Nuclear Magnetic Resonance Chemical Shifts Research Articles

NMR chemical shift of confined 129Xe: coordination number, paramagnetic channels and molecular dynamics in a cryptophane-A biosensor.

Advances in hyperpolarisation and indirect detection have enabled the development of xenon nuclear magnetic resonance (NMR) biosensors (XBSs) for molecule-selective sensing in down to picomolar concentration. Cryptophanes (Crs) are popular cages for hosting the Xe "spy". Understanding the microscopic host-guest chemistry has remained a challenge in the XBS field. While early NMR computations of XBSs did not consider the important effects of host dynamics and explicit solvent, here we model the motionally averaged, relativistic NMR chemical shift (CS) of free Xe, Xe in a prototypic CrA cage and Xe in a water-soluble CrA derivative, each in an explicit H2O solvent, over system configurations generated at three different levels of molecular dynamics (MD) simulations. We confirm the "contact-type" character of the Xe CS, arising from the increased availability of paramagnetic channels, magnetic couplings between occupied and virtual orbitals through the short-ranged orbital hyperfine operator, when neighbouring atoms are in contact with Xe. Remarkably, the Xe CS in the present, highly dynamic and conformationally flexible situations is found to depend linearly on the coordination number of the Xe atom. We interpret the high- and low-CS situations in terms of the magnetic absorption spectrum and choose our preference among the used MD methods based on comparison with the experimental CS. We check the role of spin-orbit coupling by comparing with fully relativistic CS calculations. The study outlines the computational workflow required to realistically model the CS of Xe confined in dynamic cavity structures under experimental conditions, and contributes to microscopic understanding of XBSs.

Synthesis, Spectroscopic, Quantum Chemical, and Molecular Docking Studies of 2-Cyano-N-Cyclopropylacetamide and 2-Cyano-N-(1-Phenylethyl)Acetamide

2-Cyano-N-cyclopropylacetamide (2CCPA) and 2-cyano-N-(1-phenylethyl) acetamide (2CPEA) compounds were synthesized and characterized by FT-IR, FT-Raman, UV-Vis, 1H NMR, and 13C NMR spectrum analyses. Density functional theory (DFT) B3LYP and HF techniques with 6-31++G(d,p) and 6-311++G(d,p) basis sets were used to determine the optimal molecular geometry, vibrational wavenumbers, infrared intensities, and Raman scattering activities. To get better results for the computed data, the vibrational wavenumbers are additionally adjusted with a scaling factor. 2CCPA and 2CPEA were investigated for their MEP, NBO analysis, NLO behavior, and thermodynamic characteristics. To determine the chemically active locations, the MEP and ELF have been illustrated. Fukui functions have shown the electrophilic and nucleophilic areas. The (GIAO) technique was used to compute the molecule 1H and 13C Nuclear Magnetic Resonance (NMR) chemical shifts. UV-Vis and HOMO-LUMO studies, which characterize charge transfer between atoms in a molecule, have been used to elucidate the electronic characteristics of 2CCPA and 2CPEA molecules. The minimal binding energy was obtained using 2D and 3D molecular docking calculations on 2CCPA and 2CPEA to examine hydrogen bond interactions. Molecular docking methods were employed with 2CCPA and 2CPEA molecules with the different protein receptors (1H23, 1QXK, 1ZXM, 3PTA, 4JS9, 2C5Y, ABW, 3RDE, 6GU7, and 6J7A) t and -o find the best of two molecules for drug identification and binding energy was found to be −5.8 and −7.5 kcal/mol. A molecular dynamics simulation was used to explore biomolecular stability.

Seven diterpenoids from the resin of Pinus yunnanensis Franch and their anti-inflammatory activity

Phytochemical investigation of the 95% ethanol extract from Pinus yunnanensis Franch resin induced the isolation of six previously unreported diterpenoids pinuyunnanacids K − N, P − Q, a nor-diterpenoid with a novel skeleton pinuyunnanacid O and six known analogues. Their structures were elucidated by spectroscopic analysis and computational methods, including nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, calculated NMR chemical shifts method and electronic circular dichroic (ECD) spectra. All the compounds were analyzed for anti-inflammatory activity through western blotting and cell viability, compounds 2, 10 and 12 significantly downregulated the protein expression of iNOS at the concentration of 40 μM. At the same time, compounds 10 and 12 decreased the expression of COX-2 in LPS-treated RAW264.7 (leukemia cells in mouse macrophage) cells at the concentration of 40 μM.

