Prediction Of NMR Chemical Shifts Research Articles

The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale

How can a single protein domain encode a conformational landscape with multiple stably folded states, and how do those states interconvert? Here, we use real-time and relaxation-dispersion NMR to characterize the conformational landscape of the circadian rhythm protein KaiB from Rhodobacter sphaeroides. Unique among known natural metamorphic proteins, this KaiB variant spontaneously interconverts between two monomeric states: the "Ground" and "Fold-switched" (FS) states. KaiB in its FS state interacts with multiple binding partners, including the central KaiC protein, to regulate circadian rhythms. We find that KaiB itself takes hours to interconvert between the Ground and FS state, underscoring the ability of a single-sequence to encode the slow process needed for function. We reveal the rate-limiting step between the Ground and FS state is the cis-trans isomerization of three prolines in the fold-switching region by demonstrating interconversion acceleration by the prolyl isomerase Cyclophilin A. The interconversion proceeds through a "partially disordered" (PD) state, where the C-terminal half becomes disordered while the N-terminal half remains stably folded. We found two additional properties of KaiB's landscape. First, the Ground state experiences cold denaturation: At 4 °C, the PD state becomes the majorly populated state. Second, the Ground state exchanges with a fourth state, the "Enigma" state, on the millisecond-timescale. We combine AlphaFold2-based predictions and NMR chemical shift predictions to predict this Enigma state is a beta-strand register shift that relieves buried charged residues, and support this structure experimentally. These results provide mechanistic insight into how evolution can design a single-sequence that achieves specific timing needed for its function.

Molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction.

The construction, management, and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments

The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. 19F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT). However, the screening of compounds and discovery of feasible drug candidates is limited due to its computational cost. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated 19F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional environments, representing their neighboring atoms within a radius of n Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. Among the models, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 3.31 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while reducing computational time from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments.

Isolation, Structure Elucidation, and Biological Activity of the Selective TACR2 Antagonist Tumonolide and its Aldehyde from a Marine Cyanobacterium.

The macrocyclic tumonolide (1) with enamide functionality and the linear tumonolide aldehyde (2) are new interconverting natural products from a marine cyanobacterium with a peptide-polyketide skeleton, representing a hybrid of apratoxins and palmyrolides or laingolides. The planar structures were established by NMR and mass spectrometry. The relative configuration of the stereogenically-rich apratoxin-like polyketide portion was determined using J-based configuration analysis. The absolute configuration of tumonolide (1) was determined by chiral analysis of the amino acid units and computational methods, followed by NMR chemical shift and ECD spectrum prediction, indicating all-R configuration for the polyketide portion, as in palmyrolide A and contrary to the all-S configuration in apratoxins. Functional screening against a panel of 168 GPCR targets revealed tumonolide (1) as a selective antagonist of TACR2 with an IC50 of 7.0 μM, closely correlating with binding affinity. Molecular docking studies established the binding mode and rationalized the selectivity for TACR2 over TACR1 and TACR3. RNA sequencing upon treatment of HCT116 colorectal cancer cells demonstrated activation of the pulmonary fibrosis idiopathic signaling pathway and the insulin secretion signaling pathway at 20 μM, indicating its potential to modulate these pathways.

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering.

By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.

Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.

Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict 1H and 13C chemical shifts, CSTShift, that combines atomic features with DFT-calculated shielding tensor descriptors, capturing both isotropic and anisotropic shielding effects. Utilizing the NMRShiftDB2 data set and conducting DFT optimization and GIAO calculations at the B3LYP/6-31G(d) level, we prepared the NMRShiftDB2-DFT data set of high-quality 3D structures and shielding tensors with corresponding experimentally measured 1H and 13C chemical shifts. The developed CSTShift models achieve the state-of-the-art prediction performance on both the NMRShiftDB2-DFT test data set and external CHESHIRE data set. Further case studies on identifying correct structures from two groups of constitutional isomers show its capability for structure assignment and elucidation. The source code and data are accessible at https://yzhang.hpc.nyu.edu/IMA.

