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

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

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

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

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

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

Comparison of experimental to fit parameters.

<p>Comparison of experimental to fit parameters.</p

Human Serum Albumin Domain I Fusion Protein for Antibody Conjugation

Bioorthogonal labeling of antibodies enables the conjugation of compounds, such as small molecules or peptides, which expand targeting capacity or enhance cytotoxicity. Taking advantage of a cyclohexene sulfonamide compound that site-selectively labels Lys64 in human serum albumin (HSA), we demonstrate that domain I of HSA can be used as a fusion protein for the preparation of antibody conjugates. Trastuzumab fusions were expressed at the N-terminus of the light chain or the C-terminus of the heavy chain enabling conjugation to small molecules. Moreover, these conjugates retained HER2 binding and proved to be highly stable in human plasma. Antibody conjugation via HSA domain I fusion should therefore have broad utility for making serum-stable antibody conjugates, particularly for antibody–drug conjugates