Recent trends in the structural revision of natural products

Accurate Structural Elucidation of Samoquasine A and an Unknown Homologue Using a Computation-Based Machine Learning Protocol

The structure of samoquasine A has long been a subject of controversy, which was resolved only upon its successful total synthesis. We examined the structures of the associated compounds using the state-of-the-art SVM-M protocol. The method accurately discriminated all putative structures historically attributed to samoquasine A from a pool of 48 isomeric structures, confirming that samoquasine A is indeed identical to perlolidine. Furthermore, by applying the SVM-M protocol to an additional pool of 67 isomeric structures, we successfully assigned a yet unknown natural product, initially misidentified as perlolidine, as a novel oxime, (E)-3H-cyclopenta[c]quinolin-3-one oxime, representing the first reported cyclone oxime-quinoline natural product.

HSQC Spectra Simulation and Matching for Molecular Identification

In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML). For the latter two techniques, we developed a reconstruction logic to combine proton and carbon 1D spectra into HSQC spectra. The methodology involved the implementation of three peak-matching strategies (minimum-sum, Euclidean-distance, and Hungarian distance) combined with three padding strategies (zero-padding, peak-truncated, and nearest-neighbor double assignment). We found that coupling these strategies with a robust simulation technique facilitates the accurate identification of correct molecules from similar analogues (regio- and stereoisomers) and allows for fast and accurate large database searches. Furthermore, we demonstrated the efficacy of the best-performing methodology by rectifying the structures of a set of previously misidentified molecules. This research indicates that effective HSQC spectral simulation and matching methodologies significantly facilitate molecular structure elucidation. Furthermore, we offer a Google Colab notebook for researchers to use our methods on their own data (https://github.com/AstraZeneca/hsqc_structure_elucidation.git).

Structural Revision of Tinotufolins from Tinospora crispa Leaves Guided by Empirical Rules and DFT Calculations

Clerodane diterpenes are a class of secondary metabolites that can be classified into four types according to the configuration of the H3-19/H-10-H3-17/H3-20 fragment, i.e., trans-cis (TC), trans-trans (TT), cis-cis (CC), and cis-trans (CT). Tinotufolins A-C and E (1a-3a and 5a), isolated from the leaves of Tinospora crispa, were previously elucidated as CT-type clerodanes; however, our established 13C NMR-based empirical rules and density functional theory calculations suggested that these clerodanes belong to the CC type. Therefore, tinotufolins A-F (1-6) were reisolated from the leaves of T. crispa, along with an undescribed compound 7 and known compounds 8-11, and their structures were established by extensive spectroscopic analyses. The structures of tinotufolins A-C and E were revised to CC-type 1-3 and 5, and undescribed compound 7 was established as a CC-type clerodane. The present study demonstrates that empirical rules and calculations can efficiently identify and revise erroneous structures in clerodane diterpenes.

Temperature-Controllable Liquid Crystalline Medium for Stereochemical Elucidation of Organic Compounds via Residual Chemical Shift Anisotropies

The configuration elucidation of organic molecules continues to pose significant challenges in studies involving stereochemistry. Nuclear magnetic resonance (NMR) techniques are powerful for obtaining such structural information. Anisotropic NMR techniques, such as measurement of residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), complementing isotropic NMR parameters, provide relative configuration information. RCSAs provide valuable structural information, especially for nonprotonated carbons, yet have been severely underutilized due to the lack of an easily operational alignment medium capable of rapid transition from anisotropic to isotropic environments, especially in aqueous conditions. In this study, an oligopeptide-based alignment media (FK)4 is presented for RCSA measurements. Temperature variation manipulates the assembly of (FK)4, yielding tunable anisotropic and isotropic phases without the requirement of any special devices or time-consuming correction procedures during data analysis. Decent observed ΔΔRCSA values from sp3 carbons benefit the utilization of RCSA measurements in the structural elucidation of organic molecules highly composed with sp3 carbons. Moreover, the (FK)4 alignment medium is applicable for both RDC and RCSA measurements in one sample, further advancing the configuration analysis of molecules of interest.

