33. SHARED PEPTIDES IN MASS SPECTROMETRY BASED PROTEIN QUANTIFICATION
Department: Computer Science & Engineering
Faculty Advisor(s):
Vineet Bafna
Primary Student
Name: Banu Dost
Email: bdost@ucsd.edu
Phone: 858-534-8855
Grad Year: 2009
Abstract
In analyzing the proteome using mass spectrometry, the mass values help identify the molecules, and the intensities help quantify them. The relative abundance of a peptide across samples is a proxy for the relative abundance of the parent protein. This is acceptable only when the peptide sequence is unique to the protein. By contrast, when a peptide is shared across proteins (Ex: homologues and isoforms), its abundance (and relative abundance) depends upon contributions from multiple proteins. For this reason, shared peptides have been traditionally disregarded in protein-level quantification analysis. However, this may significantly decrease the number of proteins for which abundance estimates can be obtained. While often unreported, a significant portion of the data is ignored.
In this paper, we investigate the use of shared peptides which are ubiquitous (~50% of peptides) in mass spectrometric data-sets. In many cases, shared peptides can help compute the relative amounts of different proteins that share the same peptide. Also, proteins with no unique peptide in the sample can still be analyzed for relative abundance across samples. Our paper is the first attempt to use shared peptides in protein quantification, and makes use of combinatorial optimization to reduce the error in relative abundance measurements. We describe the topological and numerical properties required for robust estimates, and use them to improve our estimates for ill-conditioned systems. We also propose an extension to our approach for using shared peptides for detectability computation, and point to the importance of detectability values in extending the scope of shared peptide analysis in protein quantification.
Extensive simulations validate our approach even in the presence of experimental error. We apply our method to a model of Arabidopsis root-knot nematode infection, and elucidate the differential role of many protein family members in mediating host response to the pathogen.
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