The probability distribution for a random match between an experimental-theoretical spectral pair in tandem mass spectrometry

Proteomic techniques are fast becoming the main method for qualitative and quantitative determination of the protein content in biological systems. Despite notable advances, efficient and accurate analysis of high throughput proteomic data generated by mass spectrometers remains one of the major stumbling blocks in the protein identification problem. We present a model for the number of random matches between an experimental MS-MS spectrum and a theoretical spectrum of a peptide. The shape of the probability distribution is a function of the experimental accuracy, the number of peaks in the experimental spectrum, the length of the interval over which the peaks are distributed, and the number of theoretical spectral peaks in this interval. Based on this probability distribution, a goodness-of-fit tool can be used to yield fast and accurate scoring schemes for peptide identification through database search. In this paper, we describe one possible implementation of such a method and compare the performance of the resulting scoring function with that of SEQUEST. In terms of speed, our algorithm is roughly two orders of magnitude faster than the SEQUEST program, and its accuracy of peptide identification compares favorably to that of SEQUEST. Moreover, our algorithm does not use information related to the intensities of the peaks.     

A proteomic approach to identification of transmembrane proteins and membrane-anchored proteins of Arabidopsis thaliana by peptide sequencing.

A proteomic approach was developed for the identification of membrane-bound proteins of Arabidopsis thaliana. A subcellular fraction enriched in vacuolar membranes was prepared from 4-week-old plants and was washed with various agents to remove peripheral membrane proteins and contaminating soluble proteins. The remaining membrane-bound proteins were then subjected to proteomic analysis. Given that these proteins were resolved poorly by standard two-dimensional gel electrophoresis, we subjected them instead to SDS-polyacrylamide gel electrophoresis and to protein digestion within gel slices with lysylendopeptidase. The resulting peptides were separated by reverse-phase high-performance liquid chromatography and subjected to Edman sequencing. From the 163 peptide peaks analyzed, 69 peptide sequences were obtained, 64 of which were informative. The proteins corresponding to these peptide sequences were identified as belonging to 42 families, including two subfamilies, by comparison with the protein sequences predicted from annotation of the A. thaliana genome. A total of 34 proteins was identified definitively with protein-specific peptide sequences. Transmembrane proteins detected in the membrane fraction included transporters, channels, receptors, and unknown molecules, whereas the remaining proteins, categorized as membrane-anchored proteins, included small GTPases, GTPase binding proteins, heat shock protein 70-like proteins, ribosomal proteins, and unknown proteins. These membrane-anchored proteins are likely attached to membranes by hydrophobic anchor molecules or through tight association with other membrane-bound proteins. This proteomic approach has thus proved effective for the identification of membrane-bound proteins.     

A review of quantitative methods for proteomic studies

An overview is provided of six strategies for relative or absolute quantitation of protein abundances that are widely used in proteomic studies. Strengths and limitations are discussed. Four of these involve stable isotope labeling and isotope ratio measurements by mass spectrometry. In another, mass spectra are used to deconvolute overlapping peptide HPLC peaks to provide relative quantitation based on peak areas. The sixth provides relative abundances of proteins based on 2-D gel arrays. It should be noted that these strategies measure peptide and protein abundances, and cannot directly assess changes in regulation or expression.     

Novel approach for peptide quantitation and sequencing based on 15N and 13C metabolic labeling

Here we describe a method for protein identification and quantification using stable isotopes via in vivo metabolic labeling of the hyperthermophilic crenarchaeon Sulfolobus solfataricus. Stable isotope labeling for quantitative proteomics is becoming increasingly popular; however, its usefulness in protein identification has not been fully exploited. We use both 15N and 13C labeling to create three different versions of the same peptide, corresponding to the unlabeled, 15N and 13C labeled versions. The peptide then appears as three different peaks in a TOF-MS scan and three corresponding sets of MS/MS spectra are obtained. With this information, the elemental carbon and nitrogen compositions for each peptide and each fragment can be calculated. When this is used as a constraint in database searching and/or de novo sequencing, the confidence of a match is increased (for an example intact peptide from 34 choices to 1). This makes the method a useful proteomic tool for both sequenced and unsequenced organisms. Furthermore, it allows for accurate protein quantitation (standard deviations over >4 peptides per protein were within 10%) of three phenotypes in one MS experiment. Abundances for each peptide are calculated by determining the relative areas of each of the three peaks in the TOF-MS spectrum.     

WaveletQuant,an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis

Background  

Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification.     

Monte carlo simulation-based algorithms for analysis of shotgun proteomic data

Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix (www.massmatrix.net). The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.     