Structure Revision of Formyl Phloroglucinol Meroterpenoids: A Unified Approach Using NMR Fingerprinting and DFT NMR and ECD Analyses

NMR fingerprints are valuable tools for analyzing complex natural product mixtures and identifying incorrectly assigned structures in the literature. Our diagnostic NMR fingerprints for formyl phloroglucinol meroterpenoids revealed discrepancies in the structures reported for eucalyprobusal C (1a) and eucalypcamal K (2a). NMR fingerprinting PCA analyses identified 1a as an oxepine-diformyl phloroglucinol and 2a as an oxepine 3-acyl-1-formyl phloroglucinol, contrary to their initial assignments as pyrano-diformyl and pyrano 3-acyl-1-formyl phloroglucinols, respectively. Extensive reinterpretation of their reported one- and two-dimensional NMR data, coupled with GIAO DFT-calculated 1H and 13C NMR chemical shift and DP4+ analyses, supported the unequivocal reassignment of eucalyprobusal C to 1b and eucalypcamal K to 2b. The absolute configurations of the revised oxepine-containing phloroglucinol meroterpenoids were confirmed via the reinterpretation of their reported ROESY and NOESY NMR data, along with comparative TDDFT-calculated and experimental ECD spectra.

High-Throughput Identification of Crystalline Natural Products from Crude Extracts Enabled by Microarray Technology and microED

The structural determination of natural products (NPs) can be arduous because of sample heterogeneity. This often demands iterative purification processes and characterization of complex molecules that may be available only in miniscule quantities. Microcrystal electron diffraction (microED) has recently shown promise as a method to solve crystal structures of NPs from nanogram quantities of analyte. However, its implementation in NP discovery remains hampered by sample throughput and purity requirements, akin to traditional NP-discovery workflows. In the methods described herein, we leverage the resolving power of transmission electron microscopy (TEM) and the miniaturization capabilities of deoxyribonucleic acid (DNA) microarray technology to address these challenges through the establishment of an NP screening platform, array electron diffraction (ArrayED). In this workflow, an array of high-performance liquid chromatography (HPLC) fractions taken from crude extracts was deposited onto TEM grids in picoliter-sized droplets. This multiplexing of analytes on TEM grids enables 1200 or more unique samples to be simultaneously inserted into a TEM instrument equipped with an autoloader. Selected area electron diffraction analysis of these microarrayed grids allows for the rapid identification of crystalline metabolites. In this study, ArrayED enabled structural characterization of 14 natural products, including four novel crystal structures and two novel polymorphs, from 20 crude extracts. Moreover, we identify several chemical species that would not be detected by standard mass spectrometry (MS) or ultraviolet-visible (UV/vis) spectroscopy and crystal forms that would not be characterized using traditional methods.

The Synergy between Nuclear Magnetic Resonance and Density Functional Theory Calculations

This paper deals with the synergy between Nuclear Magnetic Resonance (NMR) spectroscopic investigations and DFT calculations, mainly of NMR parameters. Both the liquid and the solid states are discussed here. This text is a mix of published results supplemented with new findings. This paper deals with examples in which useful results could not have been obtained without combining NMR measurements and DFT calculations. Examples of such cases are tautomeric systems in which NMR data are calculated for the tautomers; hydrogen-bonded systems in which better XH bond lengths can be determined; cage compounds for which assignment cannot be made based on NMR data alone; revison of already published structures; ionic compounds for which reference data are not available; assignment of solid-state spectra and crystal forms; and the creation of libraries for biological molecules. In addition to these literature cases, a revision of a cage structure and substituent effects on pyrroles is also discussed.

Penicitone: Structural Reassignment of a Proposed Natural Product Acid Chloride

The proposed structure for the natural product penicitone, which contained a chemically improbable acid chloride functional group, was reassigned to a more probable structure using a combination of chemical knowledge, computer-assisted structure elucidation, and DFT methods.

DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation

DP4+ is one of the most popular methods for the structure elucidation of natural products using NMR calculations. While the method is simple and easy to implement, it requires a series of procedures that can be tedious, coupled with the fact that its computational demand can be high in certain cases. In this work, we made a substantial improvement to these limitations. First, we deeply explored the effect of molecular mechanics architecture on the DP4+ formalism (MM-DP4+). In addition, a Python applet (DP4+App) was developed to automate the entire process, requiring only the Gaussian NMR output files and a spreadsheet containing the experimental NMR data and labels. The script is designed to use the statistical parameters from the original 24 levels of theory (employing B3LYP/6-31G* geometries) and the new 36 levels explored in this work (over MMFF geometries). Furthermore, it enables the development of customizable methods using any desired level of theory, allowing for a free choice of test molecules.

