NMR Data Sets Research Articles

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Discovery of olimycin E from Streptomyces sp. 11695.

A new natural product olimycin E (1), together with two known compounds of divergolide R (2) and olimycin B (3), were obtained from the marine-derived Streptomyces sp. 11695. The structures of 1-3 were established on the basis of HRESIMS as well as 1D and 2D NMR datasets. The absolute configuration of 1 is identified as 4 R, 6S, 7S, 10 R by comparison the experiment ECD with that of the theoretical ECD. Antibacterial results showed that compound 2 have antibacterial activities against Staphylococcus aureus and MRSA with the MIC values of 32 μg/mL, respectively.

Brewing alcohol 101: An undergraduate experiment utilizing benchtop NMR for quantification and process monitoring.

In recent years there has been a renewed interest in benchtop NMR. Given their lower cost of ownership, smaller footprint, and ease of use, they are especially suited as an educational tool. Here, a new experiment targeted at upper-year undergraduates and first-year graduate students follows the conversion of D-glucose into ethanol at low-field. First, high and low-field data on D-glucose are compared and students learn both the Hz and ppm scales and how J-coupling is field-independent. The students then acquire their own quantitative NMR datasets and perform the quantification using an Electronic Reference To Access In Vivo Concentration (ERETIC) technique. To our knowledge ERETIC is not currently taught at the undergraduate level, but has an advantage in that internal standards are not required; ideal for following processes or with future use in flow-based benchtop monitoring. Using this quantitative data, students can relate a simple chemical process (fermentation) back to more complex topics such as reaction kinetics, bridging the gaps between analytical and physical chemistry. When asked to reflect on the experiment, students had an overwhelmingly positive experience, citing agreement with learning objectives, ease of understanding the protocol, and enjoyment. Each of the respondents recommended this experiment as a learning tool for others. This experiment has been outlined for other instructors to utilize in their own courses across institutions, with the hope that a continued expansion of low-field NMR will increase accessibility and learning opportunities at the undergraduate level.

The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis

Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2–4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.

Matrix decomposition methods for accurate water saturation prediction in Canadian oil-sands by LF-NMR T2 measurements

This article presents a novel method for quantifying water saturation in oil-sand reservoirs by employing 1D low-field nuclear magnetic resonance (LF-NMR) spin-spin relaxation and bulk density measurements as indicators of pore volume variations. One of the challenges in accurately determining the volumes of bitumen and water in oil-sands is the effective separation of their overlapping T2 signals, attributed to similar spin-spin relaxation decay times and diffusive coupling. Conventional methods require deconvolution of T2 peaks and or experimentation to determine T2 cutoff values, differentiating between bitumen and water signals, notably capillary and clay-bound water. In contrast, our approach predicts the proportion of water by utilizing matrix decomposition methods to compress the T2 relaxation distribution and extract significant components. These components subsequently train the regression model, facilitating the accurate estimation of relative water saturation percentages.The NMR dataset was obtained by benchtop LF-NMR T2 measurements from 82 oil-sand samples, with preserved bitumen and water saturations at both reservoir and ambient temperatures (6 °C and 25 °C), yielding 164 observations. We examined four matrix decomposition methods, including principal component analysis, its variation integrating a kernel function, canonical correlation analysis, and partial least squares regression. X-ray CT measurements and Dean-Stark extraction ascertained the respective sample bulk densities and fluid-solid volume proportions.The PCA model prediction statistics (RMSE = 0.86%, R2 = 0.84), indicate its application can be extended for saturation prediction from NMR and bulk density well logs. Moreover, we underscore the importance of incorporating bulk density measurements and establish the statistical and physical correlations between these measurements and NMR T2 relaxation, providing insights into the approach's efficacy and causality.

Chemical profiling of botanical extracts obtained in NADES systems using centrifugal partition chromatography combined with 13 C NMR dereplication-Hypericum perforatum as a case study.

