Quantum-chemical simulation of solid-state NMR spectra: the example of a molecular tweezer host–guest complex

Zienau, Jan; Kussmann, Jörg; Ochsenfeld, Christian

doi:10.1080/00268970903476647

Cited by 14 publications

(14 citation statements)

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“…These types of equilibria are not ruled out in the more stable cases either, although they appear to be most unlikely due to the large Δδ max values and the better correspondence between computed and experimental data. The situation here is thus rather different from the previously studied molecular tweezers, ,− where the guest molecules were more rigid and did not have the same conformational freedom as the flexible lysine or arginine side chains. Thus, dynamical equilibria play a more important role for the present systems.…”

Section: Resultscontrasting

confidence: 59%

“…The full NMR shielding tensors were calculated for all nuclei within both molecular entities with the HF method using the SVP basis set, employing a local development version of the Q-Chem quantum chemical software package to yield the complexation-induced chemical shifts Δδ max . The reliability of the HF/SVP approach utilizing gauge including atomic orbitals (GIAO) − has been shown elsewhere for comparable systems. ,− For the calculation of the nuclear shieldings, we apply linear-scaling methods, , together with the recently developed density matrix-based Laplace reformulation of coupled self-consistent field equations (DL-CPSCF) , In cases where the flexibility of two or more close-by rotating hydrogens yields only one peak in the experimental NMR spectrum, the calculated results were averaged accordingly over these hydrogen shifts. For diastereotopic hydrogens in host–guest species 1b ·Ac Lys OMe and 1b ·Ts Arg OMe labeled with footnote “d” in Table , the computed hydrogen shifts are assigned in two different possible ways and the values closer to the experimental ones are chosen to represent the calculated diastereotopic NMR shifts.…”

Section: Resultsmentioning

confidence: 99%

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Molecular Tweezers with Varying Anions: A Comparative Study

et al. 2013

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Selective binding of the phosphate-substituted molecular tweezer 1a to protein lysine residues was suggested to explain the inhibition of certain enzymes and the aberrant aggregation of amyloid petide Aβ42 or α-synuclein, which are assumed to be responsible for Alzheimer's and Parkinson's disease, respectively. In this work we systematically investigated the binding of four water-soluble tweezers 1a-d (substituted by phosphate, methanephosphonate, sulfate, or O-methylenecarboxylate groups) to amino acids and peptides containing lysine or arginine residues by using fluorescence spectroscopy, NMR spectroscopy, and isothermal titration calorimetry (ITC). The comparison of the experimental results with theoretical data obtained by a combination of QM/MM and ab initio(1)H NMR shift calculations provides clear evidence that the tweezers 1a-c bind the amino acid or peptide guest molecules by threading the lysine or arginine side chain through the tweezers' cavity, whereas in the case of 1d the guest molecule is preferentially positioned outside the tweezer's cavity. Attractive ionic, CH-π, and hydrophobic interactions are here the major binding forces. The combination of experiment and theory provides deep insight into the host-guest binding modes, a prerequisite to understanding the exciting influence of these tweezers on the aggregation of proteins and the activity of enzymes.

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Section: Resultscontrasting

confidence: 59%

Section: Resultsmentioning

confidence: 99%

Molecular Tweezers with Varying Anions: A Comparative Study

et al. 2013

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“…Recently, there has been renewed interest in modernized versions of these sorts of methods. Thanks to increased computer power and efficient algorithms for chemical shift calculations − within the gauge-including atomic orbital (GIAO) formalism, chemical shift prediction on larger clusters of molecules can now be performed routinely. Clusters consisting of ∼10–15 molecules within a few angstroms of a molecule in the asymmetric unit mimic the effect of the extended molecular crystal lattice on the chemical shielding of the central molecule very effectively.…”

Section: Introductionmentioning

confidence: 99%

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions

Hartman

Day

Beran

2016

Crystal Growth & Design

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Chemical shift prediction plays an important role in the determination or validation of crystal structures with solid-state nuclear magnetic resonance (NMR) spectroscopy. One of the fundamental theoretical challenges lies in discriminating variations in chemical shifts resulting from different crystallographic environments. Fragment-based electronic structure methods provide an alternative to the widely used plane wave gauge-including projector augmented wave (GIPAW) density functional technique for chemical shift prediction. Fragment methods allow hybrid density functionals to be employed routinely in chemical shift prediction, and we have recently demonstrated appreciable improvements in the accuracy of the predicted shifts when using the hybrid PBE0 functional instead of generalized gradient approximation (GGA) functionals like PBE. Here, we investigate the solid-state 13C and 15N NMR spectra for multiple crystal forms of acetaminophen, phenobarbital, and testosterone. We demonstrate that the use of the hybrid density functional instead of a GGA provides both higher accuracy in the chemical shifts and increased discrimination among the different crystallographic environments. Finally, these results also provide compelling evidence for the transferability of the linear regression parameters mapping predicted chemical shieldings to chemical shifts that were derived in an earlier study.

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“…These recent successes emphasize how the highly local nature of the chemical shielding tensor makes it amenable to machine-learning based on local geometric descriptors that capture the chemical environment within several Ångstroms from the atom of interest. At the same time, ample evidence demonstrates that chemical shieldings can be influenced by surrounding atoms lying 5–8 Å away, − outside the range of local atomic environment descriptors typically used in present-day ML models. Despite the excellent progress in ML chemical shielding prediction discussed above, the errors in current state-of-the-art ML models relative to first-principles DFT remain substantially larger than the errors between DFT and experiment.…”

Section: Introductionmentioning

confidence: 99%

Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning

Unzueta

Greenwell

Beran

2021

J. Chem. Theory Comput.

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First-principles prediction of nuclear magnetic resonance chemical shifts plays an increasingly important role in the interpretation of experimental spectra, but the required density functional theory (DFT) calculations can be computationally expensive. Promising machine learning models for predicting chemical shieldings in general organic molecules have been developed previously, though the accuracy of those models remains below that of DFT. The present study demonstrates how much higher accuracy chemical shieldings can be obtained via the Δ-machine learning approach, with the result that the errors introduced by the machine learning model are only one-half to one-third the errors expected for DFT chemical shifts relative to experiment. Specifically, an ensemble of neural networks is trained to correct PBE0/6-31G chemical shieldings up to the target level of PBE0/6-311+G(2d,p). It can predict 1H, 13C, 15N, and 17O chemical shieldings with root-mean-square errors of 0.11, 0.70, 1.69, and 2.47 ppm, respectively. At the same time, the Δ-machine learning approach is 1–2 orders of magnitude faster than the target large-basis calculations. It is also demonstrated that the machine learning model predicts experimental solution-phase NMR chemical shifts in drug molecules with only modestly worse accuracy than the target DFT model. Finally, the ability to estimate the uncertainty in the predicted shieldings based on variations within the ensemble of neural network models is also assessed.

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Quantum-chemical simulation of solid-state NMR spectra: the example of a molecular tweezer host–guest complex

Cited by 14 publications

References 61 publications

Molecular Tweezers with Varying Anions: A Comparative Study

Molecular Tweezers with Varying Anions: A Comparative Study

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions

Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning

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