A computer program for the prediction of 13-C-NMR chemical shifts of organic compounds

Fürst, Andràs; Pretsch, Ernö

doi:10.1016/s0003-2670(00)85105-3

Cited by 89 publications

(53 citation statements)

References 16 publications

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“…Detailed assignments are not available for this crowded spectral region. First, we assigned these peaks by calculating the chemical shifts using the additivity rules proposed for isotropic liquids (61)(62)(63)(64)(65). With such assignments, the measured jS CH j order parameters for DMPC lipids agreed well with solid-state 2 H-NMR-determined jS CD j values (43,112).…”

Section: Dipolar Order Parameters Reflect Structural and Dynamic Featmentioning

confidence: 63%

Area per Lipid and Cholesterol Interactions in Membranes from Separated Local-Field 13 C NMR Spectroscopy

Leftin

Molugu

Job

et al. 2014

Biophysical Journal

108

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Investigations of lipid membranes using NMR spectroscopy generally require isotopic labeling, often precluding structural studies of complex lipid systems. Solid-state (13)C magic-angle spinning NMR spectroscopy at natural isotopic abundance gives site-specific structural information that can aid in the characterization of complex biomembranes. Using the separated local-field experiment DROSS, we resolved (13)C-(1)H residual dipolar couplings that were interpreted with a statistical mean-torque model. Liquid-disordered and liquid-ordered phases were characterized according to membrane thickness and average cross-sectional area per lipid. Knowledge of such structural parameters is vital for molecular dynamics simulations, and provides information about the balance of forces in membrane lipid bilayers. Experiments were conducted with both phosphatidylcholine (dimyristoylphosphatidylcholine (DMPC) and palmitoyloleoylphosphatidylcholine (POPC)) and egg-yolk sphingomyelin (EYSM) lipids, and allowed us to extract segmental order parameters from the (13)C-(1)H residual dipolar couplings. Order parameters were used to calculate membrane structural quantities, including the area per lipid and bilayer thickness. Relative to POPC, EYSM is more ordered in the ld phase and experiences less structural perturbation upon adding 50% cholesterol to form the lo phase. The loss of configurational entropy is smaller for EYSM than for POPC, thus favoring its interaction with cholesterol in raftlike lipid systems. Our studies show that solid-state (13)C NMR spectroscopy is applicable to investigations of complex lipids and makes it possible to obtain structural parameters for biomembrane systems where isotope labeling may be prohibitive.

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Section: Dipolar Order Parameters Reflect Structural and Dynamic Featmentioning

confidence: 63%

Area per Lipid and Cholesterol Interactions in Membranes from Separated Local-Field 13 C NMR Spectroscopy

Leftin

Molugu

Job

et al. 2014

Biophysical Journal

108

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“…Comparison of the plots and statistical parameters calculated for the examined methods shows that all three models are characterized by acceptable quality. However, both visual inspection and comparison of the linear regression statistical terms shows www.interscience.wiley.com/journal/mrc 2 , CH and quaternary carbons. To extract statistical significance from the analyzed parameters, atom types for which there were less than 50 representatives in the dataset were excluded from consideration.…”

Section: Statistical Comparison Of Methodsmentioning

confidence: 99%

Empirical and DFT GIAO quantum‐mechanical methods of ¹³C chemical shifts prediction: competitors or collaborators?

Elyashberg¹,

Blinov²,

Smurnyy³

et al. 2010

Magnetic Reson in Chemistry

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The accuracy of (13)C chemical shift prediction by both DFT GIAO quantum-mechanical (QM) and empirical methods was compared using 205 structures for which experimental and QM-calculated chemical shifts were published in the literature. For these structures, (13)C chemical shifts were calculated using HOSE code and neural network (NN) algorithms developed within our laboratory. In total, 2531 chemical shifts were analyzed and statistically processed. It has been shown that, in general, QM methods are capable of providing similar but inferior accuracy to the empirical approaches, but quite frequently they give larger mean average error values. For the structural set examined in this work, the following mean absolute errors (MAEs) were found: MAE(HOSE) = 1.58 ppm, MAE(NN) = 1.91 ppm and MAE(QM) = 3.29 ppm. A strategy of combined application of both the empirical and DFT GIAO approaches is suggested. The strategy could provide a synergistic effect if the advantages intrinsic to each method are exploited.