Natural deep eutectic solvents (NADES) have emerged as interesting extractants to develop botanical ingredients. They are nontoxic and biodegradable, nonflammable, easy to prepare, and able to solubilize a wide range of molecules. However, NADES extracts remain difficult to analyze because the metabolites of interest stay highly diluted in the nonvolatile viscous NADES matrix. This study presents a robust analytical workflow for the chemical profiling of NADES extracts. It is applied to Hypericum perforatum aerial parts extracted with the neutral mixture fructose/glycerol/water (3/1/1, w/w/w), and compared to the chemical profiling of a classical dry methanol extract. Exploiting polarity differences between metabolites, the H. perforatum NADES extract was partitioned in a liquid-liquid solvent system to trap the hydrophilic NADES constituents in the lower phase. The upper phase, containing a diversity of secondary metabolites from H. perforatum, was fractionated by centrifugal partition chromatography. All fractions were chemically investigated using a 13 C NMR dereplication method which involves hierarchical clustering analysis of the whole NMR dataset, a natural metabolite database for metabolite identification, and 2D NMR analyses for validation. Liquid chromatography-mass spectrometry (LC-MS) analyses were also performed to complete the identification process. A range of 21 metabolites were unambiguously identified, including glycosylated flavonols, lactones, catechins, phenolic acids, lipids, and simple sugars, and 15 additional minor extract constituents were annotated by LC-MS based on exact mass measurements. The proposed identification process is rapid and nondestructive and provides good prospects to deeply characterize botanical extracts obtained in nonvolatile and viscous NADES systems.

Synergistic Combination of NAPROC-13 and NMR 13C DFT Calculations: A Powerful Approach for Revising the Structure of Natural Products.

This article describes the structure revision of nine triterpenoids that have been reported corresponding to the same 13C NMR data set. In addition, 13C NMR calculation shows that some chemical shift assignments must be swapped. Our analysis improves the fit between the experimental and calculated data. Correcting misassigned structures and correctly assigning each signal is essential for elucidating new structurally related compounds. Furthermore, the ambiguity of several compounds, the structure of which differs in the literature and the Sci-Finder database, has been eliminated. Misassigned structures were found by chemical shift searches in NAPROC-13, and the results provide two or more different compounds with the same 13C NMR data. The process to determine the correct, most likely structural proposal in agreement with the experimental 13C NMR data was carried out by DFT calculations.

Rapid Chemical Profiling of Filipendula ulmaria Using CPC Fractionation, 2-D Mapping of 13C NMR Data, and High-Resolution LC-MS.

Filipendula ulmaria, commonly known as meadowsweet, is a wild herbaceous flowering plant that is widely distributed in Europe. A range of salicylic acid derivatives and flavonol glycosides have been previously associated with the antirheumatic and diuretic properties of F. ulmaria. In the present work, a hydroalcoholic extract from F. ulmaria aerial parts was extensively profiled using an efficient NMR-based dereplication strategy. The approach involves the fractionation of the crude extract by centrifugal partition chromatography (CPC), 13C NMR analysis of the fractions, 2D-cluster mapping of the entire NMR dataset, and, finally, structure elucidation using a natural metabolite database, validated by 2D NMR data interpretation and liquid chromatography coupled with mass spectrometry. The chemodiversity of the aerial parts was extensive, with 28 compounds unambiguously identified, spanning various biosynthetic classes. The F. ulmaria extract and CPC fractions were screened for their potential to enhance skin epidermal barrier function and skin renewal properties using in vitro assays performed on Normal Human Epidermal Keratinocytes. Fractions containing quercetin, kaempferol glycosides, ursolic acid, pomolic acid, naringenin, β-sitosterol, and Tellimagrandins I and II were found to upregulate genes related to skin barrier function, epidermal renewal, and stress responses. This research is significant as it could provide a natural solution for improving hydration and skin renewal properties.

