Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry

Shotgun proteome analysis platforms based on multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) provide a powerful means to discover biomarker candidates in tissue specimens. Analysis platforms must balance sensitivity for peptide detection, reproducibility of detected peptide inventories and analytical throughput for protein amounts commonly present in tissue biospecimens (< 100 microg), such that platform stability is sufficient to detect modest changes in complex proteomes. We compared shotgun proteomics platforms by analyzing tryptic digests of whole cell and tissue proteomes using strong cation exchange (SCX) and isoelectric focusing (IEF) separations of peptides prior to LC-MS/MS analysis on a LTQ-Orbitrap hybrid instrument. IEF separations provided superior reproducibility and resolution for peptide fractionation from samples corresponding to both large (100 microg) and small (10 microg) protein inputs. SCX generated more peptide and protein identifications than did IEF with small (10 microg) samples, whereas the two platforms yielded similar numbers of identifications with large (100 microg) samples. In nine replicate analyses of tryptic peptides from 50 microg colon adenocarcinoma protein, overlap in protein detection by the two platforms was 77% of all proteins detected by both methods combined. IEF more quickly approached maximal detection, with 90% of IEF-detectable medium abundance proteins (those detected with a total of 3-4 peptides) detected within three replicate analyses. In contrast, the SCX platform required six replicates to detect 90% of SCX-detectable medium abundance proteins. High reproducibility and efficient resolution of IEF peptide separations make the IEF platform superior to the SCX platform for biomarker discovery via shotgun proteomic analyses of tissue specimens.     

Comparison of alternative analytical techniques for the characterisation of the human serum proteome in HUPO Plasma Proteome Project

Based on the same HUPO reference specimen (C1-serum) with the six proteins of highest abundance depleted by immunoaffinity chromatography, we have compared five proteomics approaches, which were (1) intact protein fractionation by anion-exchange chromatography followed by 2-DE-MALDI-TOF-MS/MS for protein identification (2-DE strategy); (2) intact protein fractionation by 2-D HPLC followed by tryptic digestion of each fraction and microcapillary RP-HPLC/microESI-MS/MS identification (protein 2-D HPLC fractionation strategy); (3) protein digestion followed by automated online microcapillary 2-D HPLC (strong cation-exchange chromatography (SCX)-RPC) with IT microESI-MS/MS; (online shotgun strategy); (4) same as (3) with the SCX step performed offline (offline shotgun strategy) and (5) same as (4) with the SCX fractions reanalysed by optimised nanoRP-HPLC-nanoESI-MS/MS (offline shotgun-nanospray strategy). All five approaches yielded complementary sets of protein identifications. The total number of unique proteins identified by each of these five approaches was (1) 78, (2) 179, (3) 131, (4) 224 and (5) 330 respectively. In all, 560 unique proteins were identified. One hundred and sixty-five proteins were identified through two or more peptides, which could be considered a high-confidence identification. Only 37 proteins were identified by all five approaches. The 2-DE approach yielded more information on the pI-altered isoforms of some serum proteins and the relative abundance of identified proteins. The protein prefractionation strategy slightly improved the capacity to detect proteins of lower abundance. Optimising the separation at the peptide level and improving the detection sensitivity of ESI-MS/MS were more effective than fractionation of intact proteins in increasing the total number of proteins identified. Overall, electrophoresis and chromatography, coupled respectively with MALDI-TOF/TOF-MS and ESI-MS/MS, identified complementary sets of serum proteins.     

Identification and quantification of basic and acidic proteins using solution-based two-dimensional protein fractionation and label-free or 18O-labeling mass spectrometry

Reduction in sample complexity enables more thorough proteomic analysis using mass spectrometry (MS). A solution-based two-dimensional (2D) protein fractionation system, ProteomeLab PF 2D, has recently become available for sample fractionation and complexity reduction. PF 2D resolves proteins by isoelectric point (pI) and hydrophobicity in the first and second dimensions, respectively. It offers distinctive advantages over 2D gel electrophoresis with respects to automation of the fractionation processes and characterization of proteins having extreme pIs. Besides fractionation, PF 2D is equipped with built-in UV detectors intended for relative quantification of proteins in contrasting samples using its software tools. In this study, we utilized PF 2D for the identification of basic and acidic proteins in mammalian cells, which are generally under-characterized. In addition, mass spectrometric methods (label-free and 18O-labeling) were employed to complement protein quantification based on UV absorbance. Our studies indicate that the selection of chromatographic fractions could impact protein identification and that the UV-based quantification for contrasting complex proteomes is constrained by coelution or partial coelution of proteins. In contrast, the quantification post PF 2D chromatography based on label-free or 18O-labeling mass spectrometry provides an alternative platform for basic/acidic protein identification and quantification. With the use of HCT116 colon carcinoma cells, a total of 305 basic and 183 acidic proteins was identified. Quantitative proteomics revealed that 17 of these proteins were differentially expressed in HCT116 p53-/- cells.     