OSMAC Approach and Cocultivation for the Induction of Secondary Metabolism of the Fungus Pleotrichocladium opacum

The application of an OSMAC (One Strain-Many Compounds) approach on the fungus Pleotrichocladium opacum, isolated from a soil sample collected on the coast of Asturias (Spain), using different culture media, chemical elicitors, and cocultivation techniques resulted in the isolation and identification of nine new compounds (8, 9, 12, 15-18, 20, 21), along with 15 known ones (1-7, 10, 11, 14, 19, 22-25). Compounds 1-9 were detected in fungal extracts from JSA liquid fermentation, compounds 10-12 were isolated from a solid rice medium, whereas compounds 14 and 15 were isolated from a solid wheat medium. Addition of 5-azacytidine to the solid rice medium caused the accumulation of compounds 16-18, whereas adding N-acetyl-d-glucosamine triggered the production of two additional metabolites, 19 and 20. Finally, cocultivation of the fungus Pleotrichocladium opacum with Echinocatena sp. in a solid PDA medium led to the production of five additional natural products, 21-25. The structures of the new compounds were elucidated by HRESIMS and 1D and 2D NMR as well as by comparison with literature data. DP4+ and mix-J-DP4 computational methods were applied to determine the relative configurations of the novel compounds, and in some cases, the absolute configurations were assigned by a comparison of the optical rotations with those of related natural products.

Road Map Toward Computer-Guided Total Synthesis of Natural Products. The Dysiherbol A Case Study: What if Serendipity Hadn't Intervened?

We present a computational study inspired by the story of dysiherbol A, a natural product whose putative structure was found incorrect through synthesis by a completely fortuitous event. While the carbon connectivity and chemical environment between both structures remain similar, the real dysiherbol A has a different molecular weight than that reported for the natural product. Had the synthesis groups not been favored by fortune, it could be speculated that a substantial amount of time and effort would have been required to solve the structural puzzle. Within the realm of computer-guided total synthesis of natural products, the question arose whether a synthesis group could have in silico reassigned the structure before embarking on the experimental adventure. To address this query, we evaluated some state-of-the-art computational procedures based on their computational demand and ease of implementation for nonexpert users with basic skills in computational chemistry (including HOSE, CASCADE, ANN-PRA, ML-J-DP4, DP4, and DP4+). While discussing the strengths and limitations of these methods, this case study provides a roadmap of what could be done before venturing into complex and time-demanding total synthesis projects.

The Ethics of Publishing Biomedical and Natural Products Research

Given that the essence of Science is a search for the truth, one might expect that those identifying as scientists would be conscientious and observant of the demands this places on them. However, that expectation is not fulfilled universally as, not too surprisingly, egregious examples of unethical behavior appear and are driven by money, personal ambition, performance pressure, and other incentives. The reproducibility-, fact-, and truth-oriented modus operandi of Science has come to face a variety of challenges. Organized into 11 cases, this article outlines examples of compromised integrity from borderline to blatant unethical behavior that disgrace our profession unnecessarily. Considering technological developments in neural networks/artificial intelligence, a host of factors are identified as impacting Good Ethical Practices. The goal is manifold: to raise awareness and offer perspectives for refocusing on Science and true scientific evidence; to trigger discussion and developments that strengthen ethical behavior; to foster the recognition of the beauty, simplicity, and rewarding nature of scientific integrity; and to highlight the originality of intelligence.

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.

Computational Structural Revision of Elaeophorbate and Other Triterpenoids with the Help of NAPROC-13. A New Strategy for Structural Revision of Natural Products

A considerable number of natural products have been published in recent years with misassigned structure, even though they had been correctly elucidated in the past. The availability of databases containing revised structures can prevent the amplification of errors in structural elucidation. NAPROC-13, a dereplication tool based on the ¹³C chemical shift, has been used to search for substances that, possessing the same chemical shifts, have been described with different structures. The correct structure of these different structural proposals is verified by computational chemistry. This paper reports the structural revision of nine triterpenoids following this methodology.

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.

Absolute configuration of cyclopropanes and the structural revision of pyrones from Marine-derived fungus Stagonospora sp. SYSU-MS7888

Two new cyclopropane derivatives (1-2) and seven undescribed α-pyrone derivatives (3-9), along with one known congener (10) were obtained from the marine fungus Stagonospora sp. SYSU-MS7888, which was isolated from the South China Sea. Their planar structures were established through extensive spectroscopic analyses including 1D and 2D NMR and HR-ESIMS. The absolute configurations were identified on basis of the quantum chemical calculations of ECD and NMR, as well as the modified Mosher's method. It's particularly noteworthy that the tetrasubstituted furopyrans, chenopodolans A-F, possessing phytotoxicity and zootoxicity, were structural misassignments. The structures of chenopodolans featuring with furopyran skeleton were revised as common trisubstituted α-pyrones by computational chemistry, NMR spectroscopic method, and empirical rule. Compounds 1, 2, 7, and 9 showed significant anti-inflammatory activity with IC₅₀ values ranging from 3.6 to 22.8 μM, which is better than the positive control indomethacin (IC₅₀ = 26.5 ± 1.13 μM). This discovery holds potential for the development of new anti-inflammatory agents.

