The ϳ230-residue C-terminal tail of the epidermal growth factor receptor (EGFR) is phosphorylated upon activation. We examined whether this phosphorylation is affected by deletions within the tail and whether the two tails in the asymmetric active EGFR dimer are phosphorylated differently. We monitored autophosphorylation in cells using flow cytometry and found that the first ϳ80 residues of the tail are inhibitory, as demonstrated previously. The entire ϳ80-residue span is important for autoinhibition and needs to be released from both kinases that form the dimer. These results are interpreted in terms of crystal structures of the inactive kinase domain, including two new ones presented here. Deletions in the remaining portion of the tail do not affect autophosphorylation, except for a six-residue segment spanning Tyr 1086 that is critical for activation loop phosphorylation. Phosphorylation of the two tails in the dimer is asymmetric, with the activator tail being phosphorylated somewhat more strongly. Unexpectedly, we found that reconstitution of the transmembrane and cytoplasmic domains of EGFR in vesicles leads to a peculiar phenomenon in which kinase domains appear to be trapped between stacks of lipid bilayers. This artifactual trapping of kinases between membranes enhances an intrinsic functional asymmetry in the two tails in a dimer.

Numerous experimental strategies exist for relative protein quantification, one of the primary objectives of mass spectrometry based proteomics analysis. These strategies mostly involve the incorporation of a stable isotope label via either metabolic incorporation in cell or tissue culture (¹⁵N/¹⁴N metabolic labeling, stable isotope labeling by amino acids in cell culture (SILAC)), chemical derivatization (ICAT, iTRAQ, TMT), or enzymatically catalyzed incorporation (¹⁸O labeling). Also, these techniques can be cost or time prohibitive or not amenable to the biological system of interest (i.e., metabolic labeling of clinical samples, most animals, or fungi). This is the case with the quantification of fungal proteomes, which often require auxotroph mutants to fully metabolically label. Alternatively, label-free strategies for protein quantification such as using integrated ion abundance and spectral counting have been demonstrated for quantification affording over 2 orders of magnitude of dynamic range which is comparable to metabolic labeling strategies. Direct comparisons of these quantitative techniques are largely lacking in the literature but are highly warranted in order to evaluate the capabilities, limitations, and analytical variability of available quantitative strategies. Here, we present the direct comparison of SILAC to label-free quantification by spectral counting of an identical set of data from the bottom-up proteomic analysis of human embryonic stem cells, which are readily able to be quantified using both strategies, finding that both strategies result in a similar number of protein identifications. We also discuss necessary constraints for accurate quantification using spectral counting and assess the potential of this label-free strategy as a viable alternative for quantitative proteomics.

Online liquid chromatography-mass spectrometric (LC-MS) analysis of intact proteins (i.e., top-down proteomics) is a growing area of research in the mass spectrometry community. A major advantage of top-down MS characterization of proteins is that the information of the intact protein is retained over the vastly more common bottom-up approach that uses protease-generated peptides to search genomic databases for protein identification. Concurrent to the emergence of top-down MS characterization of proteins has been the development and implementation of the stable isotope labeling of amino acids in cell culture (SILAC) method for relative quantification of proteins by LC-MS. Herein we describe the qualitative and quantitative top-down characterization of proteins derived from SILAC-labeled Aspergillus flavus using nanoflow reversed-phase liquid chromatography directly coupled to a linear ion trap Fourier transform ion cyclotron resonance mass spectrometer (nLC-LTQ-FTICR-MS). A. flavus is a toxic filamentous fungus that significantly impacts the agricultural economy and human health. SILAC labeling improved the confidence of protein identification, and we observed 1318 unique protein masses corresponding to 659 SILAC pairs, of which 22 were confidently identified. However, we have observed some limiting issues with regard to protein quantification using top-down MS/MS analyses of SILAC-labeled proteins. The role of SILAC labeling in the presence of competing endogenously produced amino acid residues and its impact on quantification of intact species are discussed in detail.

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Human embryonic stem cells (hESCs) are self-renewing pluripotent cells with relevance to treatment of numerous medical conditions. However, a global understanding of the role of the hESC proteome in maintaining pluripotency or triggering differentiation is still largely lacking. The emergence of top-down proteomics has facilitated the identification and characterization of intact protein forms that are not readily apparent in bottom-up studies. Combined with metabolic labeling techniques such as stable isotope labeling by amino acids in cell culture (SILAC), quantitative comparison of intact protein expression under differing experimental conditions is possible. Herein, quantitative top-down proteomics of hESCs is demonstrated using the SILAC method and nano-flow reverse phase chromatography directly coupled to a linear-ion-trap Fourier transform ion cyclotron resonance mass spectrometer (nLC-LTQ-FT-ICR-MS). In this study, which to the best of our knowledge represents the first top-down analysis of hESCs, we have confidently identified 11 proteins by accurate intact mass, MS/MS, and amino acid counting facilitated by SILAC labeling. Although quantification is challenging due to the incorporation of multiple labeled amino acids (i.e., lysine and arginine) and arginine to proline conversion, we are able to quantitatively account for these phenomena using a mathematical model.