Computational prediction of proteotypic peptides for quantitative proteomics

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).     

The influence of sample preparation and replicate analyses on HeLa Cell phosphoproteome coverage

Ongoing optimization of proteomic methodologies seeks to improve both the coverage and confidence of protein identifications. The optimization of sample preparation, inclusion of technical replicates (repeated instrumental analysis of the same sample), and biological replicates (multiple individual samples) are crucial in proteomic studies to avoid the pitfalls associated with single point analysis and under-sampling. Phosphopeptides were isolated from HeLa cells and analyzed by nano-reversed phase liquid chromatography electrospray ionization tandem mass spectrometry (nano-RP-LC-MS/MS). We observed that a detergent-based protein extraction approach, followed with additional steps for nucleic acid removal, provided a simple alternative to the broadly used Trizol extraction. The evaluation of four technical replicates demonstrated measurement reproducibility with low percent variance in peptide responses at approximately 3%, where additional peptide identifications were made with each added technical replicate. The inclusion of six technical replicates for moderately complex protein extracts (approximately 4000 uniquely identified peptides per data set) affords the optimal collection of peptide information.     

Quality classification of tandem mass spectrometry data

Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS: We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.     

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.     

"Top Down" characterization is a complementary technique to peptide sequencing for identifying protein species in complex mixtures

At present, mass spectrometry provides a rapid and sensitive means for making conclusive protein identifications from complex mixtures. Sequencing tryptic peptides derived from proteolyzed protein samples, also known as the "Bottom Up" approach, is the mass spectrometric gold standard for identifying unknowns. An alternative technology, "Top Down" characterization, is emerging as a viable option for protein identifications, which involves analyzing the intact unknowns for accurate mass and amino acid sequence tags. In this paper, both characterization methods were employed to more comprehensively differentiate two early-eluting peaks in a process-scale size-exclusion chromatography (SEC) step for a recombinant, immunoglobulin gamma-1 (IgG-1) fusion protein. The contents of each SEC peak were enzymatically digested, and the resulting peptides were mapped using reversed-phase (RP) HPLC-ion trap MS. Many low-level UV signals were observed among the fusion protein-related peptide peaks. These unknowns were collected, concentrated, and analyzed using nanoelectrospray (nanoES) collision-induced dissociation (CID) tandem (MS/MS) mass spectrometry for identification. The peptide sequencing experiments resulted in the identification of twenty host cell-related proteins. Following peptide mapping, the contents of the two SEC peaks were protein mass profiled using on-line RP HPLC coupled to a high-resolution, quadrupole time-of-flight (Qq/TOF) MS. Unknown proteins were also collected, concentrated, and dissociated using nanoES CID MS/MS. Intact protein CID experiments and accurate molecular weight information allowed for the identification of three full length host cell-derived proteins and numerous clips from these and additional proteins. The accurate molecular weight values allowed for the assignment of N- and C-terminal processing, which is difficult to conclusively access from peptide mapping data. The peptide-mapping experiments proved to be far more effective for making protein identifications from complex mixtures, whereas the protein mass profiling was useful for assessing modifications and distinguishing protein clips from full length species.     

Protein Extraction of Formalin-fixed, Paraffin-embedded Tissue Enables Robust Proteomic Profiles by Mass Spectrometry

