Analysis of Monoglycerides, Diglycerides, Sterols, and Free Fatty Acids in Coconut (Cocos nucifera L.) Oil by 31P NMR Spectroscopy

Phosphorus-31 nuclear magnetic resonance spectroscopy (31P NMR) was used to differentiate virgin coconut oil (VCO) from refined, bleached, deodorized coconut oil (RCO). Monoglycerides (MGs), diglycerides (DGs), sterols, and free fatty acids (FFAs) in VCO and RCO were converted into dioxaphospholane derivatives and analyzed by 31P NMR. On the average, 1-MG was found to be higher in VCO (0.027%) than RCO (0.019%). 2-MG was not detected in any of the samples down to a detection limit of 0.014%. On the average, total DGs were lower in VCO (1.55%) than RCO (4.10%). When plotted in terms of the ratio [1,2-DG/total DGs] versus total DGs, VCO and RCO samples grouped separately. Total sterols were higher in VCO (0.096%) compared with RCO (0.032%), and the FFA content was 8 times higher in VCO than RCO (0.127% vs 0.015%). FFA determination by 31P NMR and titration gave comparable results. Principal components analysis shows that the 1,2-DG, 1,3-DG, and FFAs are the most important parameters for differentiating VCO from RCO

Correlating Conformational Shift Induction with Altered Inhibitor Potency in a Multidrug Resistant HIV‑1 Protease Variant

Inhibitor-induced conformational ensemble shifts in a multidrug resistant HIV-1 protease variant, MDR769, are characterized by site-directed spin labeling double electron–electron resonance spectroscopy. For MDR769 compared to the native enzyme, changes in inhibitor IC₅₀ values are related to a parameter defined as |Δ<i>C</i>|, which is the relative change in the inhibitor-induced shift to the closed state. Specifically, a linear correlation is found between |Δ<i>C</i>| and the magnitude of the change in IC₅₀, provided that inhibitor binding is not too weak. Moreover, inhibitors that exhibit MDR769 resistance no longer induce a strong shift to a closed conformational ensemble as seen previously in the native enzyme

Schematic outline of the fitting protocol adopted by <i>decon1d</i>.

<p>Schematic outline of the fitting protocol adopted by <i>decon1d</i>.</p

Deconvolution of Complex 1D NMR Spectra Using Objective Model Selection

<div><p>Fluorine (¹⁹F) NMR has emerged as a useful tool for characterization of slow dynamics in ¹⁹F-labeled proteins. One-dimensional (1D) ¹⁹F NMR spectra of proteins can be broad, irregular and complex, due to exchange of probe nuclei between distinct electrostatic environments; and therefore cannot be deconvoluted and analyzed in an objective way using currently available software. We have developed a Python-based deconvolution program, <i>decon1d</i>, which uses Bayesian information criteria (BIC) to objectively determine which model (number of peaks) would most likely produce the experimentally obtained data. The method also allows for fitting of intermediate exchange spectra, which is not supported by current software in the absence of a specific kinetic model. In current methods, determination of the deconvolution model best supported by the data is done manually through comparison of residual error values, which can be time consuming and requires model selection by the user. In contrast, the BIC method used by <i>decond1d</i> provides a quantitative method for model comparison that penalizes for model complexity helping to prevent over-fitting of the data and allows identification of the most parsimonious model. The <i>decon1d</i> program is freely available as a downloadable Python script at the project website (<a href="https://github.com/hughests/decon1d/" target="_blank">https://github.com/hughests/decon1d/</a>).</p></div

Fit of real experimental data.

<p>The ligand binding domain of PPARγ C285S/K474C was treated with BTFA and then NMR was performed at 298K. Deconvolution of the ¹⁹F NMR signal was carried out using the indicated programs. In each case the difference between the data and the fit (residual error) is shown in grey and the sum of individual fitted peaks is shown in green.</p

Fitting of simulated spectra with <i>decon1d</i>.

<p>a-d) Non-exchange broadened spectra of varying signal-to-noise ratio and number, width, frequency and height of component peaks were simulated (top row). <i>decon1d</i> was then used to fit these simulated spectra allowing for either fixed phase (middle row) or variable phase (bottom row). The color of the component peaks identified in each fit serves as a visual aid for comparisons between fits as it identifies the approximate chemical shift of the peak center, indicated by the colored bar on the bottom. The difference between the data and the fit (residual error) is shown in grey and the sum of individual fitted peaks is shown in green. An alternate deconvolution of the variable phase fit for column c is displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134474#pone.0134474.s006" target="_blank">S5 Fig</a>.</p

Intermediate exchange data are well fit by <i>decon1d</i>.

<p>Data were simulated using LineShapeKin and fit using <i>decon1d</i>. a) The spectrum from a single nucleus exchanging between two chemical shift environments was simulated with near equal populations (left panel; 48%:52%) and skewed populations (right panel; 25%:75%) at varying exchange rate to chemical shift difference values (k_ex/Δδ, displayed numbers) and the best model of the component spectral lines was determined by <i>decon1d</i>. Vertical gray dashed lines indicate the true chemical shifts in the absence of exchange. b) Fitted parameters from the models in panel a. c) Simulated spectra from a single nucleus exchanging between four chemical shift environments with similar populations at varying k_ex/Δδ values (displayed numbers) and the best model as determined by <i>decon1d</i>. The difference between the data and the fit (residual error) is shown in grey and the sum of individual fitted peaks is shown in green.</p

Schematic outline of the fitting protocol adopted by <i>decon1d</i>.

<p>Schematic outline of the fitting protocol adopted by <i>decon1d</i>.</p

Out-of-phase data are well fit by <i>decon1d</i>.

<p>Simulated data (input) were fit with <i>decon1d</i> allowing the phase to vary (model). a) The input and fit models are nearly identical for these incorrectly phased simulated spectra. b) The fractional population, center, full width at half maximum peak height (FWHM) and phase of the simulated spectrum (dashed and solid lines) and the fits (colored dots) were graphed as a function of the phase of the simulated spectrum (x-axis). In general these fits are not adversely affected by poor phasing. The difference between the data and the fit (residual error) is shown in grey and the sum of individual fitted peaks is shown in green.</p

Lower signal-to-noise ratio leads to decreased peak assignment.

<p>a-e) Fits of simulated data with signal-to-noise ratio: a) 5 b) 10, c) 25, d) 75 and e) 244. f) Input simulated NMR spectra showing the true underlying peaks that make up the spectra. Signal-to-noise was calculated from the highest signal value divided by the root mean square value of the noise in a region devoid of signal. An alternate fit of the lowest signal to noise data (panel a) was found with a BIC value 4.67 higher than the model shown, with the only substantial difference being that the prediction of rightmost peak chemical shift is -3.58 ppm (not shown) rather than -3.12 ppm (shown).</p