Hosimosines A-E, structurally diverse cytisine derivatives from the seeds of Ormosia hosiei Hemsl. et Wils

Ormosia hosiei Hemsl. et Wils (Fabaceae family) is an arbor species endemic to China. The seeds of O. hosiei have been used as traditional Chinese medicine to treat hernia, abdominal pain, blood stasis and amenorrhea. Cytisine-like and angustifoline type alkaloids were main components identified from this plant. In our research on the bioactive alkaloids from the promising Chinese medicinal plants, four new angustifoline type alkaloids (1–4) and a new cytisine-like alkaloid (5), named hosimosine A-E, together with 13 known analogues (6–18) were isolated from the seeds of O. hosiei. Their structures were elucidated by the extensive spectroscopic methods, especially the interpretation of NMR spectra and specific rotations, along with the methods of NMR and ECD calculation. Compounds 1–4 were identified as two pairs of epimers, whose relative configurations were deduced from density functional theory (DFT) calculations of NMR chemical shifts and DP4+ analysis, and absolute configurations were determined by comparison of their experimental and theoretical ECD spectra. Compound 5 displayed two sets of NMR data caused by the existence of tautomeric forms. Compounds 14, 17 and 18 were determined to be enantiomers of literature compounds. Some of the isolates exhibited moderate cytotoxic effects against HepG2, A2780 and MCF-7 cells.

Integrated NMR/Molecular Dynamics Determination of the Ensemble Conformation of a Thermodynamically Stable CUUG RNA Tetraloop.

Both experimental and theoretical structure determinations of RNAs have remained challenging due to the intrinsic dynamics of RNAs. We report here an integrated nuclear magnetic resonance/molecular dynamics (NMR/MD) structure determination approach to describe the dynamic structure of the CUUG tetraloop. We show that the tetraloop undergoes substantial dynamics, leading to averaging of the experimental data. These dynamics are particularly linked to the temperature-dependent presence of a hydrogen bond within the tetraloop. Interpreting the NMR data by a single structure represents the low-temperature structure well but fails to capture all conformational states occurring at a higher temperature. We integrate MD simulations, starting from structures of CUUG tetraloops within the Protein Data Bank, with an extensive set of NMR data, and provide a structural ensemble that describes the dynamic nature of the tetraloop and the experimental NMR data well. We thus show that one of the most stable and frequently found RNA tetraloops displays substantial dynamics, warranting such an integrated structural approach.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such “blind” targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70–108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Study of coffee sensory attributes by ordered predictors selection applied to 1H NMR spectroscopy

The determination of sensory attributes of coffee samples through trained cupping tasters is one of the main methodologies for coffee quality control. However, this analysis may suffer some distortions due to different ways of interpreting quality from the tasters' perspective. Thus, other, and complementary have been developed as alternative ways to determine sensory attributes. Hydrogen nuclear magnetic resonance (1H NMR) is a technique that allows the chemical study of compounds present in organic matrices. Through 1H NMR it is possible to predict a sensory attribute using chemometric methods. However, 1H NMR generates many variables per sample, making it necessary to use variable selection methods to find the most useful information. Ordered predictors selection (OPS) is a variable selection method developed to search, sort, and select the variables in order of importance. In this study, six sensory attributes from 42 coffee samples were predicted by partial least squares (PLS) and support vector regression (SVR) methods and OPS as variable selection in 1H NMR data set. The integration of areas of the 1H NMR spectra and the full data sets were also used to compare the prediction models. OPS-PLS model was the best strategy to predict aftertaste, balance, and fragrance with root mean squared error of prediction (RMSEP) of 0.24, 0.35, and 0.20, respectively. The coefficients of determination for prediction (R2p) of these models were 0.71, 0.60, and 0.77, respectively. OPS-SVR predominated as the best strategy to predict acidity, body, and flavor with RMSEP of 0.23, 0.21, and 0.22, and R2p of 0.89, 0.75, and 0.83, respectively. None of the models chosen as the best showed the presence of trends in residuals above 5% of significance. Variable selection by OPS can improve the prediction of regression models.