Global mass spectrometry (MS) profiling and spectral count quantitation are used to identify unique or differentially expressed proteins and can help identify potential biomarkers. MS has rarely been conducted in retrospective studies, because historically, available samples for protein analyses were limited to formalin-fixed, paraffin-embedded (FFPE) archived tissue specimens. Reliable methods for obtaining proteomic profiles from FFPE samples are needed. Proteomic analysis of these samples has been confounded by formalin-induced protein cross-linking. The performance of extracted proteins in a liquid chromatography tandem MS format from FFPE samples and extracts from whole and laser capture microdissected (LCM) FFPE and frozen/optimal cutting temperature (OCT)–embedded matched control rat liver samples were compared. Extracts from FFPE and frozen/OCT–embedded livers from atorvastatin-treated rats were further compared to assess the performance of FFPE samples in identifying atorvastatin-regulated proteins. Comparable molecular mass representation was found in extracts from FFPE and OCT-frozen tissue sections, whereas protein yields were slightly less for the FFPE sample. The numbers of shared proteins identified indicated that robust proteomic representation from FFPE tissue and LCM did not negatively affect the number of identified proteins from either OCT-frozen or FFPE samples. Subcellular representation in FFPE samples was similar to OCT-frozen, with predominantly cytoplasmic proteins identified. Biologically relevant protein changes were detected in atorvastatin-treated FFPE liver samples, and selected atorvastatin-related proteins identified by MS were confirmed by Western blot analysis. These findings demonstrate that formalin fixation, paraffin processing, and LCM do not negatively impact protein quality and quantity as determined by MS and that FFPE samples are amenable to global proteomic analysis. (J Histochem Cytochem 57:849–860, 2009)     

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.     

Development and evaluation of a micro- and nanoscale proteomic sample preparation method

Challenges associated with the efficient and effective preparation of micro- and nanoscale (micro- and nanogram) clinical specimens for proteomic applications include the unmitigated sample losses that occur during the processing steps. Herein, we describe a simple "single-tube" preparation protocol appropriate for small proteomic samples using the organic cosolvent, trifluoroethanol (TFE) that circumvents the loss of sample by facilitating both protein extraction and protein denaturation without requiring a separate cleanup step. The performance of the TFE-based method was initially evaluated by comparisons to traditional detergent-based methods on relatively large scale sample processing using human breast cancer cells and mouse brain tissue. The results demonstrated that the TFE-based protocol provided comparable results to the traditional detergent-based protocols for larger, conventionally sized proteomic samples (>100 microg protein content), based on both sample recovery and numbers of peptide/protein identifications. The effectiveness of this protocol for micro- and nanoscale sample processing was then evaluated for the extraction of proteins/peptides and shown effective for small mouse brain tissue samples (approximately 30 microg total protein content) and also for samples of approximately 5000 MCF-7 human breast cancer cells (approximately 500 ng total protein content), where the detergent-based methods were ineffective due to losses during cleanup and transfer steps.     

Profiling of myelin proteins by 2D-gel electrophoresis and multidimensional liquid chromatography coupled to MALDI TOF-TOF mass spectrometry

The myelin sheath is an electrically insulating layer that consists of lipids and proteins. It plays a key role in the functioning of the nervous system by allowing fast saltatory conduction of nerve pulses. Profiling of the proteins present in myelin is an indispensable prerequisite to better understand the molecular aspects of this dynamic, functionally active membrane. Two types of protein, the myelin basic protein and the proteolipid protein, account for nearly 85% of the protein content in myelin. Identification and characterization of the other "minor" proteins is, in this respect, a real challenge. In the present work, two proteomic strategies were applied in order to study the protein composition of myelin from the murine central nervous system. First, the protein mixture was separated by 2D-gel electrophoresis and, after spot excision and in-gel digestion, samples were analyzed by mass spectrometry. Via this approach, we identified 57 protein spots, corresponding to 38 unique proteins. Alternatively, the myelin sample was digested by trypsin and the resulting peptide mixture was further analyzed by off-line 2D-liquid chromatography. After the second-dimension separation (nanoLC), the peptides were spotted "on-line" onto a MALDI target and analyzed by MALDI TOF-TOF mass spectrometry. We identified 812 peptides by MALDI MS/MS, representing 93 proteins. Membrane proteins, low abundant proteins, and highly basic proteins were all represented in this shotgun proteomic approach. By combining the results of both approaches, we can present a comprehensive proteomic map of myelin, comprising a total of 103 protein identifications, which is of utmost importance for the molecular understanding of white matter and its disorders.     