A Fast, Low-Cost and Simple Method for Predicting Atomic/Inter-Atomic Properties by Combining a Low Dimensional Deep Learning Model with a Fragment Based Graph Convolutional Network

Calculations with high accuracy for atomic and inter-atomic properties, such as nuclear magnetic resonance (NMR) spectroscopy and bond dissociation energies (BDEs) are valuable for pharmaceutical molecule structural analysis, drug exploration, and screening. It is important that these calculations should include relativistic effects, which are computationally expensive to treat. Non-relativistic calculations are less expensive but their results are less accurate. In this study, we present a computational framework for predicting atomic and inter-atomic properties by using machine-learning in a non-relativistic but accurate and computationally inexpensive framework. The accurate atomic and inter-atomic properties are obtained with a low dimensional deep neural network (DNN) embedded in a fragment-based graph convolutional neural network (F-GCN). The F-GCN acts as an atomic fingerprint generator that converts the atomistic local environments into data for the DNN, which improves the learning ability, resulting in accurate results as compared to experiments. Using this framework, the 13C/1H NMR chemical shifts of Nevirapine and phenol O–H BDEs are predicted to be in good agreement with experimental measurement.

Reference interaction site model self-consistent field with constrained spatial electron density approach for nuclear magnetic shielding in solution.

We propose a new hybrid approach combining quantum chemistry and statistical mechanics of liquids for calculating the nuclear magnetic resonance (NMR) chemical shifts of solvated molecules. Based on the reference interaction site model self-consistent field with constrained spatial electron density distribution (RISM-SCF-cSED) method, the electronic structure of molecules in solution is obtained, and the expression for the nuclear magnetic shielding tensor is derived as the second-order derivative of the Helmholtz energy of the solution system. We implemented a method for calculating chemical shifts and applied it to an adenine molecule in water, where hydrogen bonding plays a crucial role in electronic and solvation structures. We also performed the calculations of 17O chemical shifts, which showed remarkable solvent dependence. While converged results could not be sometimes obtained using the conventional method, in the present framework with RISM-SCF-cSED, an adequate representation of electron density is guaranteed, making it possible to obtain an NMR shielding constant stably. This introduction of cSED is key to extending the method's applicability to obtain the chemical shift of various chemical species. The present demonstration illustrates our approach's superiority in terms of numerical robustness and accuracy.

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.

Two Binding Sites of SARS-CoV-2 Macrodomain 3 Probed by Oxaprozin and Meclomen.

Severe acute respiratory syndrome-coronavirus-1/2 (SARS-CoV-1/2) macrodomain 3 (Mac3) is critical for replication and transcription of the viral genome and is therefore a potential therapeutic target. Here, we solved the crystal structure of SARS-CoV-2 Mac3, which reveals a small-molecule binding pocket. Two low-molecular-weight drugs, oxaprozin and meclomen, induced different patterns of nuclear magnetic resonance (NMR) chemical shift perturbations (CSPs). Meclomen binds to site I of SARS-CoV-2 Mac3 with binding pose determined by NMR CSP and transferred paramagnetic relaxation enhancement, while oxaprozin binds to site II as revealed by the crystal structure. Interestingly, oxaprozin and meclomen both perturb residues in site I of SARS-CoV Mac3. Fluorescence polarization experiments further demonstrated that oxaprozin and meclomen inhibited the binding of DNA-G4s to SARS-CoV-2 Mac3. Our work identified two adjacent ligand-binding sites of SARS-CoV-2 Mac3 that shall facilitate structure-guided fragment linking of these compounds for more potent inhibitors.

