Prediction Of NMR Chemical Shifts Research Articles

The need for operando modelling of 27Al NMR in zeolites: the effect of temperature, topology and water.

Solid state (ss-) 27Al NMR is one of the most valuable tools for the experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be extracted from the spectra. Unfortunately, the interpretation of ss-NMR is complex and the determination of aluminum distributions remains generally unfeasible. As a result, computational modelling of 27Al ss-NMR spectra has grown increasingly popular as a means to support experimental characterization. However, a number of simplifying assumptions are commonly made in NMR modelling, several of which are not fully justified. In this work, we systematically evaluate the effects of various common models on the prediction of 27Al NMR chemical shifts in zeolites CHA and MOR. We demonstrate the necessity of operando modelling; in particular, taking into account the effects of water loading, temperature and the character of the charge-compensating cation. We observe that conclusions drawn from simple, high symmetry model systems such as CHA do not transfer well to more complex zeolites and can lead to qualitatively wrong interpretations of peak positions, Al assignment and even the number of signals. We use machine learning regression to develop a simple yet robust relationship between chemical shift and local structural parameters in Al-zeolites. This work highlights the need for sophisticated models and high-quality sampling in the field of NMR modelling and provides correlations which allow for the accurate prediction of chemical shifts from dynamical simulations.

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.

DFT calculations of 1H- and 13C-NMR chemical shifts of 3-methyl-1-phenyl-4-(phenyldiazenyl)-1H-pyrazol-5-amine in solution

Geometries of the 3-methyl-1-phenyl-4-(phenyldiazenyl)-1H-pyrazol-5-amine azo-dye compound and its tautomer were optimized using B3LYP and M06-2X functionals in coupling with TZVP and 6–311 + G(d,p) basis sets. The 1H- and 13C-NMR chemical shifts of all species were predicted using 13 density functional theory (DFT) approaches in coupling with TZVP and 6–311 + G(d,p) basis sets at the different optimized geometries by applying the using GIAO method using the eight geometries. The selected functionals are characterized by having different amount of Hartree–Fock exchange. The selected DFT methods were B3LYP, M06-2X, BP86, B97XD, TPSSTPSS, PBE1PBE, CAM-B3LYP, wB97XD, LSDA, HSEH1PBE, PW91PW91, LC-WPBE, and B3PW91. The results obtained were compared with the available experimental data using different statistical descriptors such as root mean square error (RMSE) and maximum absolute error (MAE). Results revealed that the prediction of the 1H-NMR chemical shifts has more significant dependence on the applied geometry than that of the prediction of the 13C-NMR chemical shifts. Among all the examined functionals, B97D and TPSSTPSS functionals were found to be the most accurate ones, while the M06-2X functional is the least accurate one. Results also revealed that the prediction of NMR chemical shifts using TZVP basis sets results is more accurate results than 6–311 + G(2d,p) basis set.

Efficient Screening of Target-Specific Selected Compounds in Mixtures by 19 F NMR Binding Assay with Predicted 19 F NMR Chemical Shifts.

Ligand-based 19 F NMR screening is a highly effective and well-established hit-finding approach. The high sensitivity to protein binding makes it particularly suitable for fragment screening. Different criteria can be considered for generating fluorinated fragment libraries. One common strategy is to assemble a large, diverse, well-designed and characterized fragment library which is screened in mixtures, generated based on experimental 19 F NMR chemical shifts. Here, we introduce a complementary knowledge-based 19 F NMR screening approach, named 19 Focused screening, enabling the efficient screening of putative active molecules selected by computational hit finding methodologies, in mixtures assembled and on-the-fly deconvoluted based on predicted 19 F NMR chemical shifts. In this study, we developed a novel approach, named LEFshift, for 19 F NMR chemical shift prediction using rooted topological fluorine torsion fingerprints in combination with a random forest machine learning method. A demonstration of this approach to a real test case is reported.

Towards accurate predictions of NMR chemical shifts in liquid solutions.

Towards accurate predictions of NMR chemical shifts in liquid solutions.

