There is a fundamental need for computational methods that can increase proteome coverage for in vivo studies of proteome dynamics using metabolic labeling with heavy water and LC-MS. Currently, only 30-40% of all quantified peptides are utilized to determine proteome dynamics, as the rest are filtered due to poor goodness- of-fit (Pearson correlation, residual sum of squares) between experimental data and its theoretical fit. The long- term goal is to develop methods for inferring the causative effects of protein turnover changes on the development of diseases. The objectives of this application are to develop computational methods to estimate protein turnover using abundances of only two mass isotopomers, estimate the number of exchangeable hydrogens in a peptide from three mass isotopomers, and use chromatogram alignment to quantify label incorporation into peptides whose elution profiles have not been sampled in MS2. Current methods for estimation of protein turnover use time-course of relative abundance (RA) depletion of the monoisotopic peak of a peptide, as determined from a normalization of the complete isotope profile of the peptide in MS1. Thus, only peptides identified in MS2 are used in quantification of label incorporation. Determination of the RA requires accurate quantification of all isotopomers (? six) of a peptide. In complex samples, contamination of at least one of the isotopomers by a co-eluting species is high. The model of deuterium incorporation into a peptide is dependent on the number of exchangeable hydrogens, NEH. NEH values have been accurately determined only for a mouse. Based on preliminary data, three specific aims will be pursued to resolve the methodological issues: 1) Develop, test, and validate bioinformatics solutions to determine degradation rate constant using two mass isotopomers; 2) Develop, test, and validate computational methods to estimate the number of exchangeable hydrogens from three mass isotopomers; and 3) Develop bioinformatics solutions to address the missing data problem in the presence of metabolic labeling. We derived two new equations relating the time-course of raw abundances of three mass isotopomers in metabolic labeling. The rationale for Aim 1 is that the equation relating the raw abundances of two mass isotopomers can be used to estimate label quantification from the raw abundances of only two mass isotopomers. The rationale for Aim 2 is that the two equations for three mass isotopomers can be used to estimate the NEH values.
Aim 3 uses mutual information between the chromatographic profiles to obtain a time-warping function. The rationale is that mutual information is a better- suited criterium for estimation of the non-linear relationship between profiles of a peptide at different timepoints of metabolic labeling.
Aims 1 and 3 will provide bioinformatics solutions that increase proteome coverage in heavy water labeling and LC-MS experiments. The implementation of Aim 2 will make it possible to apply the technology to systems with unknown amino acid NEH values.
Metabolic labeling with heavy water followed by LC-MS is a powerful technique to study protein turnover in vivo. Protein turnover is a fundamental biological process, which is dysregulated in many diseases, such as non-Alcoholic fatty liver disease (NAFLD), cystic fibrosis, and neurodegenerative diseases. This work will result in novel computational methods for increasing proteome coverage in the studies of protein turnover that will help to advance research on diagnostics of dysregulated proteostasis.
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