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Using self assembling amphiphilic nanomaterials to selectively extract and concentrate peptides for analysis by mass spectrometry
Abstract
Very selective and highly sensitive ways to detect peptides or proteins of interest remain important goals in proteomics applications. This dissertation focuses on the use of amphiphilic polymeric materials that self-assemble as reverse micelles in apolar solvents as part of a liquid-liquid extraction methodology to selectively extract and concentrate peptides from aqueous solutions. After extraction the polymer-peptide mixtures are amenable to direct analysis by mass spectrometry (MS) using matrix-assisted laser desorption/ionization (MALDI). The polymeric materials that self-assemble in apolar solvents have charged interiors that allow oppositely-charged peptides to readily migrate into the aggregate's core, while similarly-charged peptides do not. This charge complementarity can be controlled by varying aqueous phase pH such that peptides having a narrow range of a pI values can be selectively isolated. Insights into the extraction mechanism and how these materials can potentially open the way for multidimensional separations were also studied. Furthermore, amino acid-specific covalent labels are introduced inside the reverse micelles to selectively react with the extracted peptides having the amino acid of interest. This reaction leads to a mass signature that can be used to identify peptides with amino acids of interest. In addition, bulk ampholytic gels and small commercial surfactants were investigated as extraction materials. Overall, our results reveal that the amphiphilic polymeric reverse micelles are far superior for selectively and efficiently extracting peptides from complex mixtures.
Subject Area
Analytical chemistry|Biochemistry|Polymer chemistry
Recommended Citation
Gomez-Escudero, Andrea, "Using self assembling amphiphilic nanomaterials to selectively extract and concentrate peptides for analysis by mass spectrometry" (2010). Doctoral Dissertations Available from Proquest. AAI3427532.
https://scholarworks.umass.edu/dissertations/AAI3427532