Sample simplification through affinity selection in proteomics

Diya Ren, Purdue University

Abstract

In “bottom up” approach in proteomics, it is always challenging to analyze the large number of peptides generated by protein digestion. Affinity selection of specific peptides provides a quick way to reduce sample complexity to match the analytical capacity of the system. In this thesis, agarose based immobilized metal affinity chromatography (IMAC) columns loaded with copper (II) were evaluated for the selection of histidine-containing peptides in comparative proteomics. Recovery, binding specificity, and reproducibility were investigated with the model proteins, apo-transferrin and β-galactosidase. The study showed that Cu(II)-IMAC is a robust sample simplification method with little non specific binding and reasonable column longevity. In addition, it was found out that the selectivity could be tuned by the eluting condition, salt concentration, peptide N-terminus acylation, and type of sorbent. As an application, Cu(II)-IMAC in tandem with reversed phase chromatography RPC) was applied to enrich histidine-rich peptides and to quantify a yeast protein extract at two fermentation times. An inverse labeling strategy was applied to increase reliability in determining upand down-regulation. Two other methods were investigated as well. One was strong anion exchange chromatography coupled with Cu(II)-IMAC. Another was strong cation exchange of cysteine derivatized peptides. Due to the fact that most tryptic peptides carry a negative charge at neutral pH, cysteine residues were derivatized with a quaternary amine tag (QAT) and selected by strong cation exchange chromatography. An advantage of the method is that it enriched QAT derivatized cysteinyl peptides. Compared with other cysteine selection methods, this approach provides a simple, fast and robust way to facilitate enrichment cysteinyl peptide.

Degree

Ph.D.

Advisors

Regnier, Purdue University.

Subject Area

Analytical chemistry

COinS