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“…In this work, only 1D 1 H NMR spectra were used, but an identical system exists for 1D 13 C NMR spectra. A discussion of 2D NMR verification system design and accuracy will be covered in a future publication.…”

Section: Discussionmentioning

confidence: 99%

“…The rest of the molecule is treated as substituents associated with each of the substructures. 13 Estimates have been given 11 that the NMR shift locations can be predicted to within 0.3 ppm accuracy. However, for structures where no or few additivity rules are available, the technique suffers.…”

Section: Introductionmentioning

confidence: 99%

“…To support the increasing structural diversity of today's chemistry, it is also possible to populate user databases with additional chemical structures and nuclear assignments; thereby expanding the structural diversity supported by the prediction algorithms. This HOSE code method is available in a number of commercially available software packages for NMR shift prediction, including Sadtler's Know-It-All package, 20 Chemical Concepts' SpecInfo, 21 and Advanced Chemistry Developments' (ACD/Labs) ACD/NMR Predictors for 1 H, 13 C, 15 N, 19 F and 31 P nuclei. 22 Griffiths and Bright published their method for structure validation 23 utilizing the chemical shift prediction software ACD/HNMR Predictor (Version 4.0).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated structure verification based on ¹H NMR prediction

Golotvin¹,

Vodopianov²,

Lefebvre³

et al. 2006

Magnetic Reson in Chemistry

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A unique opportunity exists when an experimental NMR spectrum is obtained for which a specific chemical structure is anticipated. A process of Verification--the confirmation of a postulated structure--is now possible, as opposed to Elucidation-the de novo determination of a structure. A method for automated structure verification is suggested, which compares the chemical shifts, intensities and multiplicities of signals in an experimental 1H NMR spectrum with those from a predicted spectrum for the proposed structure. A match factor (MF) is produced and used to classify the spectrum-structure match into one of three categories, correct, ambiguous, or incorrect. The verification result is also augmented by the spectrum assignment obtained as part of the verification process. This method was tested on a set of synthetic spectra and several sets of experimental spectra, all of which were automatically prepared from raw data. Taking into account even the most problematic structures, with many labile protons present and poor prediction accuracy, 50% of all spectra can still be automatically verified without any false positives or negatives. In a blind test on a typical set of data, it is shown that fewer than 31% of the structures would need manual evaluation. This means that a system is possible whereby 69% of the spectra are prepared and evaluated automatically, and never need to be seen or evaluated by a human.

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A computer program for the prediction of 13-C-NMR chemical shifts of organic compounds

Cited by 89 publications

References 16 publications

Area per Lipid and Cholesterol Interactions in Membranes from Separated Local-Field 13 C NMR Spectroscopy

Area per Lipid and Cholesterol Interactions in Membranes from Separated Local-Field 13 C NMR Spectroscopy

Empirical and DFT GIAO quantum‐mechanical methods of ¹³C chemical shifts prediction: competitors or collaborators?

Automated structure verification based on ¹H NMR prediction

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A computer program for the prediction of 13-C-NMR chemical shifts of organic compounds

Cited by 89 publications

References 16 publications

Area per Lipid and Cholesterol Interactions in Membranes from Separated Local-Field 13 C NMR Spectroscopy

Area per Lipid and Cholesterol Interactions in Membranes from Separated Local-Field 13 C NMR Spectroscopy

Empirical and DFT GIAO quantum‐mechanical methods of 13C chemical shifts prediction: competitors or collaborators?

Automated structure verification based on 1H NMR prediction

Contact Info

Product

Resources

About

Empirical and DFT GIAO quantum‐mechanical methods of ¹³C chemical shifts prediction: competitors or collaborators?

Automated structure verification based on ¹H NMR prediction