Evaluation of prefractionation methods as a preparatory step for multidimensional based chromatography of serum proteins

Prefractionations of proteins prior to their proteolysis, chromatography, and MS/MS analyses help reduce complexity and increase the yield of protein identifications. A number of methods were evaluated here for prefractionating serum samples distributed to the participating laboratories as part of the human Plasma Proteome Project. These methods include strong cation exchange (SCX) chromatography, slicing of SDS-PAGE gel bands, and liquid-phase IEF of the proteins. The fractionated proteins were trypsinized and the resulting peptides were resolved and analyzed by multidimensional protein identification technology coupled to IT MS/MS. The MS/MS spectra were clustered, combined, and searched against the IPI protein databank using Pep-Miner. The identification results were evaluated for the efficacy of the different prefractionation methodologies to identify larger numbers of proteins at higher confidence and to achieve the best coverage of the proteins with the identified peptides. Prefractionation based on SCX resulted in the largest number of identified proteins, followed by gel slices and then the liquid-phase IEF. An important observation was that each of the methods revealed a set of unique proteins, some identified with high confidence. Therefore, for comprehensive identification of the serum proteins, several different prefractionation approaches should be used in parallel.     

Protein identification assisted by the prediction of retention time in liquid chromatography/tandem mass spectrometry

Two-dimensional liquid chromatography (2D-LC) coupled on-line with electrospray ionization tandem mass spectrometry (2D-LC-ESI-MS/MS) is a new platform for analysis and identification of proteome. Peptides are separated by 2D-LC and then performed MS/MS analysis by tandem MS/MS. The MS/MS data are searched against database for protein identification. In one 2D-LC-ESI-MS/MS run, we obtained not only the structural information of peptides directly from MS/MS, but also the retention time of peptides eluted from LC. Information on the chromatographic behavior of peptides can assist protein identification in the new platform for proteomics. The retention time of the matching peptides of the identified protein was predicted by the hydrophobic contribute of each amino acid on reversed-phase liquid chromatography (RPLC). By using this strategy proteins were identified by four types of information: peptide mass fingerprinting (PMF), sequence query, and MS/MS ions searched and the predicted retention time. This additional information obtained from LC could assist protein identification with no extra experimental cost.     

Whole-Cell Protein Identification Using the Concept of Unique Peptides

A concept of unique peptides(CUP)was proposed and implemented to identify whole-cell proteins from tandem mass spectrometry(MS/MS)ion spectra.A unique peptide is defined as a peptide,irrespective of its length,that exists only in one protein of a proteome of interest,despite the fact that this peptide may appear more than once in the same protein.Integrating CUP,a two-step whole-cell protein identification strategy was developed to further increase the confidence of identified proteins.A dataset containing 40,243 MS/MS ion spectra of Saccharomyces cerevisiae and protein identification tools including Mascot and SEQUEST were used to illustrate the proposed concept and strategy.Without implementing CUP,the proteins identified by SEQUEST are 2.26 fold of those identified by Mascot.When CUP was applied,the proteins bearing unique peptides identified by SEQUEST are3.89 fold of those identified by Mascot.By cross-comparing two sets of identified proteins,only 89 common proteins derived from CUP were found.The key discrepancy between identified proteins was resulted from the filtering criteria employed by each protein identification tool.According to the origin of peptides classified by CUP and the commonality of proteins recognized by protein identification tools,all identified proteins were cross-compared,resulting in four groups of proteins possessing different levels of assigned confidence.     

In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.     