Determining sequential micellization steps of bile salts with multi-CMC modeling

HypothesisBile salts exhibit complex concentration-dependent micellization in aqueous solution, rooted in a long-standing hypothesis of increasing size in bile aggregation that has historically focused on the measurement of only one CMC detected by a given method, without resolving successive stepwise aggregates. Whether bile aggregation is continuous or discrete, at what concentration does the first aggregate form, and how many aggregation steps occur, all remain as open questions. ExperimentsBile salt critical micelle concentrations (CMCs) were investigated with NMR chemical shift titrations and a multi-CMC phase separation modeling approach developed herein. The proposed strategy is to establish a correspondence of the phase separation and mass action models to treat the first CMC; subsequent micellization steps, involving larger micelles, are then treated as phase separation events. FindingsThe NMR data and the proposed multi-CMC model reveal and resolve multiple closely spaced sequential preliminary, primary, and secondary discrete CMCs in dihydroxy and trihydroxy bile salt systems in basic (pH 12) solutions with a single model of one NMR data set. Complex NMR data are closely explained by the model. Four CMCs are established in deoxycholate below 100 mM (298 K, pH 12): 3.8 ± 0.5 mM, 9.1 ± 0.3 mM, 27 ± 2 mM, and 57 ± 4 mM, while three CMCs were observed in multiple bile systems, also under basic conditions. Global fitting leverages the sensitivity of different protons to different aggregation stages. In resolving these closely spaced CMCs, the method also obtains chemical shifts of these spectroscopically inaccessible (aka dark) states of the distinct micelles.

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.

Modelling solution structures of cyclic peptides - How good are we?

Cyclic peptides (CPs) are macrocycles made from amino acids that can bind to classically undruggable drug targets. Controlling pharmacokinetic properties of these macrocyclic peptides is an open question, limited by having precise knowledge of the underlying solution structures. CP solution dynamics are complex: individual structures are often separated by large energy barriers; the presence of conformers is environment dependent and solution conformers can be different from bioactive structures. Specialised simulation methods for CPs have previously been designed to reproduce available x-ray structures, but it is often unknown how well solution structures are reproduced. Here, we report the largest cyclic peptide solution NMR dataset “MacroConf”, which we built from existing literature. We use a range of molecular dynamics methods and specialised cheminformatics methods to model the solution structures of over 30 cyclic peptides from this dataset. We explore (a) how different methods perform relative to one another and to the experimental reference structures and (b) whether computationally cheaper methods can reach the accuracy of gold-standard accelerated molecular dynamics simulations to allow for fast prediction of solution ensembles. By providing automated computational workflows, this work is easily extendable, fully reproducible, and is built as a reference point to guide development of future conformer generators for cyclic peptides.

DD-ComDim: A data-driven multiblock approach for one-class classifiers

An attractive alternative to solve authentication challenges is the combination of fingerprint analytical techniques with multivariate methods of one-class classifiers (OCC). In this proof of concept, we propose a novel multiblock method for OCC that emerged from the association of the common dimension (ComDim) analysis with the dual data-driven, which allows to calculate errors of misclassification based on the orthogonal and score distances with a subsequent determination of their cut-off levels. The applicability of data-driven – common dimension (DD-ComDim) analysis was verified for the authentication of diesel S10 (10 ppm of sulfur) against S500 (500 ppm of sulfur) using two low-field 1H NMR datasets: medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR). The performance of the DD-ComDim (mid-level data fusion) was compared with data-driven – soft independent modeling of class analogy (DD-SIMCA) for each data set separately and concatenated (low-level data fusion). The results suggest that this novel method can improve the quality and efficiency of the model in authentication when compared to a traditional method. In addition, it demonstrated that the threshold of separation between target and non-target classes was more evident with the mid-level data fusion approach. Other applications are currently underway, intending to verify the method's applicability with other matrices and analytical techniques to confirm the reliability of the DD-ComDim.