The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale.

How can a single protein domain encode a conformational landscape with multiple stably-folded states, and how do those states interconvert? Here, we use real-time and relaxation-dispersion NMR to characterize the conformational landscape of the circadian rhythm protein KaiB from Rhodobacter sphaeroides. Unique among known natural metamorphic proteins, this variant of KaiB spontaneously interconverts between two monomeric states: the "Ground" and "Fold-switched" (FS) state. KaiB in its FS state interacts with multiple binding partners, including the central KaiC protein, to regulate circadian rhythms. We find that KaiB itself takes hours to interconvert between the Ground and KaiC binding-competent FS state, underscoring the ability of a single protein sequence to encode the slow process needed for its biological function. We reveal that the rate-limiting step between the Ground and FS state is the cis-trans isomerization of three prolines in the C-terminal fold-switching region by demonstrating acceleration of interconversion by the prolyl cis/trans isomerase CypA. The interconversion proceeds through a "partially disordered" (PD) state, where the N-terminal half remains stably folded while the C-terminal half becomes disordered. We discovered two additional properties of KaiB's landscape: firstly, the Ground state experiences cold denaturation: at 4°C, the PD state becomes the majorly populated state. Secondly, the Ground state exchanges with a fourth state, the "Enigma" state, on the millisecond timescale. We combine AlphaFold2-based predictions and NMR chemical shift predictions to suggest that this "Enigma" state represents a beta-strand register shift that eases buried charged residues in the Ground state. These results provide mechanistic insight in how evolution can design a single sequence that achieves specific timing needed for its function.

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

Towards 2D Borane Chemistry in Hexagonal Cyclic Compounds.

A comprehensive comparison between known benzene mono-substituted compounds R-Ph and the corresponding isoelectronic unknown R-cyclohexaborane(12) molecules is carried out from a geometric and electronic structure point of view, with R={H, BH2, CH3, NH2, OH, F ; AlH2, SiH3, PH2, SH, Cl ; NO2, OCH3}. We suggest new chemical names for the 2D borane compounds and analyze the geometric and electronic structure carbon vs. boron comparatives by means of HOMO-LUMO gaps, bonding schemes, electron density topological properties and predicted NMR chemical shifts. The predictions on the properties in planar hexagonal cyclic boranes may help in the design of synthesis procedures for these yet-unkown compounds.

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning.

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive. By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation. We illustrate the predictive capacity of the resulting inexpensive shift machine learning (iShiftML) models across several benchmarks, including unseen molecules in the NS372 data set, gas-phase experimental chemical shifts for small organic molecules, and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

Prediction of 19F NMR chemical shifts for organic compounds with ORCA

Accurate assignment of 19F NMR has long been a challenge, and quantum chemical methods are possible solutions. Herein we reported a scaling method for the prediction of 19F NMR chemical shift with freely available ORCA program package. Performance of 31 DFT functionals coupled with 11 basis sets were evaluated and influence of geometry optimization was also studied with five functionals coupled with three basis sets. The significance of geometry was further examined through the execution of relaxed surface scans of seven flexible compounds, and averaged shieldings of obtained conformers yielded notable improvement of the correlation between calculated isotropic shielidings and experimental chemical shifts. Utilization of the best scaling factor obtained successfully assigned of fluorine atoms in multifluorinated molecules with different conformations. The method reported here was computationally inexpensive, easily available with acceptable accuracy.

MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids.