Data filtration for more robust alignment of chromatograms of complex peptide mixtures

In carrying out proteomic researches using mass-spectrometry there often arises a need to compare experimental data with each other (e.g. control of pathology, the labeled to unlabelled samples). If for peptide identification in different experiments one uses only their exact mass measurements and the retention time in the chromatographic column, difficulties with the identification of chromatographic peaks belonging to the same substances in different chromatograms come up (retention time normalization). Due to inevitable discrepancies in chromatographic conditions of experiments (replacement of chromatographic columns, small changes in mobile phase flow rate or solvent concentration) retention times of the same peptides will diverge from experiment to experiment. In this paper we offer a reliable method for selecting peaks from mass-chromatograms corresponding to the same peptides, which can later be used for retention time normalization (either linear or any other monotone function).     

SELDI-TOF-MS分析结直肠腺瘤血清蛋白质谱的变化

目的：分析结直肠腺瘤血清蛋白质谱的变化,寻找结直肠腺瘤的特异性生物标志物。方法：采用SELDI-TOF-MS技术（表面增强激光解析电离飞行时间质谱）对比分析31例结直肠腺瘤患者和11例正常人的血清蛋白质谱,用Biomarker Wizard软件对获得的蛋白质谱进行分析。结果：结直肠腺瘤组与正常对照组有24个蛋白峰有差异,其中有三个蛋白峰（8565.84D、8694.51D和5910.50D）的差异非常显著,8565.84D和8694.51D在结直肠腺瘤中高表达,在正常人中低表达,而5910.50D在两组人群中的表达相反。结论：这三个蛋白峰可能为结直肠腺瘤特异性的生物蛋白标志物。     

开唇兰属植物的种间蛋白质组差异

目的:通过差异蛋白峰的比较和指纹图谱的对照,分析开唇兰属五种金线莲的种间蛋白质组差异,初步探索了这种差异在品种鉴定中的应用并推断出品种之间的亲疏关系。方法:采用SELDI-TOF-MS方法研究了开唇兰属五种金线莲的种间蛋白质组差异。结果:通过SELDI-TOF-MS方法对开唇兰属五种金线莲种间蛋白质组谱图进行的分析结果表明,属内种间存在蛋白质组差异和特征蛋白质峰,据此建立了属内五种植物的蛋白质组指纹图谱,并依据蛋白质组的差异推测了属内五种金线莲的亲疏关系。结论:依据SELDI-TOF-MS鉴定结果,找到了开唇兰属五个物种间的蛋白差异,并基于差异蛋白数据分析了品种遗传变异性,结果表明地域和气候会对品种的变异产生一定的影响。     

Comparison of plasma from healthy nonsmokers,smokers, and lung cancer patients: Pattern-based differentiation profiling of low molecular weight proteins and peptides by magnetic bead technology with MALDI-TOF MS

Context: Smoking is the major contributor of lung cancer (LC), which accounts for millions of death.

Objective: This study focused on the correlation between the proteomic profiling of LC patients, and healthy nonsmokers and smokers.

Method: Pattern-based peptide profiling of 186 plasma samples was performed through reversed-phase chromatography-18 magnetic bead fractionation coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis and resulted data were evaluated statistically by ClinProTool.

Results: Marker peaks at m/z 1760, 5773, 5851, 2940, and 7172 were found with an excellent statistical figure.

Conclusion: Selected marker peaks can be served as a differentiated tool of LC patients with high sensitivity and specificity.     

Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10⁻²⁵), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.     

Multi-Q: a fully automated tool for multiplexed protein quantitation

The iTRAQ labeling method combined with shotgun proteomic techniques represents a new dimension in multiplexed quantitation for relative protein expression measurement in different cell states. To expedite the analysis of vast amounts of spectral data, we present a fully automated software package, called Multi-Q, for multiplexed iTRAQ-based quantitation in protein profiling. Multi-Q is designed as a generic platform that can accommodate various input data formats from search engines and mass spectrometer manufacturers. To calculate peptide ratios, the software automatically processes iTRAQ's signature peaks, including peak detection, background subtraction, isotope correction, and normalization to remove systematic errors. Furthermore, Multi-Q allows users to define their own data-filtering thresholds based on semiempirical values or statistical models so that the computed results of fold changes in peptide ratios are statistically significant. This feature facilitates the use of Multi-Q with various instrument types with different dynamic ranges, which is an important aspect of iTRAQ analysis. The performance of Multi-Q is evaluated with a mixture of 10 standard proteins and human Jurkat T cells. The results are consistent with expected protein ratios and thus demonstrate the high accuracy, full automation, and high-throughput capability of Multi-Q as a large-scale quantitation proteomics tool. These features allow rapid interpretation of output from large proteomic datasets without the need for manual validation. Executable Multi-Q files are available on Windows platform at http://ms.iis.sinica.edu.tw/Multi-Q/.     