Characterization of the human salivary proteome by capillary isoelectric focusing/nanoreversed-phase liquid chromatography coupled with ESI-tandem MS

Saliva is a readily available body fluid with great diagnostic potential. The foundation for saliva-based diagnostics, however, is the development of a complete catalog of secreted and "leaked" proteins detectable in saliva. By employing a capillary isoelectric focusing-based multidimensional separation platform coupled with electrospray ionization tandem mass spectrometry (MS), a total of 5338 distinct peptides were sequenced, leading to the identification of 1381 distinct proteins. A search of bacterial protein sequences also identified many peptides unique to several organisms and unique to the NCBI nonredundant database. To the best of our knowledge, this proteome study represents the largest catalog of proteins measured from a single saliva sample to date. Data analysis was performed on individual MS/MS spectra using the highly specific peptide identification algorithm, OMSSA. Searches were conducted against a decoyed SwissProt human database to control the false-positive rate at 1%. Furthermore, the well-curated SwissProt sequences represent perhaps the least redundant human protein sequence database (12,484 records versus the 50,009 records found in the International Protein Index human database), therefore minimizing multiple protein inferences from single peptides. This combined bioanalytical and bioinformatic approach has established a solid foundation for building up the human salivary proteome for the realization of the diagnostic potential of saliva.     

ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

Mass spectrometric genomic data mining: Novel insights into bioenergetic pathways in Chlamydomonas reinhardtii

A new high-throughput computational strategy was established that improves genomic data mining from MS experiments. The MS/MS data were analyzed by the SEQUEST search algorithm and a combination of de novo amino acid sequencing in conjunction with an error-tolerant database search tool, operating on a 256 processor computer cluster. The error-tolerant search tool, previously established as GenomicPeptideFinder (GPF), enables detection of intron-split and/or alternatively spliced peptides from MS/MS data when deduced from genomic DNA. Isolated thylakoid membranes from the eukaryotic green alga Chlamydomonas reinhardtii were separated by 1-D SDS gel electrophoresis, protein bands were excised from the gel, digested in-gel with trypsin and analyzed by coupling nano-flow LC with MS/MS. The concerted action of SEQUEST and GPF allowed identification of 2622 distinct peptides. In total 448 peptides were identified by GPF analysis alone, including 98 intron-split peptides, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.     

Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.     

Identification of tryptic peptides from large databases using multiplexed tandem mass spectrometry: simulations and experimental results

Multiplexed tandem mass spectrometry (MS/MS) has recently been demonstrated as a means to increase the throughput of peptide identification in liquid chromatography (LC) MS/MS experiments. In this approach, a set of parent species is dissociated simultaneously and measured in a single spectrum (in the same manner that a single parent ion is conventionally studied), providing a gain in sensitivity and throughput proportional to the number of species that can be simultaneously addressed. In the present work, simulations performed using the Caenorhabditis elegans predicted proteins database show that multiplexed MS/MS data allow the identification of tryptic peptides from mixtures of up to ten peptides from a single dataset with only three "y" or "b" fragments per peptide and a mass accuracy of 2.5 to 5 ppm. At this level of database and data complexity, 98% of the 500 peptides considered in the simulation were correctly identified. This compares favorably with the rates obtained for classical MS/MS at more modest mass measurement accuracy. LC multiplexed Fourier transform-ion cyclotron resonance MS/MS data obtained from a 66 kDa protein (bovine serum albumin) tryptic digest sample are presented to illustrate the approach, and confirm that peptides can be effectively identified from the C. elegans database to which the protein sequence had been appended.     

Uromodulin Exclusion List Improves Urinary Exosomal Protein Identification

Advances in mass spectrometry (MS) have encouraged interest in its deployment in urine biomarker studies, but success has been limited. Urine exosomes have been proposed as an ideal source of biomarkers for renal disease. However, the abundant urinary protein, uromodulin, cofractionates with exosomes during isolation and represents a practical contaminant that limits MS sensitivity. Uromodulin depletion has been attempted but is labor- and time-intensive and may remove important protein biomarkers. We describe the application of an exclusion list (ExL) of uromodulin-related peptide ions, coupled with high-sensitivity mass spectrometric analysis, to increase the depth of coverage of the urinary exosomal proteome. Urine exosomal protein samples from healthy volunteers were subjected to tandem MS and abundant uromodulin peptides identified. Samples were run for a second time, while excluding these uromodulin peptides from fragmentation to allow identification of peptides from lower-abundance proteins. Uromodulin exclusion was performed in addition to dynamic exclusion. Results from these two procedures revealed 222 distinct proteins from conventional analysis, compared with 254 proteins after uromodulin exclusion, of which 188 were common to both methods. By unmasking a previously unidentified protein set, adding the ExL increased overall protein identifications by 29.7% to a total of 288 proteins. A fixed ExL, used in combination with conventional methods, effectively increases the depth of urinary exosomal proteins identified by MS, reducing the need for uromodulin depletion.     