Antimicrobial Polyketides from the Marine-Derived Fungus Spiromastix sp. SCSIO F190.

Three diphenyl ethers (1-3) and a cyclopentenone (4), together with seven known compounds (5-11), were isolated from the fermentation broth of the marine sediment-derived fungus Spiromastix sp. SCSIO F190. Compounds 3 and 4 were found to exist as a pair of atropisomers (3a, 3b) and racemates (4a, 4b), respectively. The planar structures of compounds 1-4 were elucidated on the basis of NMR and HRESIMS data sets. The absolute configurations of 2 and 3 were determined by spectroscopic and single-crystal X-ray diffraction analyses, whereas the configuration of 4 was determined by spectroscopic and chiral analyses. All compounds, except for 4 and 11, displayed activities against various pathogenic bacteria. Notably, compounds 1-4, especially 1, exhibited strong activity against Gram-positive bacteria, including methicillin-resistant bacterial strains of Staphylococcus aureus (MRSA), Enterococcus faecalis ATCC 29212, and Bacillus subtilis BS01, with MIC values ranging from 0.5 to 4 μg/mL. Moreover, the structure-activity relationship analyses of the active compounds and their analogues revealed the critical structural features correlating to the observed antimicrobial activities, herein providing insights for antimicrobial drug development.

Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling.

The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.

Modular Pulse Program Generation for NMR Supersequences.

NMR supersequences allow multiple 2D NMR data sets to be acquired in greatly reduced experiment durations through tailored detection of NMR responses within concatenated modules. In NOAH (NMR by Ordered Acquisition using 1H detection) experiments, up to five modules can be combined (or even more when parallel modules are employed), which in theory leads to thousands of plausible supersequences. However, constructing a pulse program for a supersequence is highly time-consuming, requires specialized knowledge, and is error-prone due to its complexity; this has prevented the true potential of the NOAH concept from being fully realized. We introduce here an online tool named GENESIS (GENEration of Supersequences In Silico), available via https://nmr-genesis.co.uk, which systematically generates pulse programs for arbitrary NOAH supersequences compatible with Bruker spectrometers. The GENESIS website provides a unified “one-stop” interface where users may obtain customized supersequences for specific applications, together with all associated acquisition and processing scripts, as well as detailed instructions for running NOAH experiments. Furthermore, it enables the rapid dissemination of new developments in NOAH sequences, such as new modules or improvements to existing modules. Here, we present several such enhancements, including options for solvent suppression, new modules based on pure shift NMR, and improved artifact reduction in HMBC and HMQC modules.

Metabolomic Profiling of Malaysian and New Zealand Honey Using Concatenated NMR and HRMS Datasets.

This study aims to compare the metabolomic profiles of Malaysian and New Zealand honey while determining their anti-oncogenic activity for potential prophylactic functions. Metabolomics tools including multivariate analysis were applied on concatenated LC-HRMS and NMR datasets to afford an intensive chemical profile of honey samples and have a snapshot of the bioactive metabolites in the respective collections. Malaysian samples were found to have higher sugar and polyphenolic content, while New Zealand samples afforded higher concentration of low molecular weight (MW) lipids. However, New Zealand honey collected from the northern islands had higher concentration of acetylated saccharides, while those from the southern islands yielded higher low MW phenolic metabolites that were comparable to Malaysian honey. Mild anti-oncogenic compounds against breast cancer cell line ZR75 were putatively identified in Malaysian honey that included earlier described antioxidants such as gingerdiol, 2-hexylphenol-O-β-D-xylopyranoside, plastoquinone, tropine isovalerate, plumerinine, and 3,5-(12-phenyl-8-dodecenyl)resorcinol, along with several phenolic esters and lignans.