Quantum Mechanical Quantitative Nuclear Magnetic Resonance Enables Digital Reference Standards at All Magnetic Fields and Enhances qNMR Sustainability.

Quantum mechanics (QM)-driven 1H iterative functionalized spin analysis produces HifSA profiles, which encode the complete 1H spin parameters ("nuclear genotype") of analytes of interest. HifSA profiles enable the establishment of digital reference standards (dRS) that are portable, FAIR (findable - accessible - interoperable - reusable), and fit for the purpose of quantitative 1H NMR (qHNMR) analysis at any magnetic field. This approach enhances the sustainability of analytical standards. Moreover, the analyte-specific complete chemical shift and J-coupling information in HifSA-based dRS enable computational quantitation of substances in mixtures via QM-total-line-shape fitting (QM-qHNMR). We present the proof of concept for HifSA-based dRS by resolving the highly overlapping NMR resonances in the experimental spectra ("nuclear phenotypes") of the diastereomeric mixture of (2RS, 4RS)- and (2RS, 4SR)-difenoconazole (DFZ), a widely used antifouling food additive. The underlying 1H spin parameters are highly conserved in various solvents, are robust against variation in measurement temperature, and work across a wide range of magnetic fields. QM-qHNMR analysis of DFZ samples at 80, 400, 600, and 800 MHz showed high congruence with metrological reference values. Furthermore, this study introduces QM-qHNMR combined with chiral shift reagents for the analysis of all four DFZ stereoisomers: (2R, 4R)-, (2S, 4S)-, (2R, 4S)-, and (2S, 4R)-DFZ to perform chiral qHNMR measurements.

Universal J-coupling prediction.

A data driven approach for small molecule J-coupling prediction is presented. The method is targeted for use as part of an automatic spectrum analysis, therefore emphasizing prediction coverage, maintainability, and speed in the design. The database search involves encoding the coupling path atom types into hash codes, which are used to retrieve the matching coupling constant entries from the database. The fast hash dictionary search is followed by a k Nearest Neighbors regression to resolve the substituent and conformational dependencies, parametrized with atomic charges, torsion angles, and steric bulk.

Comprehensive strategy for proton chemical shift prediction: linear prediction with nonlinear corrections.

A fast 3D/4D structure-sensitive procedure was developed and assessed for the chemical shift prediction of protons bonded to sp3carbons, which poses the maybe greatest challenge in the NMR spectral parameter prediction. The LPNC (Linear Prediction with Nonlinear Corrections) approach combines three well-established multivariate methods viz. the principal component regression (PCR), the random forest (RF) algorithm, and the k nearest neighbors (kNN) method. The role of RF is to find nonlinear corrections for the PCR predicted shifts, while kNN is used to take full advantage of similar chemical environments. Two basic molecular models were also compared and discussed: in the MC model the descriptors are computed from an ensemble of the conformers found by conformational search based on Metropolis Monte Carlo (MMC) simulation; in the 4D model the conformational space was further expanded to the fourth dimension (time) by adding molecular dynamics to the MC conformers. An illustrative case study about the application and interpretation of the 4D prediction for a conformationally flexible structure, scopolamine, is described in detail.

Essential parameters for structural analysis and dereplication by (1)H NMR spectroscopy.

The present study demonstrates the importance of adequate precision when reporting the δ and J parameters of frequency domain (1)H NMR (HNMR) data. Using a variety of structural classes (terpenoids, phenolics, alkaloids) from different taxa (plants, cyanobacteria), this study develops rationales that explain the importance of enhanced precision in NMR spectroscopic analysis and rationalizes the need for reporting Δδ and ΔJ values at the 0.1-1 ppb and 10 mHz level, respectively. Spectral simulations paired with iteration are shown to be essential tools for complete spectral interpretation, adequate precision, and unambiguous HNMR-driven dereplication and metabolomic analysis. The broader applicability of the recommendation relates to the physicochemical properties of hydrogen ((1)H) and its ubiquity in organic molecules, making HNMR spectra an integral component of structure elucidation and verification. Regardless of origin or molecular weight, the HNMR spectrum of a compound can be very complex and encode a wealth of structural information that is often obscured by limited spectral dispersion and the occurrence of higher order effects. This altogether limits spectral interpretation, confines decoding of the underlying spin parameters, and explains the major challenge associated with the translation of HNMR spectra into tabulated information. On the other hand, the reproducibility of the spectral data set of any (new) chemical entity is essential for its structure elucidation and subsequent dereplication. Handling and documenting HNMR data with adequate precision is critical for establishing unequivocal links between chemical structure, analytical data, metabolomes, and biological activity. Using the full potential of HNMR spectra will facilitate the general reproducibility for future studies of bioactive chemicals, especially of compounds obtained from the diversity of terrestrial and marine organisms.

Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction.

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein (1)H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for (1)Hα, (1)HN, (13)Cα, (13)Cß, (13)CO and backbone (15)N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot.

4D prediction of protein (1)H chemical shifts.

A 4D approach for protein (1)H chemical shift prediction was explored. The 4th dimension is the molecular flexibility, mapped using molecular dynamics simulations. The chemical shifts were predicted with a principal component model based on atom coordinates from a database of 40 protein structures. When compared to the corresponding non-dynamic (3D) model, the 4th dimension improved prediction by 6-7%. The prediction method achieved RMS errors of 0.29 and 0.50 ppm for Halpha and HN shifts, respectively. However, for individual proteins the RMS errors were 0.17-0.34 and 0.34-0.65 ppm for the Halpha and HN shifts, respectively. X-ray structures gave better predictions than the corresponding NMR structures, indicating that chemical shifts contain invaluable information about local structures. The (1)H chemical shift prediction tool 4DSPOT is available from http://www.uku.fi/kemia/4dspot .