Effect of cleavage enzyme, search algorithm and decoy database on mass spectrometric identification of wheat gluten proteins

While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein database content and size on the false discovery rate. We found that as database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy database influenced the number of proteins identified. Using three enzymes, two search algorithms and a specialized database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.     

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.     

Proteome profiling of the green sulfur bacterium Chlorobaculum tepidum by N-terminal proteomics

In this study, we performed the first large-scale identification of N-terminal peptides from the green sulfur bacterium Chlorobaculum tepidum. Combined fractional diagonal chromatography (COFRADIC) was used to isolate protein N-terminal peptides from three different proteome preparations, and following LC-MS/MS analysis, over 621 different proteins were identified by their N-terminal peptides. Our data constitute the largest data set currently available for protein N-termini of prokaryotic photosynthetic organisms.     

基于质谱的BLAST方法在金鱼胚胎蛋白质鉴定中的应用

未知基因组及蛋白质序列数据库有限的物种的蛋白质组学分析是当前一些非模式生物物种蛋白质组学研究领域的瓶颈之一.基于同源性搜索的BLAST方法（MS BLAST）,是近年新发展起来的一种用于未知基因组的蛋白质鉴定的搜索工具,已成功应用于许多未知基因组物种的蛋白质鉴定.SPITC化学辅助方法是本实验室建立的一种改进的de novo质谱测序方法.采用MS BLAST方法对经Mascot软件数据库搜索未能鉴定到的19个金鱼胚胎蛋白质进行鉴定,其中12个蛋白质是直接测序后进行MS BLAST搜索得到的结果,另外7个蛋白质是联合MS BLAST和SPITC衍生方法得到的鉴定结果.实验结果证明,采用MS BLAST方法进行蛋白质的跨物种鉴定具有可行性和可靠性,给蛋白质的跨物种鉴定提供了一条新的途径.     

Identification and characterization of disulfide bonds in proteins and peptides from tandem MS data by use of the MassMatrix MS/MS search engine

A new database search algorithm has been developed to identify disulfide-linked peptides in tandem MS data sets. The algorithm is included in the newly developed tandem MS database search program, MassMatrix. The algorithm exploits the probabilistic scoring model in MassMatrix to achieve identification of disulfide bonds in proteins and peptides. Proteins and peptides with disulfide bonds can be identified with high confidence without chemical reduction or other derivatization. The approach was tested on peptide and protein standards with known disulfide bonds. All disulfide bonds in the standard set were identified by MassMatrix. The algorithm was further tested on bovine pancreatic ribonuclease A (RNaseA). The 4 native disulfide bonds in RNaseA were detected by MassMatrix with multiple validated peptide matches for each disulfide bond with high statistical scores. Fifteen nonnative disulfide bonds were also observed in the protein digest under basic conditions (pH = 8.0) due to disulfide bond interchange. After minimizing the disulfide bond interchange (pH = 6.0) during digestion, only one nonnative disulfide bond was observed. The MassMatrix algorithm offers an additional approach for the discovery of disulfide bond from tandem mass spectrometry data.     