Towards multidimensional liquid chromatography separation of proteins using fluorescence and isotope-coded protein labelling for quantitative proteomics

HPLC has emerged as a valuable tool for separating proteins. To address the analysis of complex proteomes and quantitative changes of proteins therein, we developed a multidimensional LC (MDLC)-based approach followed by large gel 1-D SDS-PAGE. Here we present a novel strategy that allows for simultaneously identifying and quantifying differentially regulated proteins following three separation and fractionation steps. This MDLC platform integrates advantages of dual protein labelling using both fluorescence and isotope-coded tags for subsequent detection and quantification of abundance ratios of proteins by MS.     

Implications of 15N-metabolic labeling for automated peptide identification in Arabidopsis thaliana

We report the first metabolic labeling of Arabidopsis thaliana for proteomic investigation, demonstrating efficient and complete labeling of intact plants. Using a reversed-database strategy, we evaluate the performance of the MASCOT search engine in the analysis of combined natural abundance and 15N-labeled samples. We find that 15N-metabolic labeling appears to increase the ambiguity associated with peptide identifications due in part to changes in the number of isobaric amino acids when the isotopic label is introduced. This is reflected by changes in the distributions of false positive identifications with respect to MASCOT score. However, by determining the nitrogen count from each pair of labeled and unlabeled peptides we may improve our confidence in both heavy and light identifications.     

MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines

Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.     

Performance of five different electrospray ionisation sources in conjunction with rapid monolithic column liquid chromatography and fast MS/MS scanning

The performances of five different ESI sources coupled to a polystyrene–divinylbenzene monolithic column were compared in a series of LC‐ESI‐MS/MS analyses of Escherichia coli outer membrane proteins. The sources selected for comparison included two different modifications of the standard electrospray source, a commercial low‐flow sprayer, a stainless steel nanospray needle and a coated glass Picotip. Respective performances were judged on sensitivity and the number and reproducibility of significant protein identifications obtained through the analysis of multiple identical samples. Data quality varied between that of a ground silica capillary, with 160 total protein identifications, the lowest number of high quality peptide hits obtained (3012), and generally peaks of lower intensity; and a stainless steel nanospray needle, which resulted in increased precursor ion abundance, the highest‐quality peptide fragmentation spectra (5414) and greatest number of total protein identifications (259) exhibiting the highest MASCOT scores (average increase in score of 27.5% per identified protein). The data presented show that, despite increased variability in comparative ion intensity, the stainless steel nanospray needle provides the highest overall sensitivity. However, the resulting data were less reproducible in terms of proteins identified in complex mixtures – arguably due to an increased number of high intensity precursor ion candidates.     

Accurate mass spectrometry based protein quantification via shared peptides

In mass spectrometry-based protein quantification, peptides that are shared across different protein sequences are often discarded as being uninformative with respect to each of the parent proteins. We investigate the use of shared peptides which are ubiquitous (~50% of peptides) in mass spectrometric data-sets for accurate protein identification and quantification. Different from existing approaches, we show how shared peptides can help compute the relative amounts of the proteins that contain them. Also, proteins with no unique peptide in the sample can still be analyzed for relative abundance. Our article uses shared peptides in protein quantification and makes use of combinatorial optimization to reduce the error in relative abundance measurements. We describe the topological and numerical properties required for robust estimates, and use them to improve our estimates for ill-conditioned systems. Extensive simulations validate our approach even in the presence of experimental error. We apply our method to a model of Arabidopsis thaliana root knot nematode infection, and investigate the differential role of several protein family members in mediating host response to the pathogen.     

A new versatile peptide-based size exclusion chromatography platform for global profiling and quantitation of candidate biomarkers in hepatocellular carcinoma specimens

Disease biomarkers are predicted to be in low abundance; thus, the most crucial step of biomarker discovery is the efficient fractionation of clinical samples into protein sets that define disease stages and/or predict disease development. For this purpose, we developed a new platform that uses peptide-based size exclusion chromatography (pep-SEC) to quantify disease biomarker candidates. This new platform has many advantages over previously described biomarker profiling platforms, including short run time, high resolution, and good reproducibility, which make it suitable for large-scale analysis. We combined this platform with isotope labeling and label-free methods to identify and quantitate differentially expressed proteins in hepatocellular carcinoma (HCC) tissues. When we combined pep-SEC with a gas phase fractionation method, which broadens precursor ion selection, the protein coverage was significantly increased, which is critical for the global profiling of HCC specimens. Furthermore, pep-SEC-LC-MS/MS analysis enhanced the detection of low-abundance proteins (e.g. insulin receptor substrate 2 and carboxylesterase 1) and glycopeptides in HCC plasma. Thus, our pep-SEC platform is an efficient and versatile pre-fractionation system for the large-scale profiling and quantitation of candidate biomarkers in complex disease proteomes.     