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge

Structure elucidation of unknown compounds based on nuclear magnetic resonance (NMR) remains a challenging problem in both synthetic organic and natural product chemistry. Library matching has been an efficient method to assist structure elucidation. However, it is limited by the coverage of libraries. In addition, prior knowledge such as molecular fragments is neglected. To solve the problem, we propose a conditional molecular generation net (CMGNet) to allow input of multiple sources of information. CMGNet not only uses ¹³C NMR spectrum data as input but molecular formulas and fragments of molecules are also employed as input conditions. Our model applies large-scale pretraining for molecular understanding and fine-tuning on two NMR spectral data sets of different granularity levels to accommodate structure elucidation tasks. CMGNet generates structures based on ¹³C NMR data, molecular formula, and fragment information, with a recovery rate of 94.17% in the top 10 recommendations. In addition, the generative model performed well in the generation of various classes of compounds and in the structural revision task. CMGNet has a deep understanding of molecular connectivities from ¹³C NMR, molecular formula, and fragments, paving the way for a new paradigm of deep learning-assisted inverse problem-solving.

Future of computational molecular spectroscopy-from supporting interpretation to leading the innovation

Molecular spectroscopy measures transitions between discrete molecular energies which follow quantum mechanics. Structural information of a molecule is encoded in the spectra, which can be only decoded using quantum mechanics and therefore computational molecular spectroscopy becomes essential. In this review perspective, the role evolution of computational molecular spectroscopy has been discussed with several joint theory and experiment spectroscopic studies in the past decades, which includes rotational (microwave), vibrational and electronic spectroscopy (valence and core) of molecules. With the development in high resolution and computerized synchrotron sourced spectroscopy, spectral measurements and computational molecular spectroscopy need to be integrated for materials development. Contemporary computational molecular spectroscopy is, therefore, more than merely supporting interpretation but leading the innovation. Future development of molecular spectroscopy lies to identify the niche to integrate experimental and computational molecular spectroscopy. It also requires to engineer molecular spectroscopic databases that function according to the universal approaches of computing, such as those in a Turing machine, to be realized in a chemical and/or spectroscopic programable manner (digital twinning research) in the future.

Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = −0.008, a(R2) = 0.014, and δK = 0.021) and its prediction ability (Q2LMO = 67.89, Q2EXT = 67.75, a(Q2) = −0.068, δQ = 0.0, rm2¯ = 0.229, and Δrm2 = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Epimeric Mixture Analysis and Absolute Configuration Determination Using an Integrated Spectroscopic and Computational Approach-A Case Study of Two Epimers of 6-Hydroxyhippeastidine

Structural elucidation has always been challenging, and misassignment remains a stringent issue in the field of natural products. The growing interest in discovering unknown, complex natural structures accompanies the increasing awareness concerning misassignments in the community. The combination of various spectroscopic methods with molecular modeling has gained popularity in recent years. In this work, we demonstrated, for the first time, its power to fully elucidate the 2-dimensional and 3-dimensional structures of two epimers in an epimeric mixture of 6-hydroxyhippeastidine. DFT calculation of chemical shifts was first performed to assist the assignment of planar structures. Furthermore, relative and absolute configurations were established by three different ways of computer-assisted structure elucidation (CASE) coupled with ORD/ECD/VCD spectroscopies. In addition, the significant added value of OR/ORD computations to relative and absolute configuration determination was also revealed. Remarkably, the differentiation of two enantiomeric scaffolds (crinine and haemanthamine) was accomplished via OR/ORD calculations with cross-validation by ECD and VCD.

Structural analysis of unusual alkaloids isolated from Narcissus pseudonarcissus cv. Carlton

Narciindole A, the first representative of Amaryllidaceae alkaloids with an indol-3-ylmethanone framework, was isolated from bulbs of Narcissus pseudonarcissus (L.) cv. Carlton, together with carltonine D and carltonine E, which share the same unusual structural motif as dimeric carltonine C (reported in 2020), exhibiting atropisomerism. Unambiguous structure elucidations have been achieved by NMR spectroscopy, HRMS, and comparison with literature data of related alkaloids. Furthermore, the chirality of known alkaloids with a galanthindole biaryl core was revised using optical rotation. Last, but not least, a biosynthetic pathway for dimeric carltonine-type alkaloids was proposed. Unfortunately, in terms of biological activity, the isolated alkaloids showed only moderate inhibition of human acetylcholinesterase and/or butyrylcholinesterase.