Tissue proteomics using chemical immobilization and mass spectrometry

Proteomics analysis is important for characterizing tissues to gain biological and pathological insights, which could lead to the identification of disease-associated proteins for disease diagnostics or targeted therapy. However, tissues are commonly embedded in optimal cutting temperature medium (OCT) or are formalin-fixed and paraffin-embedded (FFPE) in order to maintain tissue morphology for histology evaluation. Although several tissue proteomic analyses have been performed on FFPE tissues using advanced mass spectrometry (MS) technologies, high-throughput proteomic analysis of OCT-embedded tissues has been difficult due to the interference of OCT in the MS analysis. In addition, molecules other than proteins present in tissues further complicate tissue proteomic analysis. Here, we report the development of a method using chemical immobilization of proteins for peptide extraction (CIPPE). In this method, proteins are chemically immobilized onto a solid support; interferences from tissues and OCT embedding are removed by extensive washing of proteins conjugated on the solid support. Peptides are then released from the solid phase by proteolysis, enabling MS analysis. This method was first validated by eliminating OCT interference from a standard protein, human serum albumin, where all of the unique peaks contributed by OCT contamination were eradicated. Finally, this method was applied for the proteomic analysis of frozen and OCT-embedded tissues using iTRAQ (isobaric tag for relative and absolute quantitation) labeling and two-dimensional liquid chromatography tandem mass spectrometry. The data showed reproducible extraction and quantitation of 10,284 proteins from 3996 protein groups and a minimal impact of OCT embedding on the analysis of the global proteome of the stored tissue samples.     

Shotgun proteome analysis utilising mixed mode (reversed phase‐anion exchange chromatography) in conjunction with reversed phase liquid chromatography mass spectrometry analysis

The 2‐D peptide separations employing mixed mode reversed phase anion exchange (MM (RP‐AX)) HPLC in the first dimension in conjunction with RP chromatography in the second dimension were developed and utilised for shotgun proteome analysis. Compared with strong cation exchange (SCX) typically employed for shotgun proteomic analysis, peptide separations using MM (RP‐AX) revealed improved separation efficiency and increased peptide distribution across the elution gradient. In addition, improved sample handling, with no significant reduction in the orthogonality of the peptide separations was observed. The shotgun proteomic analysis of a mammalian nuclear cell lysate revealed additional proteome coverage (2818 versus 1125 unique peptides and 602 versus 238 proteins) using the MM (RP‐AX) compared with the traditional SCX hyphenated to RP‐LC‐MS/MS. The MM analysis resulted in approximately 90% of the unique peptides identified present in only one fraction, with a heterogeneous peptide distribution across all fractions. No clustering of the predominant peptide charge states was observed during the gradient elution. The application of MM (RP‐AX) for 2‐D LC proteomic studies was also extended in the analysis of iTRAQ‐labelled HeLa and cyanobacterial proteomes using nano‐flow chromatography interfaced to the MS/MS. We demonstrate MM (RP‐AX) HPLC as an alternative approach for shotgun proteomic studies that offers significant advantages over traditional SCX peptide separations.     

Evaluation of the combination of bead technology with SELDI-TOF-MS and 2-D DIGE for detection of plasma proteins

Today biomarker discovery is one of the most active aspects of proteomic investigations. However, the wide dynamic range of plasma proteins makes the analysis very challenging because high abundance proteins tend to mask those of lower abundance. Using a large bead-based library of combinatorial peptide ligands (Equalizer beads or ProteoMiner), the dynamic range of the protein concentration is compressed, the high abundance proteins present in the sample are reduced and the low abundance proteins are enriched, while retaining representatives of all proteins within the sample. In the present study, the combination of beads with surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and two-dimensional differential gel electrophoresis (2-D DIGE) technology were evaluated considering efficiency, reproducibility, sensitivity, and compatibility. The bead technology is easily compatible with both SELDI-TOF-MS and 2-D DIGE and the samples can be analyzed directly without any processing of the sample. The use of the beads prior SELDI-TOF-MS and 2-D DIGE enabled detection of many new protein spots/peaks and increased resolution and improved intensity of low abundance proteins in a reproducible fashion compared with the depletion technique. Several proteins have been identified by the combination of beads, 2-D DIGE and MS for example different kinds of complement factors and cytoskeletal proteins. Our data suggest that integration of the bead technology with our current proteomic technologies will enhance the possibility to deliver new peptide/protein biomarker candidates in our projects.     

