A study of the properties of Gaussian mixture model for stable isotope standard quantification in MALDI-TOF MS.

The quantification of peptides in Matrix assisted laser desorption/ionization time-of-flight mass spectrum analysis coupled with stable isotope standards has been used to quantify native peptides under many experimental conditions. This approach has difficulties quantifying samples containing peptides with ion currents in overlapping (convolved) spectra. In a previous article we proposed a reparametrized Gaussian mixture model based on the known characteristics of the peptides that could also accommodate overlapping spectra. We demonstrated the application of our model in a series of single and overlapping peptides quantification experiments. Here, we focus solely on studying the properties of our approach and examine the characteristics of the GMM approach in convolved peptides using simulated spectra and provide a method for simulating these spectra.

The application of Gaussian mixture models for signal quantification in MALDI-TOF mass spectrometry of peptides.

Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) coupled with stable isotope standards (SIS) has been used to quantify native peptides. This peptide quantification by MALDI-TOF approach has difficulties quantifying samples containing peptides with ion currents in overlapping spectra. In these overlapping spectra the currents sum together, which modify the peak heights and make normal SIS estimation problematic. An approach using Gaussian mixtures based on known physical constants to model the isotopic cluster of a known compound is proposed here. The characteristics of this approach are examined for single and overlapping compounds. The approach is compared to two commonly used SIS quantification methods for single compound, namely Peak Intensity method and Riemann sum area under the curve (AUC) method. For studying the characteristics of the Gaussian mixture method, Angiotensin II, Angiotensin-2-10, and Angiotenisn-1-9 and their associated SIS peptides were used. The findings suggest, Gaussian mixture method has similar characteristics as the two methods compared for estimating the quantity of isolated isotopic clusters for single compounds. All three methods were tested using MALDI-TOF mass spectra collected for peptides of the renin-angiotensin system. The Gaussian mixture method accurately estimated the native to labeled ratio of several isolated angiotensin peptides (5.2% error in ratio estimation) with similar estimation errors to those calculated using peak intensity and Riemann sum AUC methods (5.9% and 7.7%, respectively). For overlapping angiotensin peptides, (where the other two methods are not applicable) the estimation error of the Gaussian mixture was 6.8%, which is within the acceptable range. In summary, for single compounds the Gaussian mixture method is equivalent or marginally superior compared to the existing methods of peptide quantification and is capable of quantifying overlapping (convolved) peptides within the acceptable margin of error.

Network modeling reveals steps in angiotensin peptide processing.

New insights into the intrarenal renin-angiotensin (Ang) system have modified our traditional view of the system. However, many finer details of this network of peptides and associated peptidases remain unclear. We hypothesized that a computational systems biology approach, applied to peptidomic data, could help to unravel the network of enzymatic conversions. We built and refined a Bayesian network model and a dynamic systems model starting from a skeleton created with established elements of the renin-Ang system and further developed it with archived matrix-assisted laser desorption ionization-time of flight mass spectra from experiments conducted in mouse podocytes exposed to exogenous Ang substrates. The model-building process suggested previously unrecognized steps, 3 of which were confirmed in vitro, including the conversion of Ang(2-10) to Ang(2-7) by neprilysin, Ang(1-9) to Ang(2-9), and Ang(1-7) to Ang(2-7) by aminopeptidase A. These data suggest a wider role of neprilysin and aminopeptidase A in glomerular formation of bioactive Ang peptides and shunting their formation. Other steps were also suggested by the model, and supporting evidence for those steps was evaluated using model-comparison methods. Our results demonstrate that systems biology methods applied to peptidomic data are effective in identifying novel steps in the Ang peptide processing network, and these findings improve our understanding of the glomerular renin-Ang system.