Reassessing the molecular structures of some previously isolated abietane diterpenoids with a naphthalene moiety and the structure-activity relationship (SAR) of quinone diterpenoids

Crystals of previously described para-naphthoquinone abietane diterpenoids 12,16-dideoxy-aegyptinone B and 12-deoxy-salvipisone were obtained from Zhumeria majdae Rech.f. & Wendelbo. However, single-crystal X-ray diffraction analysis followed by reinterpretation of their NMR data revealed that their structures require revision, and they should be revised to the two ortho-naphthoquinones, zhumerianone C and aethiopinone, respectively. Interestingly, a further search through literature revealed that there were more of such cases, in which differentiation between the ortho-/para-orientation had not been carried out correctly in the structure elucidation of naphthalene containing abietane diterpenoids. Therefore, in the current study, we pointed out some 1D and 2D NMR generalizations that would help the unambiguous deduction of the ortho-/para-orientation of naphthalene containing abietanes and revised the structure of some previously described compounds accordingly. Based on these generalizations, structures of sibiriquinones A and B, sahandinone, and sahandone were revised to the known structures 1,2-didehydromiltirone, miltirone, saprorthoquinone, and sahandone B, respectivelyand tebesinone B, arucadiol, and sahandol II were revised to three undescribed structures. It was also proposed that structures of palmitoyl arucadiol and compounds with the salvifolane skeleton need revision. Furthermore, these structure revisions shed light on the structure-activity relationship of the quinone diterpenoids, approving that the ortho-quinone is the critical structural component for cytotoxicity in these compounds.

May the Force (Field) Be with You: On the Importance of Conformational Searches in the Prediction of NMR Chemical Shifts

NMR data prediction is increasingly important in structure elucidation. The impact of force field selection was assessed, along with geometry and energy cutoffs. Based on the conclusions, we propose a new approach named mix-J-DP4, which provides a remarkable increase in the confidence level of complex stereochemical assignments-100% in our molecular test set-with a very modest increment in computational cost.

Structure Revision of the Sesquiterpene Nordine Based on NMR Spectroscopic Analysis and X-ray Crystallography

Nordine was reported to be an unusual humulene-type macrocyclic sesquiterpenoid that contains an ether-bridged bicyclic ring between C-10 and C-6 with a hydroxy group at position 2. Here, we report the structure revision of nordine based on incongruities found for carbon chemical shifts in the originally proposed structure, in addition to formation of a diacetylated derivative. As expected, a single-crystal X-ray diffraction analysis unambiguously confirmed our proposal that the nordine (1) structure contains an ether-bridged bicyclic ring between C-10 and C-7 and hydroxy groups at C-2 and C-6. Furthermore, the absolute configuration was determined by ECD spectroscopic analysis.

Identification of Photocatalytic Alkaloids from Coptidis Rhizome by an Offline HPLC/CC/SCD Approach

Natural products continue to be a valuable source of active metabolites; however, researchers of natural products are mostly focused on the biological effects, and their chemical utility has been less explored. Furthermore, low throughput is a bottleneck for classical natural product research. In this work, a new offline HPLC/CC/SCD (high performance liquid chromatography followed by co-crystallization and single crystal diffraction) workflow was developed that greatly expedites the discovery of active compounds from crude natural product extracts. The photoactive total alkaloids of the herbal medicine Coptidis rhizome were firstly separated by HPLC, and the individual peaks were collected. A suitable coformer was screened by adding it to the individual peak solution and observing the precipitation, which was then redissolved and used for co-crystallization. Seven new co-crystals were obtained, and all the single crystals were subjected to X-ray diffraction analysis. The molecular structures of seven alkaloids from milligrams of crude extract were resolved within three days. NDS greatly decreases the required crystallization amounts of alkaloids to the nanoscale and enables rapid stoichiometric inclusion of all the major alkaloids with full occupancy, typically without disorder, affording well-refined structures. It is noteworthy that anomalous scattering by the coformer sulfur atoms enables reliable assignment of absolute configuration of stereogenic centers. Moreover, the identified alkaloids were firstly found to be photocatalysts for the green synthesis of benzimidazoles. This study demonstrates a new and green phytochemical workflow that can greatly accelerate natural product discovery from complex samples.

Total Syntheses of the Structures Assigned to the Marine Natural Products Orthoscuticellines A-E

The readily prepared and vinylated β-carboline 11 has been converted over one or two steps into compounds 1-5, the structures assigned to the recently reported marine natural products orthoscuticellines A-E. The spectral data recorded on the synthetically derived compounds are fully consistent with the assigned structures and, on making allowances for variations in the pH of the medium in which the spectra of the natural products were recorded, it is concluded that the structures assigned to orthoscuticellines A-E are most likely correct. Certainly, the calculated ¹³C NMR spectra of the α-, γ-, and δ-carboline isomers of compounds 1-5 suggest that orthoscuticellines A-E do incorporate the assigned β-carboline core.

