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1. Predicted and Experimental NMR Chemical Shifts at Variable Temperatures: The Effect of Protein Conformational Dynamics

   Xu Yi, Lichirui Zhang (January 2023, bioRxiv)

   na (Ed.)

   NMR chemical shifts provide a sensitive probe of protein structure and dynamics. Prediction of shifts, and therefore interpretation of shifts, particularly for the frequently measured amidic 15N sites, remains a tall challenge. We demonstrate that protein 15N chemical shift prediction from QM/MM predictions can be improved if conformational variation is included via MD sampling, focusing on the antibiotic target, E. coli Dihydrofolate reductase (DHFR). Variations of up to 25 ppm in predicted 15N chemical shifts are observed over the trajectory. For solution shifts the average of fluctuations on the low picosecond timescale results in a superior prediction to a single optimal conformation. For low temperature solid state measurements, the histogram of predicted shifts for locally minimized snapshots with specific solvent arrangements sampled from the trajectory explains the heterogeneous linewidths; in other words, the conformations and associated solvent are ‘frozen out’ at low temperatures and result in inhomogeneously broadened NMR peaks. We identified conformational degrees of freedom that contribute to chemical shift variation. Backbone torsion angles show high amplitude fluctuations during the trajectory on the low picosecond timescale. For a number of residues, including I60, ψ varies by up to 60º within a conformational basin during the MD simulations, despite the fact that I60 (and other sites studied) are in a secondary structure element and remain well folded during the trajectory. Fluctuations in ψ appear to be compensated by other degrees of freedom in the protein, including φ of the succeeding residue, resulting in “rocking” of the amide plane with changes in hydrogen bonding interactions. Good agreement for both room temperature and low temperature NMR spectra provides strong support for the specific approach to conformational averaging of computed chemical shifts. 

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2. Predicting chemical shifts with graph neural networks

   https://doi.org/10.1039/D1SC01895G  

   Yang, Ziyue; Chakraborty, Maghesree; White, Andrew D. (January 2021, Chemical Science)

   null (Ed.)

   Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state-of-the-art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules. 

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3. Origin of the ²⁹ Si NMR chemical shift in R ₃ Si–X and relationship to the formation of silylium (R ₃ Si ⁺ ) ions

   https://doi.org/10.1039/D0DT02099K  

   Huynh, Winn; Conley, Matthew P. (November 2020, Dalton Transactions)

   null (Ed.)

   The origin in deshielding of 29 Si NMR chemical shifts in R 3 Si–X, where X = H, OMe, Cl, OTf, [CH 6 B 11 X 6 ], toluene, and O X (O X = surface oxygen), as well as i Pr 3 Si + and Mes 3 Si + were studied using DFT methods. At the M06-L/6-31G(d,p) level of theory the geometry optimized structures agree well with those obtained experimentally. The trends in 29 Si NMR chemical shift also reproduce experimental trends; i Pr 3 Si–H has the most shielded 29 Si NMR chemical shift and free i Pr 3 Si + or isolable Mes 3 Si + have the most deshielded 29 Si NMR chemical shift. Natural localized molecular orbital (NLMO) analysis of the contributions to paramagnetic shielding ( σ p ) in these compounds shows that Si–R (R = alkyl, H) bonding orbitals are the major contributors to deshielding in this series. The Si–R bonding orbitals are coupled to the empty p-orbital in i Pr 3 Si + or Mes 3 Si + , or to the orbital in R 3 Si–X. This trend also applies to surface bound R 3 Si–O X . This model also explains chemical shift trends in recently isolated t Bu 2 SiH 2 + , t BuSiH 2 + , and SiH 3 + that show more shielded 29 Si NMR signals than R 3 Si + species. There is no correlation between isotropic 29 Si NMR chemical shift and charge at silicon. 

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4. Solvent effect on the ¹⁹⁵ Pt NMR properties in pyridonate-bridged Pt ^III dinuclear complex derivatives investigated by ab initio molecular dynamics and localized orbital analysis

   https://doi.org/10.1039/D0CP05849A  

   Batista, Patrick R.; Ducati, Lucas C.; Autschbach, Jochen (June 2021, Physical Chemistry Chemical Physics)

   null (Ed.)

   An ab initio molecular dynamics investigation of the solvent effect (water) on the structural parameters, 195 Pt NMR spin–spin coupling constants (SSCCs) and chemical shifts of a series of pyridonate-bridged Pt III dinuclear complexes is performed using Kohn–Sham (KS) Car–Parrinello molecular dynamics (CPMD) and relativistic hybrid KS NMR calculations. The indirect solvent effect ( via structural changes) has a dramatic effect on the 1 J PtPt SSCCs. The complexes exhibit a strong trans influence in solution, where the Pt–Pt bond lengthens with increasing axial ligand σ-donor strength. In the diaqua complex, where the solvent effect is more pronounced, the SSCCs averaged for CPMD configurations with explicit plus implicit solvation agree much better with the experimental data, while the calculations for static geometry and CPMD unsolvated configurations show large deviations with respect to experiment. The combination of CPMD with hybrid KS NMR calculations provides a much more realistic computational model that reproduces the large magnitudes of 1 J PtPt and 195 Pt chemical shifts. An analysis of 1 J PtPt in terms of localized and canonical orbitals shows that the SSCCs are driven by changes in the s-character of the natural atomic orbitals of Pt atoms, which affect the 'Fermi contact' mechanism. 

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5. The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions

   https://doi.org/10.1039/D4FD00072B  

   Ramos, Sebastian A; Mueller, Leonard J; Beran, Gregory_J O (January 2024, Faraday Discussions)

   Ab initio chemical shift prediction plays a central role in nuclear magnetic resonance (NMR) crystallography, and the accuracy with which chemical shifts can be predicted relative to experiment impacts the... 

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