We developed a random forest machine learning (ML) model for the prediction of 1H and 13C NMR chemical shifts of nucleic acids. Our ML model is trained entirely on reproducing computed chemical shifts obtained previously on 10 nucleic acids using a Molecules-in-Molecules (MIM) fragment-based density functional theory (DFT) protocol including microsolvation effects. Our ML model includes structural descriptors as well as electronic descriptors from an inexpensive low-level semiempirical calculation (GFN2-xTB) and trained on a relatively small number of DFT chemical shifts (2080 1H chemical shifts and 1780 13C chemical shifts on the 10 nucleic acids). The ML model is then used to make chemical shift predictions on 8 new nucleic acids ranging in size from 600 to 900 atoms and compared directly to experimental data. Though no experimental data was used in the training, the performance of our model is excellent (mean absolute deviation of 0.34 ppm for 1H chemical shifts and 2.52 ppm for 13C chemical shifts for the test set), despite having some nonstandard structures. A simple analysis suggests that both structural and electronic descriptors are critical for achieving reliable predictions. This is the first attempt to combine ML from fragment-based DFT calculations to predict experimental chemical shifts accurately, making the MIM-ML model a valuable tool for NMR predictions of nucleic acids.

NMR chemical shift prediction and structural elucidation of linker-containing oligo- and polysaccharides using the computer program CASPER

Carbohydrate structures containing alkyl groups as aglycones are useful for investigating enzyme activity and glycan-protein interactions. Moreover, linker-containing oligosaccharides with a spacer group are commonly used to print glycan microarrays or to prepare protein-conjugates as vaccine candidates. The structural accuracy of these synthesized glycans are essential for interpretation of results from biological experiments in which the compounds have been used and NMR spectroscopy can unravel and confirm their structures. An approach for efficient 1H and 13C NMR chemical shift assignments employed a parallel NOAH-10 measurement followed by NMR spin-simulation to refine the 1H NMR chemical shifts, as exemplified for a disaccharide with an azidoethyl group as an aglycone, the NMR chemical shifts of which have been used to enhance the quality of CASPER (http://www.casper.organ.su.se/casper/). The CASPER program has been further developed to aid characterization of linker-containing oligo- and polysaccharides, either by chemical shift prediction for comparison to experimental NMR data or as structural investigation of synthesized glycans based on acquired unassigned NMR data. The ability of CASPER to elucidate structures of linker-containing oligosaccharides is demonstrated and comparisons to assigned or unassigned NMR data show the utility of CASPER in supporting a proposed oligosaccharide structure. Prediction of NMR chemical shifts of an oligosaccharide, corresponding to the repeating unit of an O-antigen polysaccharide, having a linker as an aglycone and a non-natural substituent derivative thereof are presented to exemplify the diversity of structures handled. Furthermore, NMR chemical shift predictions of synthesized polysaccharides, corresponding to bacterial polysaccharides, containing a linker are described showing that in addition to oligosaccharide structures also polysaccharide structures having an aglycone spacer group can be analyzed by CASPER.

Alpiniamides E-G from the Saline Lake-Derived Streptomyces sp. QHA48 and Their Lipid Accumulation Inhibitory Activity.

Alpiniamides E-G, three previously unreported linear polyketide derivatives, along with two known compounds, were isolated from Streptomyces sp. QHA48, which was isolated from the saline lakes of Qinghai-Tibet Plateau. The structures of these compounds were determined through analysis of their spectroscopic data, as well as density functional theory prediction of NMR chemical shifts, application of the DP4+ algorithm and electronic circular dichroism (ECD) calculations. In a cell-based lipid-lowering assay, all five alpiniamides exhibited significant inhibition of lipid accumulation in HepG2 cells without inducing cytotoxic effects at a concentration of 27 μM.

Dendrillic Acids A and B: Nitrogenous, Rearranged Spongian Nor-Diterpenes from a Dendrilla sp. Marine Sponge.

Two nitrogenous rearranged spongian nor-diterpenoids, dendrillic acids A and B, were isolated from a marine sponge Dendrilla sp. (order: Dendroceratida; family: Darwinellidae). The structures of the metabolites were elucidated on the basis of spectroscopic analysis as well as density functional theory prediction of NMR chemical shifts and application of the DP4+ algorithm. The absolute configuration of the metabolites was established via comparison of experimental and time-dependent density functional theory predicted electronic circular dichroism data. An unusual epimerization reaction was observed leading to the interconversion of the metabolites upon storage in dimethyl sulfoxide solution, which is proposed to proceed via an anionic pathway as probed via isotopic incorporation experiments. Evaluation against a panel of micro-organisms and cell lines revealed that the compounds were devoid of any significant biological activity against all organisms tested, with the exception of mild antiprotozoal activity displayed by dendrillic acid B (2) against Giardia duodenalis.

