Extending the Dynamic Range of Label-free Mass Spectrometric Quantification of Affinity Purifications

Abstract

Affinity purification (AP) of protein complexes combined with LC-MS/MS analysis is the current method of choice for identification of protein-protein interactions. Their interpretation with respect to significance, specificity, and selectivity requires quantification methods coping with enrichment factors of more than 1000-fold, variable amounts of total protein, and low abundant, unlabeled samples. We used standardized samples (0.1-1000 fmol) measured on high resolution hybrid linear ion trap instruments (LTQ-FT/Orbitrap) to characterize and improve linearity and dynamic range of label-free approaches. Quantification based on spectral counts was limited by saturation and ion suppression effects with samples exceeding 100 ng of protein, depending on the instrument setup. In contrast, signal intensities of peptides (peak volumes) selected by a novel correlation-based method (TopCorr-PV) were linear over at least 4 orders of magnitude and allowed for accurate relative quantification of standard proteins spiked into a complex protein background. Application of this procedure to APs of the voltage-gated potassium channel Kv1.1 as a model membrane protein complex unambiguously identified the whole set of known interaction partners together with novel candidates. In addition to discriminating these proteins from background, we could determine efficiency, cross-reactivities, and selection biases of the used purification antibodies. The enhanced dynamic range of the developed quantification procedure appears well suited for sensitive identification of specific protein-protein interactions, detection of antibody-related artifacts, and optimization of AP conditions. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111.007955, 1-12, 2012.Antibody-based affinity purification (AP) 1 of protein assemblies from biological samples followed by mass spectrometric analysis represents an increasingly popular approach for identification of protein-protein interactions (AP-MS) (1-3). Despite the exquisitely high and specific enrichment theoretically obtainable with antibodies (Abs), this approach faces a number of technical and intrinsic challenges in practice. Target protein complexes typically suffer from poor solubility, instability, and low abundance, particularly when associated with lipid membranes. Moreover, various antibody-related properties such as target selectivity, cross-reactivity, and interference with protein-protein interactions may lead to falsepositive and false-negative results (4). Finally, biological protein-protein interactions may have a more dynamic character, may depend on regulated modifications, or may involve rare protein partners. Together, these effects lead to a significant reduction of AP signal to noise, i.e. low co-enrichment efficiency of interaction partners and significant overlap with background or nonspecific proteins.Classically, AP specificity has been addressed by visualization of purified proteins on one-or two-dimensional gels and comparison of band patterns or spots with those obtained in...

Cryo-slicing Blue Native-Mass Spectrometry (csBN-MS), a Novel Technology for High Resolution Complexome Profiling

“…The filtered and re-scaled PVs for each peptide were then normalized to their maximum to yield relative peptide abundance profiles. Subsequently, these profiles were ranked by pairwise linear correlation (19), and the 50% most consistent peptide profiles were selected (Fig. 3B, 3rd panel).…”

“…3B, 3rd panel). Finally, the relative protein abundance value in each slice was calculated from the average of the selected normalized PV values over a window of three slices, and after least square fitting to the respective abundance norm values (19), the molecular abundance profile of NDUA4 was determined (Fig. 3B, 4th panel).…”

“…Protein Quantification-Label-free quantification of proteins was carried out following the principles described previously (19) with further optimizations to address the specific challenges arising from combining 230 complex datasets. Peptide signal intensities were integrated over time and m/z as peak volumes (PVs) from FT full scans using MaxQuant version 1.4.1.2 with integrated off-line mass calibration (20).…”

“…These were subsequently ranked for each protein by pairwise Pearson correlation (19). The average of at least two (one for the exception of proteins identified by one specific peptide (see under "Protein Identification")) and up to 6 or 50% (whichever value is greater) best correlating protein-specific peptide profiles over a window of three consecutive slices was used to calculate the relative abundance values of each protein profile.…”

“…The average of at least two (one for the exception of proteins identified by one specific peptide (see under "Protein Identification")) and up to 6 or 50% (whichever value is greater) best correlating protein-specific peptide profiles over a window of three consecutive slices was used to calculate the relative abundance values of each protein profile. Finally, these protein profiles were least squares-fitted to the normalized (molecular) abundance (abundance norm ) values (19) determined for the respective protein in the 230 datasets as a rough estimate of molecular abundance (supplemental table 3).…”