Complete 1H and 13C NMR chemical shift assignments of mono-to tetrasaccharides as basis for NMR chemical shift predictions of oligo- and polysaccharides using the computer program CASPER

Carbohydrate structure can be elucidated or confirmed by using NMR spectroscopy as the prime technique. Prediction of 1H and 13C NMR chemical shifts by computational approaches makes this assignment process more efficient and the program CASPER can perform this task rapidly. It does so by relying on chemical shift data of mono-, di-, and trisaccharides. In order to improve accuracy and quality of these predictions we have assigned 1H and 13C NMR chemical shifts of 30 monosaccharides, 17 disaccharides, 10 trisaccharides and one tetrasaccharide; in total 58 compounds. Due to different rotamers, ring forms, α- and β-anomeric forms and pD conditions this resulted in 74 1H and 13C NMR chemical shift data sets, all of which were refined using total line-shape analysis for the 1H resonances in order to obtain accurate chemical shifts. Subsequent NMR chemical shift predictions for three sialic acid-containing oligosaccharides, viz., GD1a, a disialyl-LNnT hexasaccharide and a polysialic acid-lactose decasaccharide, and NMR-based structural elucidations of two O-antigen polysaccharides from E. coli O174 were performed by the CASPER program (http://www.casper.organ.su.se/casper/) resulting in very good to excellent agreement between experimental and predicted data thereby demonstrating its utility for carbohydrate compounds that have been chemically or enzymatically synthesized, structurally modified or isolated from nature.

Synthesis, characterization, theoretical studies and antimicrobial activity of novel 1-(2‑hydroxy-4-propoxy-3-propylphenyl)ethanones bearing thiotetrazole

We report herein a joint experimental and theoretical investigation on the novel 1-(2-hydroxy-4-propoxy-3-propylphenyl)ethanone bearing 1-propyl-1H-tetrazol-5-thiol and 2-propyl-2H-tetrazole-5-thiol pharmacophores. In the experimental studies, 1-[4-(3-((1-propyl-1H-tetrazol-5-yl)thio)propoxy)-2-hydroxy-3-propylphenyl]ethanone and 1-[4-(3-((2-propyl-2H-tetrazol-5-yl)thio)propoxy)-2-hydroxy-3-propylphenyl]ethanone were synthesized and characterized by ATR-FTIR, 1H NMR, 13C-APT, and HRMS. In the theoretical studies, the most stable conformations, vibrational and NMR spectra of these compounds were predicted by Quantum chemical calculations. Quantum chemical calculations were performed using the B3LYP functional with 3–21 G, and 6–311++G(d,p) basis sets. All quantum chemical calculations were utilized Gaussian 16 software. While torsional barriers of the compounds were determined by B3LYP/ 3–21 G, their geometry optimizations were performed by B3LYP functional with 3–21 G, and 6–311++G(d,p) basis sets. The harmonic vibrational frequencies, potential energy distribution (PED) and infrared intensities were predicted using the B3LYP/6–311++G(d,p). Theoretically predicted vibrational frequencies were compared with experimental results. Predicted NMR chemical shifts were obtained by the GIAO method using the B3LYP/6–311++G(d,p). The 1H and 13C experimental chemical shifts are consistent with the theoretical NMR chemical shift values. Antimicrobial activities of the title compounds were investigated against five bacterial strains (Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, Pseudomonas aeruginosa, Stenotrophomonas maltophilia) and two fungal strains (Candida albicans, Aspergillus fumigatus). The compounds showed good antimicrobial activity and inhibited all tested bacteria and fungi.

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1.

The dynamic interactions between G protein-coupled receptors (GPCRs) and their cognate protein partners are central to several cell signaling pathways. For example, the association of CXC chemokine receptor 1 (CXCR1) with its cognate chemokine, interleukin-8 (IL8 or CXCL8) initiates pathways leading to neutrophil-mediated immune responses. The N-terminal domain of chemokine receptors confers ligand selectivity, but unfortunately the conformational dynamics of this intrinsically disordered region remains unresolved. In this work, we have explored the interaction of CXCR1 with IL8 by microsecond time scale coarse-grain simulations, complemented by atomistic models and NMR chemical shift predictions. We show that the conformational plasticity of the apo-receptor N-terminal domain is restricted upon ligand binding, driving it to an open C-shaped conformation. Importantly, we corroborated the dynamic complex sampled in our simulations against chemical shift perturbations reported by previous NMR studies and show that the trends are similar. Our results indicate that chemical shift perturbation is often not a reporter of residue contacts in such dynamic associations. We believe our results represent a step forward in devising a strategy to understand intrinsically disordered regions in GPCRs and how they acquire functionally important conformational ensembles in dynamic protein-protein interfaces.