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.

Species-specific secondary metabolism by actinomycetes of the genus Phytohabitans and discovery of new pyranonaphthoquinones and isatin derivatives.

To further exploit secondary metabolic potential of a minor actinomycete genus Phytohabitans within the family Micromonosporaceae, metabolite profiling by HPLC-UV analysis, combined with 16S rDNA sequence-based phylotyping were attempted on seven Phytohabitans strains available at the public culture collection. The strains were grouped into three clades and each exhibited unique and distinct metabolite profiles, which were highly conserved among strains within the same clade. These results were consistent with previous observations on two other actinomycetes genera, reconfirming species-specificity of secondary metabolite production, which were conventionally thought to be strain-specific. A strain RD003215, belonging to the P. suffuscus clade, produced multiple metabolites, some of which were presumed to be naphthoquinones. Liquid fermentation followed by chromatographic separation of the broth extract led to the discovery of three new pyranonaphthoquinones, designated habipyranoquinones A-C (1-3), and one new isatin derivative, (R)-N-methyl-3-hydroxy-5,6-dimethoxyoxindole (4), along with three known synthetic compounds, 6,8-dihydroxydehydro-α-lapachone (5), N-methyl-5,6-dimethoxyisatin (6), and 5,6-dimethoxyisatin (7). Structures of 1-4 were unequivocally elucidated by NMR, MS, and CD spectral analysis, with assistance of density functional theory-based NMR chemical shift prediction and ECD spectral calculation. Compound 2 displayed antibacterial activity against Kocuria rhizophila and Staphylococcus aureus with MIC 50 µg/mL and cytotoxicity against P388 murine leukemia cells with an IC50 value of 34 µM. Compounds 1 and 4 also showed cytotoxicity against P388 cells with IC50 values of 29 and 14 µM, respectively.

Benchmarking computational chemistry approaches on iminodiacetic acid

In the present study, theoretical harmonic vibrational frequencies (IR and Raman), carbon and proton NMR chemical shifts, geometric parameters, atomic charges (only for heteroatoms), reactivity indices (eLUMO, eHOMO, electronegativity, and hardness), and thermodynamical data (inner energy, enthalpy, Gibbs free energy, and entropy) of iminodiacetic acid (IDA) molecule have been investigated. We utilized ORCA software for B3LYP and HF (combined with Pople and Karlsruhe basis sets) calculations and MOPAC2016 software for semi-empirical calculations (AM1, PM3, and PM6). Theoretical vibrational frequencies and carbon and proton NMR chemical shifts have been compared with the corresponding experimental data. Although there was a strong correlation between the experimental and computational vibrational frequencies at low frequencies (<2200 cm−1), the computational predictions of vibrational frequencies were unsuccessful at high frequencies (>2200 cm−1). Distinctly, the studied computational approaches appeared to perform better in the prediction of carbon and proton NMR chemical shifts. Theoretical vibrational frequencies were also compared to each other to understand the impact of method choice (HF vs B3LYP D3 vs semi-empirical methods), dispersion correction (B3LYP D3 vs B3LYP), water solvation (SMD supplemented vs non-supplemented calculations), the family of basis set (Pople vs Karlsruhe basis sets), numbers of zeta (double vs triple zeta), polarization function (polarized vs nonpolarized basis sets), and diffusion function (diffusion supplemented vs non-supplemented basis sets). Moreover, geometric parameters, heteroatom charges, reactivity indices, and thermodynamical data produced by distinct computational approaches, as well, were compared to each other. Based on these comparisons, we detected critical factors (such as water solvation) acting on the computation of geometries, energies, and charges.