Comparison of fractionation proteomics for local SWATH library building

For data‐independent acquisition by means of sequential window acquisition of all theoretical fragment ion spectra (SWATH), a reference library of data‐dependent acquisition (DDA) runs is typically used to correlate the quantitative data from the fragment ion spectra with peptide identifications. The quality and coverage of such a reference library is therefore essential when processing SWATH data. In general, library sizes can be increased by reducing the impact of DDA precursor selection with replicate runs or fractionation. However, these strategies can affect the match between the library and SWATH measurement, and thus larger library sizes do not necessarily correspond to improved SWATH quantification. Here, three fractionation strategies to increase local library size were compared to standard library building using replicate DDA injection: protein SDS‐PAGE fractionation, peptide high‐pH RP‐HPLC fractionation and MS‐acquisition gas phase fractionation. The impact of these libraries on SWATH performance was evaluated in terms of the number of extracted peptides and proteins, the match quality of the peptides and the extraction reproducibility of the transitions. These analyses were conducted using the hydrophilic proteome of differentiating human embryonic stem cells. Our results show that SWATH quantitative results and interpretations are affected by choice of fractionation technique. Data are available via ProteomeXchange with identifier PXD006190.     

In-gel isoelectric focusing of peptides as a tool for improved protein identification

In the analysis of proteins in complex samples, pre-fractionation is imperative to obtain the necessary depth in the number of reliable protein identifications by mass spectrometry. Here we explore isoelectric focusing of peptides (peptide IEF) as an effective fractionation step that at the same time provides the added possibility to eliminate spurious peptide identifications by filtering for pI. Peptide IEF in IPG strips is fast and sharply confines peptides to their pI. We have evaluated systematically the contribution of pI filtering and accurate mass measurements on the total number of protein identifications in a complex protein mixture (Drosophila nuclear extract). At the same time, by varying Mascot identification cutoff scores, we have monitored the false positive rate among these identifications by searching reverse protein databases. From mass spectrometric analyses at low mass accuracy using an LTQ ion trap, false positive rates can be minimized by filtering of peptides not focusing at their expected pI. Analyses using an LTQ-FT mass spectrometer delivers low false positive rates by itself due to the high mass accuracy. In a direct comparison of peptide IEF with SDS-PAGE as a pre-fractionation step, IEF delivered 25% and 43% more proteins when identified using FT-MS and LTQ-MS, respectively. Cumulatively, 2190 non redundant proteins were identified in the Drosophila nuclear extract at a false positive rate of 0.5%. Of these, 1751 proteins (80%) were identified after peptide IEF and FT-MS alone. Overall, we show that peptide IEF allows to increase the confidence level of protein identifications, and is more sensitive than SDS-PAGE.     

A systematic analysis of the effects of increasing degrees of serum immunodepletion in terms of depth of coverage and other key aspects in top-down and bottom-up proteomic analyses

Immunodepletion of clinical fluids to overcome the dominance by a few very abundant proteins has been explored but studies are few, commonly examining only limited aspects with one analytical platform. We have systematically compared immunodepletion of 6, 14, or 20 proteins using serum from renal transplant patients, analysing reproducibility, depth of coverage, efficiency, and specificity using 2-D DIGE ('top-down') and LC-MS/MS ('bottom-up'). A progressive increase in protein number (≥2 unique peptides) was found from 159 in unfractionated serum to 301 following 20 protein depletion using a relatively high-throughput 1-D-LC-MS/MS approach, including known biomarkers and moderate-lower abundance proteins such as NGAL and cytokine/growth factor receptors. On the contrary, readout by 2-D DIGE demonstrated good reproducibility of immunodepletion, but additional proteins seen tended to be isoforms of existing proteins. Depletion of 14 or 20 proteins followed by LC-MS/MS showed excellent reproducibility of proteins detected and a significant overlap between columns. Using label-free analysis, greater run-to-run variability was seen with the Prot20 column compared with the MARS14 column (median %CVs of 30.9 versus 18.2%, respectively) and a corresponding wider precision profile for the Prot20. These results illustrate the potential of immunodepletion followed by 1-D nano-LC-LTQ Orbitrap Velos analysis in a moderate through-put biomarker discovery process.     