DFT Simulations as Valuable Tool to Support NMR Characterization of Halide Perovskites: the Case of Pure and Mixed Halide Perovskites

AbstractSolid state NMR spectroscopy is swiftly emerging as useful tool to characterize the structure, composition and dynamic properties of lead halide perovskites. On the other hand, interpretation of solid state NMR signatures is often challenging, because of the potential presence of many overlapping signals in small range of chemical shifts, hence complicating the extraction of detailed structural features. Here, we demonstrate the reliability of periodic Density Functional Theory in providing theoretical support for the NMR characterization of halide perovskite compounds, considering nuclei with spin I=1/2. For light 1H and 13C nuclei, we predict NMR chemical shifts in good agreement with experiment, further highlighting the effects of motional narrowing. Accurate prediction of the NMR response of 207Pb nuclei is comparably more challenging, but we successfully reproduce the downshift in frequency when changing the halide composition from pure iodine to pure bromine. Furthermore, we confirm NMR as ideal tool to study mixed halide perovskite compounds, currently at the limelight for tandem solar cells and color‐tunable light emission.

Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions.

An accurate prediction of chemical shifts (δ) to elucidate molecular structures has been a challenging problem. Recently, noble machine learning architectures achieve accurate prediction performance, but the difficulty of building a huge chemical database limits the applicability of machine learning approaches. In this work, we demonstrate that the prior knowledge gained from the simulation database is successfully transferred into the problem of predicting an experimentally measured δ. Although both simulation and experimental databases are vastly different in chemical perspectives, reliable accuracy for δ is achieved by additional training with randomly sampled small numbers of experimental data. Furthermore, the prior knowledge allows us to successfully train the model on the more focused chemical space that the experimental database sparsely covers. The proposed approach, the knowledge transfer from the simulation database, can be utilized to enhance the usability of the local experimental database.

TMKS8A, an antibacterial and cytotoxic chlorinated α-lapachone, from a sea slug-derived actinomycete of the genus Streptomyces.

TMKS8A (1), a new chlorinated α-lapachone derivative, along with five known related metabolites, A80915 C (2), SF2415B1 (3), chlorinated dihydroquinone 3 (4), SF2415B3 (5), and A80915 C (6), were identified from the culture extract of Streptomyces sp. TMKS8, which was isolated from a sea slug, Paromoionchis tumidus. The structure of 1 was determined by the analysis of NMR and MS spectral data, assisted by NMR chemical shift prediction using DFT-based calculation. The absolute configuration was determined to be R by comparison of experimental and calculated ECD spectra. Compound 1 displayed antimicrobial activity against Gram-positive bacteria with MIC values ranging from 6.25 to 12.5 μg ml-1 and cytotoxicity against murine leukemia P388 cells with IC50 9.8 μM.

Cytotoxic constituents from the wheat plant pathogen Parastagonospora nodorum SN15

Microbial natural products are continuing to be a promising platform for future drug lead discover. As a part of our ongoing research program on fungal natural product, herein we report metabolites isolated from the fungus Parastagonospora nodorum SN15 a pathogen of wheat and related cereals. Its chemical investigation led to the purification of new isoleucinic acid derivatives (1–2) along with the cis procuramine (4). Their structures were determined based on extensive NMR and the relative configuration by comparison of experimental and predicted NMR chemical shifts. All compounds were evaluated for their cytotoxic activity against a panel of human cell lines and some displayed specific feature towards cancer cells compared to normal immortalised fibroblasts.

Density Functional Theory (DFT)-Aided Structure Elucidation of Linear Diterpenes from the Irish Brown Seaweed Bifurcaria bifurcata.

Brown alga Bifurcaria bifurcata is an extraordinarily rich source of linear (acylic) diterpenes with enormous structural diversity. As part of our interest into secondary metabolites of the Irish seaweeds, here we report four new acyclic diterpenes (1–4) and seven known terpenoids (5–11) from the CHCl3 extract of B. bifurcata. The planar structures of the new metabolites were elucidated by means of 1D and 2D NMR, HRMS, and FT-IR spectroscopy. Since linear diterpenes are highly flexible compounds, the assignment of their stereochemistry by conventional methods, e.g., NOESY NMR, is difficult. Therefore, we employed extensive quantum-mechanical prediction of NMR chemical shifts and optical rotation analyses to identify the relative and absolute configurations of the new compounds 1–4. Several compounds moderately inhibited the human breast cancer cell line (MDA-MB-231) with IC50 values ranging from 10.0 to 33.5 μg/mL. This study not only demonstrates the vast capacity of the Irish B. bifurcata to produce highly oxygenated linear diterpenoids, but also highlights the potential of new methodologies for assignment of their stereogenic centers.

Cyclopeptides from the Mushroom Pathogen Fungus Cladobotryum varium.