Reference map for liquid chromatography–mass spectrometry-based quantitative proteomics

The accurate mass and time (AMT) tag strategy has been recognized as a powerful tool for high-throughput analysis in liquid chromatography–mass spectrometry (LC–MS)-based proteomics. Due to the complexity of the human proteome, this strategy requires highly accurate mass measurements for confident identifications. We have developed a method of building a reference map that allows relaxed criteria for mass errors yet delivers high confidence for peptide identifications. The samples used for generating the peptide database were produced by collecting cysteine-containing peptides from T47D cells and then fractionating the peptides using strong cationic exchange chromatography (SCX). LC–tandem mass spectrometry (MS/MS) data from the SCX fractions were combined to create a comprehensive reference map. After the reference map was built, it was possible to skip the SCX step in further proteomic analyses. We found that the reference-driven identification increases the overall throughput and proteomic coverage by identifying peptides with low intensity or complex interference. The use of the reference map also facilitates the quantitation process by allowing extraction of peptide intensities of interest and incorporating models of theoretical isotope distribution.     

The advantage of laser‐capture microdissection over whole tissue analysis in proteomic profiling studies

Laser‐capture microdissection (LCM) offers a reliable cell population enrichment tool and has been successfully coupled to MS analysis. Despite this, most proteomic studies employ whole tissue lysate (WTL) analysis in the discovery of disease biomarkers and in profiling analyses. Furthermore, the influence of tissue heterogeneity in WTL analysis, nor its impact in biomarker discovery studies have been completely elucidated. In order to address this, we compared previously obtained high resolution MS data from a cohort of 38 breast cancer tissues, of which both LCM enriched tumor epithelial cells and WTL samples were analyzed. Label‐free quantification (LFQ) analysis through MaxQuant software showed a significantly higher number of identified and quantified proteins in LCM enriched samples (3404) compared to WTLs (2837). Furthermore, WTL samples displayed a higher amount of missing data compared to LCM both at peptide and protein levels (p‐value < 0.001). 2D analysis on co‐expressed proteins revealed discrepant expression of immune system and lipid metabolisms related proteins between LCM and WTL samples. We hereby show that LCM better dissected the biology of breast tumor epithelial cells, possibly due to lower interference from surrounding tissues and highly abundant proteins. All data have been deposited in the ProteomeXchange with the dataset identifier PXD002381 ( http://proteomecentral.proteomexchange.org/dataset/PXD002381 ).     

Universal metrics for quality assessment of protein identifications by mass spectrometry

Increasing numbers of large proteomic datasets are becoming available. As attempts are made to interpret these datasets and integrate them with other forms of genomic data, researchers are becoming more aware of the importance of data quality with respect to protein identification. We present three simple and universal metrics that describe different aspects of the quality of protein identifications by peptide mass fingerprinting. Hit ratio gives an indication of the signal-to-noise ratio in a mass spectrum, mass coverage measures the amount of protein sequence matched, and excess of limit-digested peptides reflects the completeness of the digestion that precedes the peptide mass fingerprinting. Receiver-operating characteristic plots show that the novel metric, excess of limit-digested peptides, can discriminate between correct and random matches more accurately than search score when validating the results from a state-of-the-art protein identification software system (Mascot) especially when combined with the two other metrics, hit ratio and mass coverage. Recommendations are made regarding the use of the metrics when reporting protein identification experiments.     

Computational prediction of proteotypic peptides

Evaluation of: Mallick P, Schirle M, Chen SS et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25(1), 125–131 (2007).

Mass spectrometry, the driving analytical force behind proteomics, is primarily used to identify and quantify as many proteins in a complex biological mixture as possible. While there are many ways to prepare samples, one aspect that is common to a vast majority of bottom-up proteomic studies is the digestion of proteins into tryptic peptides prior to their analysis by mass spectrometry. As correctly highlighted by Mallick and colleagues, only a few peptides are repeatedly and consistently identified for any given protein within a complex mixture. While the existence of these proteotypic peptides (to borrow the authors’ terminology) is well known in the proteomics community, there has never been an empirical method to recognize which peptides may be proteotypic for a given protein. In this study, the investigators discovered over 16,000 proteotypic peptides from a collection of over 600,000 peptide identifications obtained from four different analytical platforms. The study examined a number of physicochemical parameters of these peptides to determine which properties were most relevant in defining a proteotypic peptide. These characteristic properties were then used to develop computational tools to predict proteotypic peptides for any given protein within an organism.     

Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.     

Large-scale identification of N-terminal peptides in the halophilic archaea Halobacterium salinarum and Natronomonas pharaonis

Characterization of protein N-terminal peptides supports the quality assessment of data derived from genomic sequences (e.g., the correct assignment of start codons) and hints to in vivo N-terminal modifications such as N-terminal acetylation and removal of the initiator methionine. The current work represents the first large-scale identification of N-terminal peptides from prokaryotes, of the two halophilic euryarchaeota Halobacterium salinarum and Natronomonas pharaonis. Two methods were used that specifically allow the characterization of protein N-terminal peptides: combined fractional diagonal chromatography (COFRADIC) and strong cation exchange chromatography (SCX), both known to enrich for N-terminally blocked peptides. In addition to these specific methods, N-terminal peptide identifications were extracted from our previous genome-wide proteomic data. Combining all data, 606 N-terminal peptides from Hbt. salinarum and 328 from Nmn. pharaonis were reliably identified. These results constitute the largest available dataset holding identified and characterized protein N-termini for prokaryotes (archaea and bacteria). They allowed the validation/improvement of start codon assignments as automatic gene finders tend to misassign start codons for GC-rich genomes. In addition, the dataset allowed unravelling N-terminal protein maturation in archaea, showing that 60% of the proteins undergo methionine cleavage and that-in contrast to current knowledge-Nalpha-acetylation is common in the archaeal domain of life with 13-18% of the proteins being Nalpha-acetylated. The protein sets described in this paper are available by FTP and might be used as reference sets to test the performance of new gene finders.     

The Medicago truncatula small protein proteome and peptidome

The small protein and native peptide component of plant tissues is a neglected area of proteomic studies. We have used fractionation techniques for denatured and nondenatured protein preparations combined with 2-D LC tandem mass spectrometry to examine the sequences of small proteins and peptides in four tissues of the model legume, Medicago truncatula: the root tip and root of germinating seedlings, nitrogen fixing nodules, and young leaves. The isolation and fractionation strategies successfully enriched the small protein and native peptide content of the samples. Eighty-one small M. truncatula proteins and native peptides were identified. Most samples were dominated by ribosomal and histone proteins, and leaf samples possessed photosynthesis-related proteins. Secreted proteins such as lipid transfer proteins were common to several tissues. Twenty-four hours after germination, the roots and root tip tissues possessed several "seed-specific" and late-embryogenesis proteins. We conclude that these proteins are present in cells prior to germination and that they are subsequently used as a nutritional source for the young tissues. Native UV absorbing peptides were detected in very low molecular weight fractions and sequenced. Each peptide shared C-terminal residues and showed homology to the seed storage protein legumin. The strategies used here would be suitable for combining bioassays and mass spectrometry to identify bioactive peptides in the M. truncatula peptidome.     

Protein identification and Peptide expression resolver: harmonizing protein identification with protein expression data

Proteomic discovery platforms generate both peptide expression information and protein identification information. Peptide expression data are used to determine which peptides are differentially expressed between study cohorts, and then these peptides are targeted for protein identification. In this paper, we demonstrate that peptide expression information is also a powerful tool for enhancing confidence in protein identification results. Specifically, we evaluate the following hypothesis: tryptic peptides originating from the same protein have similar expression profiles across samples in the discovery study. Evidence supporting this hypothesis is provided. This hypothesis is integrated into a protein identification tool, PIPER (Protein Identification and Peptide Expression Resolver), that reduces erroneous protein identifications below 5%. PIPER's utility is illustrated by application to a 72-sample biomarker discovery study where it is demonstrated that false positive protein identifications can be reduced below 5%. Consequently, it is recommended that PIPER methodology be incorporated into proteomic studies where both protein expression and identification data are collected.