using a set of spectroscopic techniques (NMR, MS, UV-IR, ECD, VCD, etc.). However, the number of erroneously determined structures remains noticeable. Structure revisions are very costly, as they usually require extensive use of spectroscopic data, computational chemistry, and total synthesis. The cost is particularly high when a biologically active compound is resynthesized and the product is inactive because its structure is wrong and remains unknown. In this paper, we propose using Computer-Assisted Structure Elucidation (CASE) and Density Functional Theory (DFT) methods as tools for preventive verification of the originally proposed structure, and elucidation of the correct structure if the original structure is deemed to be incorrect. We examined twelve real cases in which structure revisions of natural products were performed using total synthesis, and we showed that in each of these cases, time-consuming total synthesis could have been avoided if CASE and DFT had been applied. In all described cases, the correct structures were established within minutes of using the originally published NMR and MS data, which were sometimes incomplete or had typos.

DELTA50: A Highly Accurate Database of Experimental 1H and 13C NMR Chemical Shifts Applied to DFT Benchmarking

Density functional theory (DFT) benchmark studies of ¹H and ¹³C NMR chemical shifts often yield differing conclusions, likely due to non-optimal test molecules and non-standardized data acquisition. To address this issue, we carefully selected and measured ¹H and ¹³C NMR chemical shifts for 50 structurally diverse small organic molecules containing atoms from only the first two rows of the periodic table. Our NMR dataset, DELTA50, was used to calculate linear scaling factors and to evaluate the accuracy of 73 density functionals, 40 basis sets, 3 solvent models, and 3 gauge-referencing schemes. The best performing DFT methodologies for ¹H and ¹³C NMR chemical shift predictions were WP04/6-311++G(2d,p) and ωB97X-D/def2-SVP, respectively, when combined with the polarizable continuum solvent model (PCM) and gauge-independent atomic orbital (GIAO) method. Geometries should be optimized at the B3LYP-D3/6-311G(d,p) level including the PCM solvent model for the best accuracy. Predictions of 20 organic compounds and natural products from a separate probe set had root-mean-square deviations (RMSD) of 0.07 to 0.19 for ¹H and 0.5 to 2.9 for ¹³C. Maximum deviations were less than 0.5 and 6.5 ppm for ¹H and ¹³C, respectively.

Ominoxanthone-The First Xanthone Linearly Fused to a γ-Lactone from Cortinarius ominosus Bidaud Basidiomata. CASE- and DFT-Based Structure Elucidation

The UHPLC-HRMS analysis of Cortinarius ominosus basidiomata extract revealed that this mushroom accumulated elevated yields of an unreported specialized metabolite. The molecular formula of this unknown compound, C₁₇H₁₀O₈, indicated that a challenging structure elucidation lay ahead, owing to its critically low H/C atom ratio. The structure of this new isolate, namely ominoxanthone (1), could not be solved from the interpretation of the usual set of 1D/2D NMR data that conveyed too limited information to afford a single, unambiguous structure. To remedy this, a Computer-Assisted Structure Elucidation (CASE) workflow was used to rank the different possible structure candidates consistent with our scarce spectroscopic data. DFT-based chemical shift calculations on a limited set of top-ranked structures further ascertained the determined structure for ominoxanthone. Although the determined scaffold of ominoxanthone is unprecedented as a natural product, a plausible biosynthetic scenario involving a precursor known from cortinariaceous sources and classical biogenetic reactions could be proposed.

Computational 1 H and 13 C NMR in structural and stereochemical studies

Present review outlines the advances and perspectives of computational ¹ H and ¹³ C NMR applied to the stereochemical studies of inorganic, organic, and bioorganic compounds, involving in particular natural products, carbohydrates, and carbonium ions. The first part of the review briefly outlines theoretical background of the modern computational methods applied to the calculation of chemical shifts and spin-spin coupling constants at the DFT and the non-empirical levels. The second part of the review deals with the achievements of the computational ¹ H and ¹³ C NMR in the stereochemical investigation of a variety of inorganic, organic, and bioorganic compounds, providing in an abridged form the material partly discussed by the author in a series of parent reviews. Major attention is focused herewith on the publications of the recent years, which were not reviewed elsewhere.

