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.
Medical University of South Carolina, Department of Public Health Sciences, Charleston, South Carolina, United States of America.
John Christian G
JC
Spainhour
Division of Nephrology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America.
Michael G
MG
Janech
Scientific Research Corporation, North Charleston, South Carolina, United States of America.
John H
JH
Schwacke
Division of Nephrology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America; Ralph H. Johnson Veterans Affairs Medical Center, Charleston, South Carolina, United States of America.
Juan Carlos Q
JC
Velez
Medical University of South Carolina, Department of Public Health Sciences, Charleston, South Carolina, United States of America.
Viswanathan
V
Ramakrishnan
D015233:Models, Statistical; D016011:Normal Distribution; D010455:Peptides / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
D010455:Peptides