Application of the random forest classification method to peaks detected from mass spectrometric proteomic profiles of cancer patients and controls

The random forest classification method was applied to classify samples from 76 breast cancer patients and 77 controls whose proteomic profile had been obtained using mass spectrometry. The analysis consisted of two stages, the detection of peaks from the profiles and the construction of a classification rule using random forests. Using a peak detection method based on finding common local maxima in the smoothed sample spectra, 444 peaks were detected, reducing to 365 robust peaks found in at least 7 out of 10 random subsets of samples. Subjects were classified as cases or controls using the random forest algorithm applied to the 365 peaks. Based on the prediction of the status of out-of-bag samples, the total error rate was 16.3%, with a sensitivity of 81.6% and a specificity of 85.7%. Measures of importance of each of the peaks were calculated to identify regions of the spectrum influencing the classification, and the four most important peaks were identified as mz3863_13, mz2943_12, mz3193_44 and mz8925_94. Combining initial peak detection with the random forest algorithm provides a high-performance classification system for proteomic data, with unbiased estimates of future performance.     

Diagnostic model of saliva peptide finger print analysis of oral squamous cell carcinoma patients using weak cation exchange magnetic beads

Saliva diagnostics utilizing nanotechnology and molecular technologies to detect oral squamous cell carcinoma (OSCC) has become an attractive field of study. However, no specific methods have been established. To refine the diagnostic power of saliva peptide fingerprints for the early detection of OSCC, we screened the expression spectrum of salivary peptides in 40 T1 stage OSCC patients (and healthy controls) using MALDI-TOF-MS combined with magnetic beads. Fifty proteins showed significantly different expression levels in the OSCC samples (P<0.05). Potential biomarkers were also predicted. The novel diagnostic proteomic model with m/z peaks of 1285.6 Da and 1432.2 Da are of certain value for early diagnosis of OSCC.     

Quantitative analysis of the yeast proteome by incorporation of isotopically labeled leucine

Quantitative comparison of protein expression levels in 2D gels is complicated by the variables associated with protein separation and mass spectrometric responses. Metabolic labeling allows cells from different experiments to be mixed prior to analysis. This approach has been reported for prokaryotic cells. Here, we demonstrate that metabolic labeling can also be successfully applied to the eukaryote Saccharormyces cerevisiae. Yeast leucine auxotrophs grown on synthetic complete media containing natural abundance Leu or D10-Leu were mixed prior to 2D gel separation and MALDI analysis of the digested proteins. D10-Leu labeling provided an effective internal calibrant for peptide MS analysis, and the number of Leu residues yielded an additional parameter for peptide identification at low mass resolution (1000). Metabolic incorporation of D10-Leu into yeast proteins was found to be quantitative since the intensities of the peptide peaks corresponded to those expected on the basis of the percent label in the media. Thus, D10-Leu labeling should provide reliable data for comparing proteomes both quantitatively and qualitatively from wild-type and nonessential-gene-null-mutant strains of S. cerevisiae. Given the central role played by yeast in our understanding of eukaryotic gene and protein expression, it is anticipated that the quantitative expressional proteomic method outlined here will have widespread applications.     

Sulfonation chemistry as a powerful tool for MALDI TOF/TOF de novo sequencing and post-translational modification analysis.

Mass spectrometry using matrix-assisted laser desorption/ionization (MALDI) is a widespread technique for various types of proteomic analysis. In the identification of proteins using peptide mass fingerprinting, samples are enzymatically digested and resolved into a number of peptides, whose masses are determined and matched with a sequence data-base. However, the presence inside the cell of several splicing variants, protein isoforms, or fusion proteins gives rise to a complex picture, demanding more complete analysis. Moreover, the study of species with yet uncharacterized genomes or the investigation of post-translational modifications are not possible with classical mass fingerprinting, and require specific and accurate de novo sequencing. In the last several years, much effort has been made to improve the performance of peptide sequencing with MALDI. Here we present applications using a fast and robust chemical modification of peptides for improved de novo sequencing. Post-source decay of derivatized peptides generates at the same time peaks with high intensity and simple spectra, leading to a very easy and clear sequence determination.     

Two‐step OFFGEL approach for effective peptide separation compatible with iTRAQ labeling

Shotgun proteomic analyses are increasingly becoming methods of choice for complex samples. The development of effective methods for fractionating peptides to reduce the complexity of the sample before mass analysis is a key point in this strategy. The OFFGEL technology has recently become a tool of choice in proteomic analysis at peptide level. This OFFGEL electrophoresis (OGE) approach allows the in‐solution separation of peptides from various biological sources by isoelectric focusing in highly resolved 24 fractions. It was also demonstrated that OGE technology is a filtering tool for pI‐based validation of peptide identification. As peptide OGE is compatible with iTRAQ labeling, OGE is finding valuable applications in quantitative proteomics as well. The aim of this study is to explain a new 2D‐OGE approach that improves the proteomic coverage of complex mixtures such as colorectal cell line lysates, and which is compatible with iTRAQ labeling.