Principles and applications of multidimensional protein identification technology

Multidimensional chromatography coupled to tandem mass spectrometry is an emerging technique for the analysis of proteomes and is rapidly being implemented by many researchers for proteomic analysis. In this technology profile, a particular proteomic approach known as multidimensional protein identification technology (MudPIT) is discussed. In MudPIT, a biphasic microcapillary column is packed with high-performance liquid chromatography grade reversed phase and strong cation exchange packing materials, loaded with a complex peptide mixture and placed in line with quaternary high-performance liquid chromatography and a tandem mass spectrometer. MudPIT has the capability to analyze highly complex proteomic mixtures such as whole proteomes, organelles and protein complexes.     

Principles and applications of Multidimensional Protein Identification Technology

Multidimensional chromatography coupled to tandem mass spectrometry is an emerging technique for the analysis of proteomes and is rapidly being implemented by many researchers for proteomic analysis. In this technology profile, a particular proteomic approach known as multidimensional protein identification technology (MudPIT) is discussed. In MudPIT, a biphasic microcapillary column is packed with high-performance liquid chromatography grade reversed phase and strong cation exchange packing materials, loaded with a complex peptide mixture and placed in line with quaternary high-performance liquid chromatography and a tandem mass spectrometer. MudPIT has the capability to analyze highly complex proteomic mixtures such as whole proteomes, organelles and protein complexes.     

A proteomic view of Bifidobacterium infantis generated by multi-dimensional chromatography coupled with tandem mass spectrometry

Bifidobacteria are Gram-positive prokaryotes that naturally colonize the human gut where they exert several health-promoting effects. The present paper reports the use of a strong cation exchange-reversed-phase-tandem mass spectrometry strategy to catalogue the most abundantly expressed proteins of a probiotic Bifidobacterium infantis strain. A global view of the B. infantis proteome was obtained. The bimodal representation of the proteins identified by mass spectrometry provides the first theoretical two-dimensional map of protein distribution for this organism. Among the 136 proteins identified by multidimensional protein identification technology (MudPIT) analysis, 118 showed the highest similarity with the translated sequences of B. longum genome, two proteins were similar to other Bifidobacterium species and the remaining 16 were similar to different genera. Specific biological activities have been assigned to 115 identified proteins, whereas 21 have been referred to the group of hypothetical proteins. The MudPIT approach allowed us to identify high mass and basic isoelectric point proteins that are generally challenging to visualize using the traditional two-dimensional electrophoresis technique. Redundancy in peptide and protein identification using the double chromatography technique was also evaluated.     

Strategies for metagenomic-guided whole-community proteomics of complex microbial environments

Accurate protein identification in large-scale proteomics experiments relies upon a detailed, accurate protein catalogue, which is derived from predictions of open reading frames based on genome sequence data. Integration of mass spectrometry-based proteomics data with computational proteome predictions from environmental metagenomic sequences has been challenging because of the variable overlap between proteomic datasets and corresponding short-read nucleotide sequence data. In this study, we have benchmarked several strategies for increasing microbial peptide spectral matching in metaproteomic datasets using protein predictions generated from matched metagenomic sequences from the same human fecal samples. Additionally, we investigated the impact of mass spectrometry-based filters (high mass accuracy, delta correlation), and de novo peptide sequencing on the number and robustness of peptide-spectrum assignments in these complex datasets. In summary, we find that high mass accuracy peptide measurements searched against non-assembled reads from DNA sequencing of the same samples significantly increased identifiable proteins without sacrificing accuracy.     

High quality catalog of proteotypic peptides from human heart

Proteomics research is beginning to expand beyond the more traditional shotgun analysis of protein mixtures to include targeted analyses of specific proteins using mass spectrometry. Integral to the development of a robust assay based on targeted mass spectrometry is prior knowledge of which peptides provide an accurate and sensitive proxy of the originating gene product (i.e., proteotypic peptides). To develop a catalog of "proteotypic peptides" in human heart, TRIzol extracts of left-ventricular tissue from nonfailing and failing human heart explants were optimized for shotgun proteomic analysis using Multidimensional Protein Identification Technology (MudPIT). Ten replicate MudPIT analyses were performed on each tissue sample and resulted in the identification of 30 605 unique peptides with a q-value < or = 0.01, corresponding to 7138 unique human heart proteins. Experimental observation frequencies were assessed and used to select over 4476 proteotypic peptides for 2558 heart proteins. This human cardiac data set can serve as a public reference to guide the selection of proteotypic peptides for future targeted mass spectrometry experiments monitoring potential protein biomarkers of human heart diseases.     