ACD/Structure Elucidator: 20 Years in the History of Development

The first methods associated with the Computer-Assisted Structure Elucidation (CASE) of small molecules were published over fifty years ago when spectroscopy and computer science were both in their infancy. The incredible leaps in both areas of technology could not have been envisaged at that time, but both have enabled CASE expert systems to achieve performance levels that in their present state can outperform many scientists in terms of speed to solution. The computer-assisted analysis of enormous matrices of data exemplified 1D and 2D high-resolution NMR spectroscopy datasets can easily solve what just a few years ago would have been deemed to be complex structures. While not a panacea, the application of such tools can provide support to even the most skilled spectroscopist. By this point the structures of a great number of molecular skeletons, including hundreds of complex natural products, have been elucidated using such programs. At this juncture, the expert system ACD/Structure Elucidator is likely the most advanced CASE system available and, being a commercial software product, is installed and used in many organizations. This article will provide an overview of the research and development required to pursue the lofty goals set almost two decades ago to facilitate highly automated approaches to solving complex structures from analytical spectroscopy data, using NMR as the primary data-type.

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

The first efforts for the development of methods for Computer-Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one-dimensional (1D) and two-dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long-range correlation experiments, pure-shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X-ray analysis, and micro-Electron Diffraction [micro-ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future.

Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules

The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust ‘local’ machine learning (ML) strategy capturing the effect of the neighborhood on an atom’s ‘near-sighted’ property—chemical shielding; (2) an accurate reference dataset generated with a state-of-the-art first-principles method for training. Herein we report the QM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in 134k molecules of the QM9 dataset in gas and five common solvent phases. Using these data for training, we present benchmark results for the prediction transferability of kernel-ridge regression models with popular local descriptors. Our best model, trained on 100k samples, accurately predicts isotropic shielding of 50k ‘hold-out’ atoms with a mean error of less than 1.9 ppm. For the rapid prediction of new query molecules, the models were trained on geometries from an inexpensive theory. Furthermore, by using a Δ-ML strategy, we quench the error below 1.4 ppm. Finally, we test the transferability on non-trivial benchmark sets that include benchmark molecules comprising 10–17 heavy atoms and drugs.

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.

Unequivocal structure confirmation of a breitfussin analog by anisotropic NMR measurements

Structural features of proton-deficient heteroaromatic natural products, such as the breitfussins, can severely complicate their characterization by NMR spectroscopy. For the breitfussins in particular, the constitution of the five-membered oxazole central ring cannot be unequivocally established via conventional NMR methods when the 4′-position is halogenated. The level of difficulty is exacerbated by 4′-iodination, as the accuracy with which theoretical NMR parameters are determined relies extensively on computational treatment of the relativistic effects of the iodine atom. It is demonstrated in the present study, that the structure of a 4′-iodo breitfussin analog can be unequivocally established by anisotropic NMR methods, by adopting a reduced singular value decomposition (SVD) protocol that leverages the planar structures exhibited by its conformers.

Structural features of proton-deficient heteroaromatic natural products, such as the breitfussins, can severely complicate their characterization by NMR spectroscopy.

The 1 H and 13 C NMR chemical shifts of Strychnos alkaloids revisited at the DFT level

The density functional theory calculation of ¹ H and ¹³ C NMR chemical shifts in a series of ten 10 classically known Strychnos alkaloids with a strychnine skeleton was performed at the PBE0/pcSseg-2//pcseg-2 level. It was found that calculated ¹ H and ¹³ C NMR chemical shifts provided a markedly good correlation with experiment characterized by a mean absolute error of 0.08 ppm in the range of 7 ppm for protons and 1.67 ppm in the range of 150 ppm for carbons, so that a mean absolute percentage error was as small as ~1% in both cases.