Structural Revision of the Stemona Alkaloids Tuberostemonine O, Dehydrocroomines A and B, and Dehydrocroomine

The structural revision of four Stemona alkaloids from Stemona tuberosa is reported. The misassignment of the tuberostemonine O structure (1) was recognized when a new alkaloid, tuberostemonine P, was isolated and unambiguously assigned structure 1 in this work. Reinvestigation of the spectroscopic data and NMR calculations led to the revised structure 1a for tuberostemonine O. The structural misassignment of dehydrocroomine A as 2 was corrected by reinterpreting the X-ray crystal structure, which was consistent with 2a. The structural reassignments of dehydrocroomine B (3 to 3a) and dehydrocroomine (4 to 4a) were confirmed by X-ray crystallography and NMR calculations, respectively.

Reassignment of the structures of pestalopyrones A-D

Pestalopyrones A-D are four unusual tricyclic pyrone derivatives with flexible chiral structures, isolated from the endophytic fungus Pestalotiopsis neglecta S3. The full elucidation of their structures was a challenging task, and remained unsolved in the original article. Herein, the relative configurations of pestalopyrones A and pestalopyrones B were unambiguously assigned by detailed analyses on spectroscopic data and GIAO ¹³C NMR calculation method with sorted training sets (STS). The planar structures of pestalopyrones C and pestalopyrones D were revised by reinterpretation of their reported spectroscopic data, and then their relative configurations were deduced by STS GIAO ¹³C NMR calculation and NOE analysis. The absolute configurations of all the mentioned compounds were determined by the comparison of their experimental and calculated ECD curves.

NMR-Based Configurational Assignments of Natural Products: How Floating Chirality Distance Geometry Calculations Simplify Gambling with 2N-1 Diastereomers

Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the probability of configurational assignments to judge the quality of structural elucidations using Bayesian inference in combination with floating-chirality distance geometry simulations. Based on reference-free NOE/ROE data, residual dipolar couplings (RDCs), and residual quadrupolar couplings (RQCs) in various combinations, we demonstrate how the relative configurations of three natural compounds, namely, jatrohemiketal (1), artemisinin (2), and Taxol (3), can be unambiguously established without the necessity to carry out time-consuming DFT-based configurational and conformational analyses. Our results quantitatively describe how reliably molecular geometries can be inferred from experimental NMR data, thereby unequivocally unveiling remaining assignment ambiguities. The methodology presented here will dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data and DFT-based structure models in chemistry.

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.

Self-Assembled Oligopeptide (FK)4 as a Chiral Alignment Medium for the Anisotropic NMR Analysis of Organic Compounds

Anisotropic NMR parameters have been proven to be powerful for the structural elucidation of organic molecules. Herein, we present an alignment medium based on the self-assembled (FK)₄ oligopeptide, showing excellent properties in measurements of anisotropic NMR parameters in both D₂O and CD₃OD. The preparation of the (FK)₄-based alignment medium is simple and rapid. The low viscosity of the anisotropic phase makes it easy to be transferred to the NMR tube. The alignment of the oligopeptide is fast, stable, and homogeneous, with weak background signals, permitting the acquirement of high-quality NMR spectra. The performance of this alignment medium in residual dipolar coupling measurements and diastereomer discriminations is demonstrated by analyzing several different analytes. The enantiodiscrimination property of the (FK)₄ oligopeptide is revealed by the difference of residual chemical shift anisotropy of the two enantiomers in the 1D ¹³C spectrum, granting its potential use for the quantification and identification of enantiomers of small molecules.

MS/MS-based molecular networking accelerated discovery of germacrane-type sesquiterpene lactones from Elephantopus scaber L

Assisted by an MS/MS-based molecular networking guided strategy, six undescribed germacrane-type sesquiterpene lactones, namely scaberxones A-F, along with a known analog were obtained and characterized from Elephantopus scaber L. Their structures were unequivocally assigned by detailed spectroscopic analyses, NMR and ECD spectral calculations, and computer-assisted structure elucidation (CASE), complemented with single-crystal X-ray diffraction. All compounds were measured for their production of nitric oxide (NO) levels in lipopolysaccharide (LPS)-induced BV-2 microglial cells to assess their anti-neuroinflammatory activity. Scaberxone F showed the most potent inhibition of NO production at a concentration of 10 μM.

