Abstract
Mass spectrometric sequencing of low abundance, integral membrane proteins, particularly the transmembrane domains, presents challenges that span the multiple phases of sample preparation including solubilization, purification, enzymatic digestion, peptide extraction, and chromatographic separation. We describe a method through which we have obtained high peptide coverage for 12 γ-aminobutyric acid type A receptor (GABAA receptor) subunits from 2 picomoles of affinity-purified GABAA receptors from rat brain neocortex. Focusing on the α1 subunit, we identified peptides covering 96% of the protein sequence from fragmentation spectra (MS2) using a database searching algorithm and deduced 80% of the amino acid residues in the protein from de novo sequencing of Orbitrap spectra. The workflow combined microscale membrane protein solubilization, protein delipidation, in-solution multi-enzyme digestion, multiple stationary phases for peptide extraction, and acquisition of high-resolution full scan and fragmentation spectra. For de novo sequencing of peptides containing the transmembrane domains, timed digestions with chymotrypsin were utilized to generate peptides with overlapping sequences that were then recovered by sequential solid phase extraction using a C4 followed by a porous graphitic carbon stationary phase. The specificity of peptide identifications and amino acid residue sequences was increased by high mass accuracy and charge state assignment to parent and fragment ions. Analysis of three separate brain samples demonstrated that 78% of the sequence of the α1 subunit was observed in all three replicates with an additional 13% covered in two of the three replicates, indicating a high degree of sequence coverage reproducibility. Label-free quantitative analysis was applied to the three replicates to determine the relative abundances of 11 γ-aminobutyric acid type A receptor subunits. The deep sequence MS data also revealed two N-glycosylation sites on the α1 subunit, confirmed two splice variants of the γ2 subunit (γ2L and γ2S) and resolved a database discrepancy in the sequence of the α5 subunit.
Highlights
From the ‡Departments of Anesthesiology, §Internal Medicine, ¶Cell Biology and Physiology, and ʈDevelopmental Biology, Washington University in St
The tissue sample used for receptor protein purification was the neocortex (1 g) from a single rat brain; P2 membranes from this sample contained ϳ20 picomoles of GABAA receptor as assessed by [3H]Ro15,4513 binding at a specific activity of ϳ1.5 picomoles/mg protein
Sequential Solid Phase Extraction With C4 and Porous Graphitic Carbon Increases Recovery of Peptides from Digests of the GABAA Receptor ␣1 Subunit—We investigated the recovery of peptides from the high-salt digests (e.g. 2 M urea) using sequential SPE with two stationary phases, C4 for hydrophobic peptides and porous graphitic carbon (PGC) for smaller and hydrophilic peptides
Summary
Zi-Wei Chen‡, Karoline Fuchs**, Werner Sieghart**, R. Mass Spectrometric Sequencing of GABAA Receptors of the proteome of various organisms and tissues [5] These advances include improvements in membrane isolation [4], the introduction of membrane “shaving” [6], the use of multiple enzymes in digestion [7], the use of mass spectrometry (MS)-compatible surfactants in protease digestion [8], delipidation of membrane proteins [9], in-solution rather than in-gel digestion [10], and the performance of liquid chromatography at elevated temperature to facilitate resolution of hydrophobic peptides [11]. Top-down mass spectrometry has been applied to obtaining full sequence coverage of several membrane proteins and identifying post-translational modifications [13] This method currently requires nmol quantities of purified protein and is currently limited to proteins ϳ Ͻ50 kDa, rendering its application to many native IMPs impractical. The data obtained enabled identification of N-glycosylation sites in the ␣1 subunit, a splice variant in the ␥2 subunit and provided de novo data to resolve different protein sequences of the ␣5 subunit in two protein databases
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