Accurate and Cost-Effective NMR Chemical Shift Predictions for Nucleic Acids Using a Molecules-in-Molecules Fragmentation-Based Method.

We have developed, implemented, and assessed an efficient protocol for the prediction of NMR chemical shifts of large nucleic acids using our molecules-in-molecules (MIM) fragment-based quantum chemical approach. To assess the performance of our approach, MIM-NMR calculations are calibrated on a test set of three nucleic acids, where the structure is derived from solution-phase NMR studies. For DNA systems with multiple conformers, the one-layer MIM method with trimer fragments (MIM1trimer) is benchmarked to get the lowest energy structure, with an average error of only 0.80 kcal/mol with respect to unfragmented full molecule calculations. The MIMI-NMRdimer calibration with respect to unfragmented full molecule calculations shows a mean absolute deviation (MAD) of 0.06 and 0.11 ppm, respectively, for 1H and 13C nuclei, but the performance with respect to experimental NMR chemical shifts is comparable to the more expensive MIM1-NMR and MIM2-NMR methods with trimer subsystems. To compare with the experimental chemical shifts, a standard protocol is derived using DNA systems with Protein Data Bank (PDB) IDs 1SY8, 1K2K, and 1KR8. The effect of structural minimizations is employed using a hybrid mechanics/semiempirical approach and used for computations in solution with implicit and explicit-implicit solvation models in our MIM1-NMRdimer methodology. To demonstrate the applicability of our protocol, we tested it on seven nucleic acids, including structures with nonstandard residues, heteroatom substitutions (F and B atoms), and side chain mutations with a size ranging from ∼300 to 1100 atoms. The major improvement for predicted MIM1-NMRdimer calculations is obtained from structural minimizations and implicit solvation effects. A significant improvement with the explicit-implicit solvation model is observed only for two smaller nucleic acid systems (1KR8 and 7NBK), where the expensive first solvation shell is replaced by the microsolvation model, in which a single water molecule is added for each solvent-exposed amino and imino protons, along with the implicit solvation. Overall, our target accuracy of ∼0.2-0.3 ppm for 1H and ∼2-3 ppm for 13C has been achieved for large nucleic acids. The proposed MIM-NMR approach is accurate and cost-effective (linear scaling with system size), and it can aid in the structural assignments of a wide range of complex biomolecules.

Primary Structure of Glycans by NMR Spectroscopy.

Glycans, carbohydrate molecules in the realm of biology, are present as biomedically important glycoconjugates and a characteristic aspect is that their structures in many instances are branched. In determining the primary structure of a glycan, the sugar components including the absolute configuration and ring form, anomeric configuration, linkage(s), sequence, and substituents should be elucidated. Solution state NMR spectroscopy offers a unique opportunity to resolve all these aspects at atomic resolution. During the last two decades, advancement of both NMR experiments and spectrometer hardware have made it possible to unravel carbohydrate structure more efficiently. These developments applicable to glycans include, inter alia, NMR experiments that reduce spectral overlap, use selective excitations, record tilted projections of multidimensional spectra, acquire spectra by multiple receivers, utilize polarization by fast-pulsing techniques, concatenate pulse-sequence modules to acquire several spectra in a single measurement, acquire pure shift correlated spectra devoid of scalar couplings, employ stable isotope labeling to efficiently obtain homo- and/or heteronuclear correlations, as well as those that rely on dipolar cross-correlated interactions for sequential information. Refined computer programs for NMR spin simulation and chemical shift prediction aid the structural elucidation of glycans, which are notorious for their limited spectral dispersion. Hardware developments include cryogenically cold probes and dynamic nuclear polarization techniques, both resulting in enhanced sensitivity as well as ultrahigh field NMR spectrometers with a 1H NMR resonance frequency higher than 1 GHz, thus improving resolution of resonances. Taken together, the developments have made and will in the future make it possible to elucidate carbohydrate structure in great detail, thereby forming the basis for understanding of how glycans interact with other molecules.