Automated comparative proteomics based on multiplex tandem mass spectrometry and stable isotope labeling

Comparative proteomic approaches using isotopic labeling and MS have become increasingly popular. Conventionally quantification is based on MS or extracted ion chromatogram (XIC) signals of differentially labeled peptides. However, in these MS-based experiments, the accuracy and dynamic range of quantification are limited by the high noise levels of MS/XIC data. Here we report a quantitative strategy based on multiplex (derived from multiple precursor ions) MS/MS data. One set of proteins was metabolically labeled with [13C6]lysine and [15N4]arginine; the other set was unlabeled. For peptide analysis after tryptic digestion of the labeled proteins, a wide precursor window was used to include both the light and heavy versions of each peptide for fragmentation. The multiplex MS/MS data were used for both protein identification and quantification. The use of the wide precursor window increased sensitivity, and the y ion pairs in the multiplex MS/MS spectra from peptides containing labeled and unlabeled lysine or arginine offered more information for, and thus the potential for improving, protein identification. Protein ratios were obtained by comparing intensities of y ions derived from the light and heavy peptides. Our results indicated that this method offers several advantages over the conventional XIC-based approach, including increased sensitivity for protein identification and more accurate quantification with more than a 10-fold increase in dynamic range. In addition, the quantification calculation process was fast, fully automated, and independent of instrument and data type. This method was further validated by quantitative analysis of signaling proteins in the EphB2 pathway in NG108 cells.     

Analysis of the Arabidopsis cytosolic ribosome proteome provides detailed insights into its components and their post-translational modification

Finding gene-specific peptides by mass spectrometry analysis to pinpoint gene loci responsible for particular protein products is a major challenge in proteomics especially in highly conserved gene families in higher eukaryotes. We used a combination of in silico approaches coupled to mass spectrometry analysis to advance the proteomics insight into Arabidopsis cytosolic ribosomal composition and its post-translational modifications. In silico digestion of all 409 ribosomal protein sequences in Arabidopsis defined the proportion of theoretical gene-specific peptides for each gene family and highlighted the need for low m/z cutoffs of MS ion selection for MS/MS to characterize low molecular weight, highly basic ribosomal proteins. We undertook an extensive MS/MS survey of the cytosolic ribosome using trypsin and, when required, chymotrypsin and pepsin. We then used custom software to extract and filter peptide match information from Mascot result files and implement high confidence criteria for calling gene-specific identifications based on the highest quality unambiguous spectra matching exclusively to certain in silico predicted gene- or gene family-specific peptides. This provided an in-depth analysis of the protein composition based on 1446 high quality MS/MS spectra matching to 795 peptide sequences from ribosomal proteins. These identified peptides from five gene families of ribosomal proteins not identified previously, providing experimental data on 79 of the 80 different types of ribosomal subunits. We provide strong evidence for gene-specific identification of 87 different ribosomal proteins from these 79 families. We also provide new information on 30 specific sites of co- and post-translational modification of ribosomal proteins in Arabidopsis by initiator methionine removal, N-terminal acetylation, N-terminal methylation, lysine N-methylation, and phosphorylation. These site-specific modification data provide a wealth of resources for further assessment of the role of ribosome modification in influencing translation in Arabidopsis.     

Exploring proteins in Anopheles gambiae male and female antennae through MALDI mass spectrometry profiling

MALDI profiling and imaging mass spectrometry (IMS) are novel techniques for direct analysis of peptides and small proteins in biological tissues. In this work we applied them to the study of Anopheles gambiae antennae, with the aim of analysing expression of soluble proteins involved in olfaction perireceptor events. MALDI spectra obtained by direct profiling on single antennae and by the analysis of extracts, showed similar profiles, although spectra obtained through profiling had a richer ion population and higher signal to noise ratio. Male and female antennae showed distinct protein profiles. MALDI imaging experiments were also performed and differences were observed in the localization of some proteins. Two proteins were identified through high resolution measurement and top-down MS/MS experiments. A 8 kDa protein only present in the male antennae matched with an unannotated sequence of the An. gambiae genome, while the presence of odorant binding protein 9 (OBP-9) was confirmed through experiments of 2-DE, followed by MS and MS/MS analysis of digested spots. This work shows that MALDI MS profiling is a technique suitable for the analysis of proteins of small and medium MW in insect appendices, and allows obtaining data for several specimens which can be investigated for differences between groups. Proteins of interest can be identified through other complementary MS approaches.