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT)

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.     

Integrating a functional proteomic approach into the target discovery process

Functional proteomics is a promising technique for the rational identification of novel therapeutic targets by elucidation of the function of newly identified proteins in disease-relevant cellular pathways. Of the recently described high-throughput approaches for analyzing protein-protein interactions, the yeast two-hybrid (Y2H) system has turned out to be one of the most suitable for genome-wide analysis. However, this system presents a challenging technical problem: the high prevalence of false positives and false negatives in datasets due to intrinsic limitations of the technology and the use of a high-throughput, genetic assay. We discuss here the different experimental strategies applied to Y2H assays, their general limitations and advantages. We also address the issue of the contribution of protein interaction mapping to functional biology, especially when combined with complementary genomic and proteomic analyses. Finally, we illustrate how the combination of protein interaction maps with relevant functional assays can provide biological support to large-scale protein interaction datasets and contribute to the identification and validation of potential therapeutic targets.     

Advanced MudPIT as a next step toward high proteome coverage

We present a simple, time- and cost-efficient approach to tackle the proteome of prokaryotic organisms. To obtain large data sets of complex biological experiments high-throughput and time- and cost-efficient methods still have to be developed and refined. In this study, we combined well-approved techniques, namely elevated chromatographic temperatures, long RP columns and the multidimensional protein identification technology MudPIT to achieve high proteome coverage. The advanced MudPIT approach has been evaluated and delivered very comprehensive results for Gram-positive as well as Gram-negative bacteria (53% proteome coverage for Corynebacterium glutamicum and 46% proteome coverage for Escherichia coli). Also, a high identification rate for the challenging integral membrane proteins was achieved. The competitiveness of the advanced MudPIT technology is strengthened by the fact that in this approach only two fractions were analyzed with both, simple and time-efficient sample preparation, and a moderate data acquisition time.     

Multidimensional separations for protein/peptide analysis in the post-genomic era

Proteomics is the study of all or part of the protein complement of genes in an organism, often involving the analysis of complex protein/peptide samples. Such complex samples are beyond the separation capacity of 1-D separation techniques. This review describes several multidimensional separations for proteins and peptides. First, several variants of 2-D liquid chromatography (2DLC) are reviewed, including coupled size exclusion-reversed phase, ion exchange-reversed phase, and reversed phase-reversed phase chromatography. Second, we describe coupled liquid chromatography and capillary electrophoresis methods. Finally, a multidimensional protein identification technique (MudPIT) is explained in detail. Each of the described techniques has a much higher separation capacity than 1-D methods and can potentially be automated for high-throughput experiments. In particular, MudPIT takes advantage of both the high separation capacity of 2DLC and the powerful peptide characterization ability of tandem mass spectrometry to analyze complex protein samples. Additional applications and developments of multidimensional liquid separations for proteomics are expected in the future.     

A method for the comprehensive proteomic analysis of membrane proteins

We describe a method that allows for the concurrent proteomic analysis of both membrane and soluble proteins from complex membrane-containing samples. When coupled with multidimensional protein identification technology (MudPIT), this method results in (i) the identification of soluble and membrane proteins, (ii) the identification of post-translational modification sites on soluble and membrane proteins, and (iii) the characterization of membrane protein topology and relative localization of soluble proteins. Overlapping peptides produced from digestion with the robust nonspecific protease proteinase K facilitates the identification of covalent modifications (phosphorylation and methylation). High-pH treatment disrupts sealed membrane compartments without solubilizing or denaturing the lipid bilayer to allow mapping of the soluble domains of integral membrane proteins. Furthermore, coupling protease protection strategies to this method permits characterization of the relative sidedness of the hydrophilic domains of membrane proteins.     