Easy Structural Dereplication of Natural Products by Means of Predicted Carbon‐13 Nuclear Magnetic Resonance Spectroscopy Data**

AbstractThe present article reports the creation and usage of a general natural product database for the structural dereplication of natural products. This database, acd_lotusv7, derives from the LOTUS natural products database as the sole source of chemical structures. Database construction also relies on the commercial “ACD/C+H Predictors and DB” software for the prediction of the carbon‐13 nuclear magnetic resonance (NMR) spectral data associated with structures. The linkage of each natural compound with a Wikidata resource identifier already present in LOTUS accelerates the access to the primary literature data such as biologic origin and bibliographic references. The open source nmrshiftdb2 web interface and search engine provide a simple and free way to retrieve compound structures stored in acd_lotusv7 from carbon‐13 data and to analyze search results. Dereplication is illustrated by the easy and free retrieval of the structure of three natural compounds of low, medium, and high complexity from published lists of carbon‐13 NMR chemical shifts.

On the structure of cetylpyridinium perchlorate: A combined XRD, NMR, IR and DFT study

Cetylpyridinium perchlorate has been synthesized and characterized by using the differential thermal analysis (DTA), X-ray powder diffraction (XRD) techniques, Fourier transform infrared spectroscopy (FT-IR), and 1H and 13C nuclear magnetic resonance (NMR) spectroscopy. The investigated salt melts congruently at 100 °C. The DTA and XRD results indicate that the cetylpyridinium perchlorate samples synthesized at different temperatures differ in crystallinity. It has been established that the crystallinity degree of the samples affects the decomposition temperature. The highest decomposition temperature (Td = 265 °C) is observed for the sample crystallized at 20 °C. Cetylpyridinium perchlorate crystallizes in the monoclinic crystal system with the cell parameters a = 20.31 Å, b = 16.20 Å, c = 7.25 Å, β = 95.26°, Z = 4.According to the DFT calculations, the interionic interactions in the structure are characterized by electron transfer from the perchlorate anion to the cetylpyridinium cation. This explains a noticeable difference in the 1H NMR chemical shifts of the hydrogen atoms in the ortho-position (∼0.2 ppm) and the α-methylene group (∼0.1 ppm) of the DMSO solutions of the cetylpyridinium perchlorate and chloride salts micelles. The electronic structure analysis of cetylpyridinium perchlorate and cetylpyridinium chloride in terms of the “quantum theory of atoms-in-molecules” and the analysis of non-covalent interactions with the “reduced density gradient” method have revealed the presence of strong interactions between the ions in the DMSO solution.

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids.

Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

MISPR: an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

Fluorine Atoms on C6H5-Corrole Affect the Interaction with Mpro and PLpro Proteases of SARS-CoV-2: Molecular Docking and 2D-QSAR Approaches.

The chymotrypsin-like cysteine protease (3CLpro, also known as main protease—Mpro) and papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been used as the main targets for screening potential synthetic inhibitors for posterior in vitro evaluation of the most promising compounds. In this sense, the present work reports for the first time the evaluation of the interaction between Mpro/PLpro with a series of 17 porphyrin analogues-corrole (C1), meso-aryl-corrole (C2), and 15 fluorinated-meso-aryl-corrole derivatives (C3–C17) via molecular docking calculations. The impact of fluorine atoms on meso-aryl-corrole structure was also evaluated in terms of binding affinity and physical-chemical properties by two-dimensional quantitative structure–activity relationship (2D-QSAR). The presence of phenyl moieties increased the binding capacity of corrole for both proteases and depending on the position of fluorine atoms might impact positively or negatively the binding capacity. For Mpro the para-fluorine atoms might decrease drastically the binding capacity, while for PLpro there was a certain increase in the binding affinity of fluorinated-corroles with the increase of fluorine atoms into meso-aryl-corrole structure mainly from tri-fluorinated insertions. The 2D-QSAR models indicated two separated regions of higher and lower affinity for Mpro:C1–C17 based on dual electronic parameters (σI and σR), as well as one model was obtained with a correlation between the docking score value of Mpro:C2–C17 and the corresponding 13C nuclear magnetic resonance (NMR) chemical shifts of the sp2 carbon atoms (δC-1 and δC-2) of C2–C17. Overall, the fluorinated-meso-aryl-corrole derivatives showed favorable in silico parameters as potential synthetic compounds for future in vitro assays on the inhibition of SARS-CoV-2 replication.