Enhancing computer-assisted structure elucidation with DFT analysis of J-couplings

Computer-assisted structure elucidation (CASE) is the class of expert systems that derives molecular structures primarily from one-dimensional and two-dimensional nuclear magnetic resonance data. Contemporary CASE systems, including Advanced Chemistry Development/Structure Elucidator (ACD/SE), consider cross-peaks in heteronuclear multiple bond coherence (HMBC) and correlation spectroscopy (COSY) spectra as two- or three-bond correlations by default. However, four and more bond correlations (nonstandard correlations [NSCs]) could be present in these spectra too. The indiscriminate addition of NSCs to the CASE computations is prohibitively expensive. To address this problem, the ACD/SE program performs a logical analysis of observed correlations and determines the minimum number of NSCs. Guided by this information, a more efficient fuzzy structure generation (FSG) algorithm is subsequently applied. Until now, the FSG algorithm was utilized without any verification of the reliability of found NSCs. Here, we report a verification method for NSCs based on the relationship between NSCs and J-couplings computed with high accuracy density functional theory (DFT) methods. We used the example of strychnine to show that 41 (32%) of 8-Hz HMBC cross-peaks were NSCs and were consistent with ^4-6 J_CH couplings greater than 0.3 Hz. This cutoff value was largely confirmed by the analysis of NSCs in 11 real-world natural products elucidated by ACD/SE. Additionally, utilizing the example of the CASE study of cleospinol A, we showed that the DFT-computed J-couplings of NSCs can distinctively differentiate the correct structure among six proposed isomers. The proposed approach of NSC verification should further improve the robustness of CASE analysis and can help reveal potential problems with reported experimental data.

Gene Cluster Activation in a Bacterial Symbiont Leads to Halogenated Angucyclic Maduralactomycins and Spirocyclic Actinospirols

Growth from spores activated a biosynthetic gene cluster in Actinomadura sp. RB29, resulting in the identification of two novel groups of halogenated polyketide natural products, named maduralactomycins and actinospirols. The unique tetracyclic and spirocyclic structures were assigned based on a combination of NMR analysis, chemoinformatic calculations, X-ray crystallography, and ¹³C labeling studies. On the basis of HRMS² data, genome mining, and gene expression studies, we propose an underlying noncanonical angucycline biosynthesis and extensive post-polyketide synthase (PKS) oxidative modifications.

Computational 1 H NMR: Part 3. Biochemical studies

This is the third and the last part of three closely interrelated reviews dealing with computation of ¹ H nuclear magnetic resonance chemical shifts and ¹ H-¹ H spin-spin coupling constants. Present review deals with the computation of these parameters in biologically active natural products, carbohydrates, and other molecules of biological origin focusing on stereochemical applications of computational ¹ H nuclear magnetic resonance to these objects.

Structural Elucidation of Presilphiperfolane-7α,8α-diol, a Bioactive Sesquiterpenoid from Pulicaria vulgaris: A Combined Approach of Solvent-Induced Chemical Shifts, GIAO Calculation of Chemical Shifts, and Full Spin Analysis

Structural elucidation of a new triquinane sesquiterpenoid, presilphiperfolane-7α,8α-diol, 1a, isolated from Pulicaria vulgaris, was accomplished by combining solvent-induced removal of chemical shift degeneracy and computational (DFT-GIAO) prediction of NMR spectra with the analysis of ¹H NMR splitting patterns. In addition to extensive NMR experiments (in 10 different solvents), MS, and FTIR, the identity of 1a was also confirmed by chemical transformations. The applied approach can facilitate structural elucidation of organic molecules and decrease the probability of an erroneous identification, permitting an unambiguous stereochemical elucidation and full NMR assignment. The pharmacological/toxicological profile of 1a was evaluated in several different models.

Computational protocols for calculating 13C NMR chemical shifts

The most recent results dealing with the computation of ¹³C NMR chemical shifts in chemistry (small molecules, saturated, unsaturated and aromatic compounds, heterocycles, functional derivatives, coordination complexes, carbocations, and natural products) are reviewed, paying special attention to theoretical background and accuracy, the latter involving solvent effects, vibrational corrections, and relativistic effects.

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.

Theoretical calculations of carbon-hydrogen spin-spin coupling constants

Structural applications of theoretical calculations of carbon-hydrogen spin-spin coupling constants are reviewed covering papers published mainly during the last 10-15 years with a special emphasis on the most notable studies of hybridization, substitution and stereoelectronic effects together with the investigation of hydrogen bonding and intermolecular interactions. The wide scope of different applications of calculated carbon-hydrogen couplings in the structural elucidation of particular classes of organic and bioorganic molecules is reviewed, concentrating mainly on saturated, unsaturated, aromatic and heteroaromatic compounds and their functional derivatives, as well as on natural compounds and carbohydrates. The review is dedicated to Professor Emeritus Michael Barfield in view of his invaluable pioneering contribution to this field.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.