Depth of proteome issues: a yeast isotope-coded affinity tag reagent study

As a test case for optimizing how to perform proteomics experiments, we chose a yeast model system in which the UPF1 gene, a protein involved in nonsense-mediated mRNA decay, was knocked out by homologous recombination. The results from five complete isotope-coded affinity tag (ICAT) experiments were combined, two using matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS) and three using electrospray MS/MS. We sought to assess the reproducibility of peptide identification and to develop an informatics structure that characterizes the identification process as well as possible, especially with regard to tenuous identifications. The cleavable form of the ICAT reagent system was used for quantification. Most proteins did not change significantly in expression as a consequence of the upf1 knockout. As expected, the Upf1 protein itself was down-regulated, and there were reproducible increases in expression of proteins involved in arginine biosynthesis. Initially, it seemed that about 10% of the proteins had changed in expression level, but after more thorough examination of the data it turned out that most of these apparent changes could be explained by artifacts of quantification caused by overlapping heavy/light pairs. About 700 proteins altogether were identified with high confidence and quantified. Many peptides with chemical modifications were identified, as well as peptides with noncanonical tryptic termini. Nearly all of these modified peptides corresponded to the most abundant yeast proteins, and some would otherwise have been attributed to "single hit" proteins at low confidence. To improve our confidence in the identifications, in MALDI experiments, the parent masses for the peptides were calibrated against nearby components. In addition, five novel parameters reflecting different aspects of identification were collected for each spectrum in addition to the Mascot score that was originally used. The interrelationship between these scoring parameters and confidence in protein identification is discussed.     

Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy

Proteome comparison of cell lines derived from cancer and normal breast epithelium provide opportunities to identify differentially expressed proteins and pathways associated with specific phenotypes. We employed 16O/18O peptide labeling, FT-ICR MS, and an accurate mass and time (AMT) tag strategy to simultaneously compare the relative abundance of hundreds of proteins in non-cancer and cancer cell lines derived from breast tissue. A cell line reference panel allowed relative protein abundance comparisons among multiple cell lines and across multiple experiments. A peptide database generated from multidimensional LC separations and MS/MS analysis was used for subsequent AMT tag-based peptide identifications. This peptide database represented a total of 2299 proteins, including 514 that were quantified in five cell lines using the AMT tag and 16O/18O strategies. Eighty-six proteins showed at least a threefold protein abundance change between cancer and non-cancer cell lines. Hierarchical clustering of protein abundance ratios revealed that several groups of proteins were differentially expressed between the cancer cell lines.     

Proteomic profiling of intact proteins using WAX-RPLC 2-D separations and FTICR mass spectrometry

We investigated the combination of weak anion exchange (WAX) fractionation and on-line reversed-phase liquid chromatography (RPLC) separation using a 12 T FTICR mass spectrometer for the detection of intact proteins from a Shewanella oneidensis MR-1 cell lysate. This work aimed at optimizing intact protein detection for profiling proteins at a level that incorporates their modification state. A total of 715 intact proteins were detected, and the combined results from the WAX fractions and the unfractionated cell lysate were aligned using LC-MS features to facilitate protein abundance measurements. Protein identifications and post-translational modifications were assigned for approximately 10% of the detected proteins by comparing intact protein mass measurements to proteins identified in peptide MS/MS analysis of an aliquot of the same fraction. Intact proteins were also detected for S. oneidensis lysates obtained from cells grown on 13C-, 15N-depleted media under aerobic and sub-oxic conditions. The strategy can be readily applied for measuring differential protein abundances and provides a platform for high-throughput selection of biologically relevant targets for further characterization.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.     

Detecting differential and correlated protein expression in label-free shotgun proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.     

Analysis of high accuracy, quantitative proteomics data in the MaxQB database

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.     

The use of proteome similarity for the qualitative and quantitative profiling of reperfused myocardium

An LC-MS-based approach is presented for the identification and quantification of proteins from unsequenced organisms. The method relies on the preservation of homology across species and the similarity in detection characteristics of proteomes in general. Species related proteomes share similarity that progresses from the amino acid frequency distribution to the complete amino sequence of matured proteins. Moreover, the comparative analysis between theoretical and experimental proteome distributions can be used as a measure for the correctness of detection and identification obtained through LC-MS-based schemes. Presented are means to the identification and quantification of rabbit myocardium proteins, immediately after inducing cardiac arrest, using a data-independent LC-MS acquisition strategy. The employed method of acquisition affords accurate mass information on both the precursor and associated product ions, whilst preserving and recording the intensities of the ions. The latter facilitates label-free quantification. The experimental ion density observations obtained for the rabbit sub proteome were found to share great similarity with five other mammalian samples, including human heart, human breast tissue, human plasma, rat liver and a mouse cell line. Redundant, species-homologues peptide identifications from other mammalian organisms were used for initial protein identification, which were complemented with peptide identifications of translated gene sequences. The feasibility and accuracy of label-free quantification of the identified peptides and proteins utilizing above mentioned strategy is demonstrated for selected cardiac rabbit proteins.     

IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.