Three new cyclopeptides with serial Phe residues were identified with the aid of HPLC-DAD analysis, from the culture broth of Cladobotryum varium, a fungal pathogen causing mushroom cobweb disease. Cladoamides A (1) and B (2) have two consecutive N-methylphenylalanine units in the destruxin class cyclic depsipentapeptide framework, while cladoamide C (3) has a three consecutive Phe motif in a cyclopentapeptide structure. Of these three cyclopeptides, 1 showed potent autophagy-inducing activity at 10 μg/mL, comparable to a positive control, rapamycin. For the determination of the absolute configurations of the Ile residues in 1 and 3, new conditions for separating Ile and allo-Ile, using a pentafluorophenyl-bonded solid phase and methanolic solvent, were established within the analytical scheme of the advanced Marfey's method, thus offering a convenient alternative to the C3 Marfey's method, which requires elution with a three-solvent mixture. The sequence of two d-Phe and one l-Phe in 3 was determined through NMR chemical shift prediction by DFT-based calculations and chemical synthesis, which demonstrated the significance of noncovalent interactions in the accurate calculation of stable conformers for peptides with multiple aromatic rings.

1H and 195Pt NMR prediction for inclusion compounds formed by cisplatin and oxidized carbon nanostructures.

Prediction of NMR chemical shifts can assist experimentalists in the characterization of drug delivery systems based on carbon nanocomposites. Chemical shifts are strongly correlated to the nucleus position and its chemical neighborhood. Therefore, to predict structures and NMR properties of complex chemical models, choosing a more consistent theoretical level capable of providing more realistic results and moderate computational demand is a major challenge. In this work, we predicted the NMR spectra of inclusion compounds formed by cisplatin (cDDP) and an oxidized carbon nanotube (CNTox) and nanocone (CNCox) considered by specialists as potential drug delivery systems. The 195Pt NMR chemical shifts calculated at the DFT level with the new relativistic NMR-DKH basis set were −2314 ppm and −2192 ppm for cDDP@CNTox and cDDP@CNCox complexes, respectively, which are both high-field shifted relative to the free cDDP (−2110 ppm). 1H NMR chemical shifts are also sensitive to the inclusion process. The H (NH3) signals are found on average at +4.3 (cDDP), −5.1 (cDDP@CNTox) and +6.6 ppm (cDDP@CNCox). Interestingly, despite the similar inclusion modes in CNTox and CNCox cavities, the 1H NMR shifts were in opposite directions. A possible reason might be the higher stability of cDDP@CNTox (ΔEF = −19.9 kcal mol−1) than that of cDDP@CNCox (ΔEF = −5.7 kcal mol−1), which suggests a short guest–host contact in the former and consequently, a more efficient shielding of hydrogen atoms due to the electron-rich carbon structure. These results may be helpful as comparison data in the NMR spectra assignment in solution and the inclusion compounds' structural elucidation.

First-Principles Calculation of 1H NMR Chemical Shifts of Complex Metal Polyhydrides: The Essential Inclusion of Relativity and Dynamics.

1H NMR spectroscopy has become an important technique for the characterization of transition-metal hydride complexes, whose metal-bound hydrides are often difficult to locate by X-ray diffraction. In this regard, the accurate prediction of 1H NMR chemical shifts provides a useful, but challenging, strategy to help in the interpretation of the experimental spectra. In this work, we establish a density-functional-theory protocol that includes relativistic, solvent, and dynamic effects at a high level of theory, allowing us to report an accurate and reliable interpretation of 1H NMR hydride chemical shifts of iridium polyhydride complexes. In particular, we have studied in detail the hydride chemical shifts of the [Ir6(IMe)8(CO)2H14]2+ complex in order to validate previous assignments. The computed 1H NMR chemical shifts are strongly dependent on the relativistic treatment, the choice of the DFT exchange–correlation functional, and the conformational dynamics. By combining a fully relativistic four-component electronic-structure treatment with ab initio molecular dynamics, we were able to reliably model both the terminal and bridging hydride chemical shifts and to show that two NMR hydride signals were inversely assigned in the experiment.

Computational NMR Study of Ion Pairing of 1-Decyl-3-methyl-imidazolium Chloride in Molecular Solvents.

The 1H NMR spectra of 10–5 mole fraction solutions of 1-decyl-3-methyl-imidazolium chloride ionic liquid in water, acetonitrile, and dichloromethane have been measured. The chemical shift of the proton at position 2 in the imidazolium ring of 1-decyl-3-methyl-imidazolium (H2) is rather different for all three samples, reflecting the shifting equilibrium between the contact pairs and free fully solvated ions. Classical molecular dynamics simulations of the 1-decyl-3-methyl-imidazolium chloride contact ion pair as well as of free ions in water, acetonitrile, and dichloromethane have been conducted, and the quantum mechanics/molecular mechanics methods have been applied to predict NMR chemical shifts for the H2 proton. The chemical shift of the H2 proton was found to be primarily modulated by hydrogen bonding with the chloride anion, while the influence of the solvents—though differing in polarity and capabilities for hydrogen bonding—is less important. By comparing experimental and computational results, we deduce that complete disruption of the ionic liquid into free ions takes place in an aqueous solution. Around 23% of contact ion pairs were found to persist in acetonitrile. Ion-pair breaking into free ions was predicted not to occur in dichloromethane.