Spin state of two mononuclear iron(II) complexes of a tridentate bis(imino)pyridine N-donor ligand: Experimental and theoretical investigations

Investigations on the spin states of octahedral Fe(II) complexes have received special attention due to their clear discrimination in the spin states of the d-orbitals. As a means to further understand the factors that influence the spin-crossover (SCO) phenomenon in Fe(II) systems, we herein report two mononuclear Fe(II) complexes, [FeL2](ClO4)2·2CH3OH (1) and [FeL2](BF4)2·CH3CN·CH3OH (2), derived from a novel N3-donor Schiff base ligand, 2,6-bis[(3-methylbenzylimino)methyl]pyridine (L) with varying counteranion and the diamagnetic [ZnL2](BF4)2 congener for a comparative investigation. The complexes have been synthesized and characterized by electrospray-ionization mass spectrometry (ESI-MS), Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR) spectroscopy, single-crystal X-ray diffraction (XRD) and magnetic susceptibility studies. Structural and magnetic investigations reveal that both 1 and 2 show Fe__N6 distorted octahedral geometry and are locked in the diamagnetic LS state throughout the entire explored temperature range from 1.8 to 400 K. The LS state of [FeL2]2+ is also confirmed by comparing the experimentally found structural parameters, NMR chemical shifts and excitation energies in the visible region with density functional theory (DFT) calculations.

Computation of NMR shieldings at the CASSCF level using gauge-including atomic orbitals and Cholesky decomposition.

We present an implementation of coupled-perturbed complete active space self-consistent field (CP-CASSCF) theory for the computation of nuclear magnetic resonance chemical shifts using gauge-including atomic orbitals and Cholesky decomposed two-electron integrals. The CP-CASSCF equations are solved using a direct algorithm where the magnetic Hessian matrix-vector product is expressed in terms of one-index transformed quantities. Numerical tests on systems with up to about 1300 basis functions provide information regarding both the computational efficiency and limitations of our implementation.

Spectroscopic investigation (IR and NMR) and HOMO-LUMO analysis of aromatic imines using theoretical approach

In this work, the theoretical studies on the structure, FT-IR, NMR, and UV–Vis spectroscopy of (E)-N-benzylidenebenzenamine (A1) and (E)-N-(2, 4′-dichlorobenzylidene) propan-1-amine (A2) are presented. The optimized structure of the molecules, NMR and UV–Vis spectra analysis were determined by the Density Functional Theory (DFT) method using B3LYP/6-311G (d, p) basis set. For FT-IR analysis, both the HF and DFT methods were used in order to determine their accuracy and reliability in theoretical calculations. The computed result of DFT calculations in comparison with the experimental results showed that the DFT method gives a more accurate prediction. The infrared (IR) spectra for the imine molecules have been recorded in the region of 500–4000 cm−1. The gauge-independent atomic orbital (GIAO) method has been used to evaluate the 13C and 1H nuclear magnetic resonance (NMR) chemical shifts of the molecules. The computed results of NMR spectra of the molecules was found to be in good agreement with the experimental data. The UV–Vis spectra of the molecules were computed to determine the HOMO-LUMO energies in order to gain insight into their electronic properties. Mulliken population analysis on atomic charges of the molecules was also calculated using the HF (Hartree-Fock) and B3LYP method. All the computed results indicated that the B3LYP method provides satisfactory results and, therefore, can be employed to support experimental data. It also demonstrated a reliable approach towards characterization of molecules in chemical science.

