May 2008

Document Type


Degree Name



Dept. of Medical Informatics and Clinical Epidemiology


Oregon Health & Science University


Reliable identification of deamidation remains challenging when using low-resolution mass spectrometry due to the MS/MS mis-triggers on first isotopic peak of unmodified peptide and limited mass resolution. We developed a bioinformatics method that utilizes differential chromatographic behavior and corrected peptide masses of the amidated and deamidated peptides to validate deamidation identifications. The method has been automated to facilitate rapid validation of deamidation identifications in large-scale proteomics experiments. Accurate quantification of deamidation is necessary for reliable protein pharmaceutical shelf life measurements, minimizing deamidation in sample processing/purification methods, and differentiating between healthy and diseased tissue in protein aggregation diseases. Deamidation quantification is complicated by coelution of modified and unmodified peptide forms, interference from other peptides with similar elution times, and poor chromatography peak shapes. We developed a robust mathematical quantification technique that uses Gaussian isotopic envelope modeling with peptide mixture models. This method estimates the abundance of deamidation by comparing the predicted isotopic envelope of deamidated and amidated peptide forms to the experimentally measured isotopic envelope. The technique has undergone extensive manual validation during development and a semi-automated graphical user interface has been designed to estimate the abundance of deamidations in large-scale proteomics experiments. We have applied these methods to estimate deamidation abundances in an aged series of human lens tissues. Increased deamidation abundance at three sites in gS lens crystallin is correlated with increasing age, loss of protein solubility, and changes in 3-dimensional protein structure.




School of Medicine



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