Toward Accurate Predictions of Atomic Properties via Quantum Mechanics Descriptors Augmented Graph Convolutional Neural Network: Application of This Novel Approach in NMR Chemical Shifts Predictions.

In this study, an augmented Graph Convolutional Network (GCN) with quantum mechanics (QM) descriptors was reported for its accurate predictions of NMR chemical shifts with respect to experimental values. The prediction errors of 13C/1H NMR chemical shifts can be as small as 2.14/0.11 ppm. There are two crucial characteristics for this modified GCN: in one aspect, such a novel neural network could efficiently extract the overall molecule structure information; in another aspect, it could accurately solve the chemical environment of the target atom. As there exists an imperfect linear regression between the experimental NMR chemical shifts (δ) and the density functional theory (DFT) calculated isotropic shielding constants (σ), the inclusion of QM descriptors within GCN can largely improve its performance. Moreover, few-shot learning also becomes feasible with these descriptors. The success of this novel GCN in chemical shifts predictions also indicates its potential applicability for other computational studies.

Halogen Bond of Halonium Ions: Benchmarking DFT Methods for the Description of NMR Chemical Shifts.

Because of their anisotropic electron distribution and electron deficiency, halonium ions are unusually strong halogen-bond donors that form strong and directional three-center, four-electron halogen bonds. These halogen bonds have received considerable attention owing to their applicability in supramolecular and synthetic chemistry and have been intensely studied using spectroscopic and crystallographic techniques over the past decade. Their computational treatment faces different challenges to those of conventional weak and neutral halogen bonds. Literature studies have used a variety of wave functions and DFT functionals for prediction of their geometries and NMR chemical shifts, however, without any systematic evaluation of the accuracy of these methods being available. In order to provide guidance for future studies, we present the assessment of the accuracy of 12 common DFT functionals along with the Hartree–Fock (HF) and the second-order Møller–Plesset perturbation theory (MP2) methods, selected from an initial set of 36 prescreened functionals, for the prediction of 1H, 13C, and 15N NMR chemical shifts of [N–X–N]+ halogen-bond complexes, where X = F, Cl, Br, and I. Using a benchmark set of 14 complexes, providing 170 high-quality experimental chemical shifts, we show that the choice of the DFT functional is more important than that of the basis set. The M06 functional in combination with the aug-cc-pVTZ basis set is demonstrated to provide the overall most accurate NMR chemical shifts, whereas LC-ωPBE, ωB97X-D, LC-TPSS, CAM-B3LYP, and B3LYP to show acceptable performance. Our results are expected to provide a guideline to facilitate future developments and applications of the [N–X–N]+ halogen bond.

Computation of NMR Shielding Constants for Solids Using an Embedded Cluster Approach with DFT, Double-Hybrid DFT, and MP2.

In this work, we explore the accuracy of post-Hartree–Fock (HF) methods and double-hybrid density functional theory (DFT) for the computation of solid-state NMR chemical shifts. We apply an embedded cluster approach and investigate the convergence with cluster size and embedding for a series of inorganic solids with long-range electrostatic interactions. In a systematic study, we discuss the cluster design, the embedding procedure, and basis set convergence using gauge-including atomic orbital (GIAO) NMR calculations at the DFT and MP2 levels of theory. We demonstrate that the accuracy obtained for the prediction of NMR chemical shifts, which can be achieved for molecular systems, can be carried over to solid systems. An appropriate embedded cluster approach allows one to apply methods beyond standard DFT even for systems for which long-range electrostatic effects are important. We find that an embedded cluster should include at least one sphere of explicit neighbors around the nuclei of interest, given that a sufficiently large point charge and boundary effective potential embedding is applied. Using the pcSseg-3 basis set and GIAOs for the computation of nuclear shielding constants, accuracies of 1.6 ppm for 7Li, 1.5 ppm for 23Na, and 5.1 ppm for 39K as well as 9.3 ppm for 19F, 6.5 ppm for 35Cl, 7.4 ppm for 79Br, and 7.5 ppm for 25Mg as well as 3.8 ppm for 67Zn can be achieved with MP2. Comparing various DFT functionals with HF and MP2, we report the superior quality of results for methods that include post-HF correlation like MP2 and double-hybrid DFT.