Correlation of the Isotropic NMR Chemical Shift with Oxygen Coordination Distances in Periodic Solids

In Nuclear Magnetic Resonance (NMR) spectroscopy, the isotropic chemical shift δiso is a measure of the electron density around the observed nuclide. For characterization of solid materials and compounds, it is desirable to find correlations between δiso and structural parameters such as coordination numbers and distances to neighboring atoms. Correlations of good quality are easier to find when the coordination sphere is formed by only one element, as the electron density is obviously strongly dependent on the atomic number. The current study is therefore restricted to nuclides in pure oxygen coordination. It is shown that the isotropic shift δiso correlates well with the average oxygen distances (as defined by the coordination sphere) for the nuclides 23Na (with spin I=3/2), 27Al (I=5/2), and 43Ca (I=7/2), using literature data for a range of periodic solids. It has been previously suggested for 207Pb (I=1/2) that δiso may alternatively be related to the shortest oxygen distance in the structure, and our study corroborates this also for the nuclides considered here. While the correlation with the minimal distance is not always better, it has the advantage of being uniquely defined. In contrast, the average distance is strongly dependent on the designation of the oxygen coordination sphere, which may be contentious in some crystal structures.

NMR and biochemical characterization of the interaction between FGFR1 juxtamembrane domain and phospholipids

Fibroblast growth factor receptors (FGFRs) play an important role in the regulation of cell proliferation, migration and differentiation, while the juxtamembrane domain (JMD) of FGFRs is the key in mediating these transmembrane signal transduction processes. Here, we expressed and purified the JMD (398K-470R) of FGFR1 with the presence of transmembrane domain (377I-397Y). The results from nuclear magnetic resonance (NMR) chemical shift analysis demonstrate that the main structure of JMD is disordered. Yet, the N-terminus of JMD was observed to form a short α-helix upon introducing negatively charged lipid 1,2-dioleoyl-sn-glycero-3-phospho-L-serine (DOPS) into its membrane mimic bicelles. Moreover, the N-terminus of JMD interacts with FRS2α, which is a substrate 2α of FGFR. Hence, we propose a model that FGFR1-JMD may interact with FRS2α and negatively charged lipids competitively. Our study provides a new understanding on the role of the JMD of FGFRs.

Pascoite Minerals and Potential Application of NMR Spectroscopy

The 20 minerals encompassing the pascoite family of decavanadate isopolyanion-containing [V10O28]6− minerals include a few minerals, such as rakovanite, that have been described as containing a protonated decavanadate anion. Rakovanite was originally assigned the formula Na3[H3V10O28]•15H2O and now is redefined with an ideal formula (NH4)3Na3[V10O28]•12H2O. Nuclear magnetic resonance (NMR) and particularly 51V NMR spectroscopy is an informative method used to describe the protonation state and speciation in both solid and solution states of materials in the chemical and life sciences. However, 51V NMR spectroscopy has not yet been used experimentally to distinguish the protonation state of the decavanadate ion of leaching solutions and thus contributing to the discussion regarding the controversial protonation states of decavanadate ions in gunterite, rakovanite, and nashite. In contrast, the morphology and crystal structure for apatites, vanadinite, pyromorphite, and mimetite was related to 207Pb NMR chemical shifts, assisting in describing the local environments of these minerals. NMR spectroscopy could be a useful method if used in the future for decavanadate-containing minerals. Currently, partial reduction of two Pascoite minerals (caseyite and nashite) is proposed and accordingly could now effectively be investigated using a different magnetic resonance technique, EPR spectroscopy.

Theoretical study of keto-enol tautomerism in 7-epi-clusianone by quantum chemical calculations of NMR chemical shifts.

Plants from the Garcinia genus have been used worldwide due to their therapeutic properties. Among the various metabolites isolated from this genus, 7-epi-clusianone, a tetraprenylated benzophenone, stands out for its wide range of identified biological activities. This benzophenone can exist in five tautomeric forms, although the benzene-d6 and chloroform-d3 solution nuclear magnetic resonance (NMR) spectra revealed only two tautomeric forms (B and C) in equilibrium, with concentration ratio depending on the solvent in which the spectrum was obtained. Calculated energy values suggested that tautomeric forms B and E would be prevalent in benzene-d6 solution, in contrast to the experimental data. Considering this conflicting result, we employed the statistical DP4 + method based on 13C and 1H NMR chemical shift calculations, in the gas phase and in benzene-d6 solution, to confirm that the B and C tautomeric forms of 7-epi-clusianone are the most prevalent in the experimental conditions.