Bayesian Inference Applied to NMR-Based Configurational Assignments by Floating Chirality Distance Geometry Calculations

Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the quality and probability of structural elucidations using Bayesian inference in combination with floating chirality distance geometry simulations. Here, we will discuss the configurational analysis of three complex natural products including isopinocampheol (1), plakilactone H (2), and iodocallophycoic acid A (3) using NMR restraints of various types and in different combinations (residual dipolar couplings (RDCs) and NOE-derived distances). Our results quantitatively demonstrate how reliably molecular geometries can be inferred from experimental NMR data, unequivocally unveiling remaining assignment ambiguities. The methodology presented here can dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data in chemistry.

Revisiting the structure of Heliannuol L: A computational approach

Recently, structural elucidation of natural products has undergone a revolution. The combined use of different modern spectroscopic methods has allowed obtaining a complete structural assignment of natural products using small amounts of sample. However, despite the extraordinary ongoing advances in spectroscopy, the mischaracterization of natural products has been and remains a recurrent problem, especially when the substance presents several stereogenic centers. The misinterpretation of nuclear magnetic resonance (NMR) data has resulted in frequent reports addressing structural reassignment. In this context, a great effort has been devoted to developing quantum chemical calculations that simulate NMR parameters accurately, allowing to achieve a more precise spectral interpretation. In this work, we employed a protocol for theoretical calculations of ¹ H NMR chemical shifts and coupling constants using density functional theory (DFT), followed by the application of the DP4+ method to revisit the structure of Heliannuol L, a member of the Heliannuol class, isolated from Helianthus annuus. Our results indicate that the originally proposed structure of Heliannuol L needs a stereochemical reassignment, placing the hydroxyl bonded to C10 in the opposite side of the methyl and hydroxyl groups bonded to C7 and C8, respectively.

Chemoenzymatic approaches to plant natural product inspired compounds

Covering: 2003 up to the end of 2021Complex molecules produced by plants have provided us with a range of medicines, flavour and fragrance compounds and pesticides. However, there are challenges associated with accessing these in an economically viable manner, including low natural abundance and the requirement for complex multi-step synthetic strategies. Chemoenzymatic approaches provide a valuable alternative strategy by combining traditional synthetic methods with biocatalysis. This review highlights recent chemoenzymatic syntheses towards plant natural products and analogues, focusing on the advantages of incorporating biocatalysts into a synthetic strategy.

Recent Achievements in Total Synthesis for Integral Structural Revisions of Marine Natural Products

A great effort to discover new therapeutic ingredients is often initiated through the discovery of the existence of novel marine natural products. Since substances produced by the marine environment might be structurally more complex and unique than terrestrial natural products, there have been cases of misassignments of their structures despite the availability of modern spectroscopic and computational chemistry techniques. When it comes to refutation to erroneously or tentatively proposed structures empirical preparations through organic chemical synthesis has the greatest contribution along with close and sophiscated inspection of spectroscopic data. Herein, we analyzed the total synthetic studies that have decisively achieved in revelation of errors, ambiguities, or incompleteness of the isolated structures of marine natural products covering the period from 2018 to 2021.

A gold(i)-catalysed approach towards harmalidine an elusive alkaloid from Peganum harmala

Upon gold catalysis, the 2,3-dihydropyrrolo[1,2-a]indole motif, encountered in few but interesting bioactive natural products, was efficiently obtained from N-aryl 2-alkynylazetidine derivatives. In an attempt to apply this methodology to the synthesis of harmalidine, isolated from the seeds of Peganum harmala, advanced amino 2,3-hydropyrrolo[1,2-a]indol(one) derivatives were readily obtained in only 11 steps from but-3-yn-1-ol. While the reported structure of harmalidine could not be reached from these intermediates, a surprising 12-membered diimino dimer was isolated. Extensive comparison of the reported harmalidine NMR data to the experimental and calculated data of our synthetic molecules, harmaline or the synthetised N-methylharmaline show discrepancies with the proposed natural product structure.

Upon gold catalysis, the 2,3-dihydropyrrolo[1,2-a]indole motif, encountered in few but interesting bioactive natural products, was efficiently obtained from N-aryl 2-alkynylazetidine derivatives.

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.

Cross-Modal Retrieval between 13C NMR Spectra and Structures for Compound Identification Using Deep Contrastive Learning

Library matching using carbon-13 nuclear magnetic resonance (¹³C NMR) spectra has been a popular method adopted in compound identification systems. However, the usability of existing approaches has been restricted as enlarging a library containing both a chemical structure and spectrum is a costly and time-consuming process. Therefore, we propose a fundamentally different, novel approach to match ¹³C NMR spectra directly against a molecular structure library. We develop a cross-modal retrieval between spectrum and structure (CReSS) system using deep contrastive learning, which allows us to search a molecular structure library using the ¹³C NMR spectrum of a compound. In the test of searching 41,494 ¹³C NMR spectra against a reference structure library containing 10.4 million compounds, CReSS reached a recall@10 accuracy of 91.64% and a processing speed of 0.114 s per query spectrum. When further incorporating a filter with a molecular weight tolerance of 5 Da, CReSS achieved a new remarkable recall@10 of 98.39%. Furthermore, CReSS has potential in detecting scaffolds of novel structures and demonstrates great performance for the task of structural revision. CReSS is built and developed to bridge the gap between ¹³C NMR spectra and structures and could be generally applicable in compound identification.

Do Double-Hybrid Exchange-Correlation Functionals Provide Accurate Chemical Shifts? A Benchmark Assessment for Proton NMR

A benchmark density functional theory (DFT) study of ¹H NMR chemical shifts for data sets comprising 200 chemical shifts, including complex natural products, has been carried out to assess the performance of DFT methods. Two new benchmark data sets, NMRH33 and NMRH148, have been established. The meta-GGA revTPSS performs remarkably well against the NMRH33 benchmark set (mean absolute deviation (MAD), 0.10 ppm; maximum deviation (max), 0.26 ppm) with the smallest MAD of all evaluated functionals. The best-performing double-hybrid density functional (DHDF), revDSD-BLYP (MAD, 0.16 ppm; max, 0.35 ppm), performs similarly to hybrid-GGA methods (e.g., mPW1PW91/6-311G(d) (MAD, 0.15 ppm; max, 0.36 ppm)), but at a considerably higher computational cost. The results indicate that currently available double-hybrid DFT methods offer no benefit over GGA (including hybrid and meta) functionals in the calculation of ¹H NMR chemical shifts.

Structure Revision of Four Classes of Prenylated Aromatic Natural Products Based on a Rule for Diagnostic 13C NMR Chemical Shifts

Errors in elucidating the structures of four natural classes of prenylated aromatic compounds with 2,3-epoxy, 2,3-dihydroxy, and cyclization with an ortho-phenolic hydroxyl to give a pyran or furan ring moiety are frequent and inevitable. Based on rigorous literature research and a series of chemical transformation experiments, a rule for the rapid determination of these four classes of prenylated derivates based on ¹³C NMR data was formulated, and 57 corrections featuring these fragments were accordingly reported.

Litosetoenins A-E, Diterpenoids from the Soft Coral Litophyton setoensis, Backbone-Rearranged through Divergent Cyclization Achieved by Epoxide Reactivity Inversion

Litosetoenins A-E (1-5), five new ring-rearranged serrulatane-type diterpenoids with a common tricyclo[3.0.4]decane core, along with a known diterpenoid glycoside (6), a related known diterpenoid (7), and four known sesquiterpenoids (8-11), were isolated from a Balinese soft coral Litophyton setoensis. Spirolitosetoenin A (5a) and isospirolitosetoenin A (5b), featuring an unprecedented spiro[4,5]decane core, were obtained after treatment of compound 5 with HCl in methanol. The structures of new compounds were elucidated by extensive spectroscopic analysis, quantum mechanical nuclear magnetic resonance approach, and chemical methods. A plausible biosynthetic pathway involving an unusual divergent biogenesis was proposed.

A critical review on the use of DP4+ in the structural elucidation of natural products: the good, the bad and the ugly. A practical guide

Covering: 2015 up to the end of 2020Even in the golden age of NMR, the number of natural products being incorrectly assigned is becoming larger every day. The use of quantum NMR calculations coupled with sophisticated data analysis provides ideal complementary tools to facilitate the elucidation process in challenging cases. Among the current computational methodologies to perform this task, the DP4+ probability is a popular and widely used method. This updated version of Goodman's DP4 synergistically combines NMR calculations at higher levels of theory with the Bayesian analysis of both scaled and unscaled data. Since its publication in late 2015, the use of DP4+ to solve controversial natural products has substantially grown, with several predictions being confirmed by total synthesis. To date, the structures of more than 200 natural products were determined with the aid of DP4+. However, all that glitters is not gold. Besides its intrinsic limitations, on many occasions it has been improperly used with potentially important consequences on the quality of the assignment. Herein we present a critical revision on how the scientific community has been using DP4+, exploring the strengths of the method and how to obtain optimal results from it. We also analyze the weaknesses of DP4+, and the paths to by-pass them to maximize the confidence in the structural elucidation.

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.