Computational methods for protein identification from mass spectrometry data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.     

Proteomic analysis of the mosquito Aedes aegypti midgut brush border membrane vesicles

We analyzed brush border membrane vesicle proteins from isolated midguts of the mosquito Aedes aegypti, by two proteomic methods: two-dimensional gel electrophoresis (isoelectric focusing and SDS-PAGE) and a shotgun two-dimensional liquid chromatographic (LS/LS) approach based on multidimensional protein identification technology (MudPIT). We were interested in the most abundant proteins of the apical brush border midgut membrane. About 400 spots were detected on 2D gels and 39 spots were cored and identified by mass spectrometry. 86 proteins were identified by MudPIT. Three proteins, arginine kinase, putative allergen and actin are shown to be the most predominant proteins in the sample. The total number of 36 proteins detected by both methods represents the most abundant proteins in the BBMV.     

Proteomic characterization of the Pseudomonas putida KT2440 global response to a monocyclic aromatic compound by iTRAQ analysis and 1DE-MudPIT

Pseudomonas putida KT2440 is a metabolically versatile soil bacterium. To examine the effects of an aromatic compound on the proteome of this bacterium, cytosolic proteins induced by the presence of benzoate and succinate were analyzed using two liquid chromatography (LC)-based proteomic approaches: an isobaric tag for relative and absolute quantitation (iTRAQ) for quantitative analysis and one-dimensional gel electrophoresis/multidimensional protein identification technology (1-DE MudPIT) for protein identification. In total, 1286 proteins were identified by 1-DE MudPIT; this represents around 23.3% of the total proteome. In contrast, 570 proteins were identified and quantified by iTRAQ analysis. Of these, 55 and 52 proteins were up- and down-regulated, respectively, in the presence of benzoate. The proteins up-regulated included benzoate degradation enzymes, chemotaxis-related proteins, and ABC transporters. Enzymes related to nitrogen metabolism and pyruvate metabolism were down-regulated. These data suggest that a combination of 1-DE MudPIT and iTRAQ is an appropriate method for comprehensive proteomic analysis of biodegradative bacteria.     

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.     

Comparison of 2-D LC and 3-D LC with post- and pre-tryptic-digestion SEC fractionation for proteome analysis of normal human liver tissue

The current "shotgun" proteomic analysis, strong cation exchange-RPLC-MS/MS system, is a widely used method for proteome research. Currently, it is not suitable for complicated protein sample analysis, like mammal tissues or cells. To increase the protein identification confidence and number, an additional separation dimension for sample fractionation is necessary to be coupled prior to current multi-dimensional protein identification technology (MudPIT). In this work, SEC was elaborately selected and applied for sample prefractionation in consideration of its non-bias against sample and variety of choice of mobile phases. The analysis of the global lysate of normal human liver tissue sample provided by the China Human Liver Proteome Project, were performed to compare the proteome coverage, sequence coverage (peptide per protein identification) and protein identification efficiency in MudPIT, 3-D LC-MS/MS identification strategy with preproteolytic and postproteolytic fractionation. It was demonstrated that 3-D LC-MS/MS utilizing protein level fractionation was the most effective method. A MASCOT search using the MS/MS results acquired by QSTAR(XL) identified 1622 proteins from 3-D LC-MS/MS identification approaches. A primary analysis on molecular weight, pI and grand average hydrophobicity value distribution of the identified proteins in different approaches was made to further evaluate the 3-D LC-MS/MS analysis strategy.     

Characterization of the human submandibular/sublingual saliva glycoproteome using lectin affinity chromatography coupled to multidimensional protein identification technology

In-depth analysis of the salivary proteome is fundamental to understanding the functions of salivary proteins in the oral cavity and to reveal disease biomarkers involved in different pathophysiological conditions, with the ultimate goal of improving patient diagnosis and prognosis. Submandibular and sublingual glands contribute saliva rich in glycoproteins to the total saliva output, making them valuable sources for glycoproteomic analysis. Lectin-affinity chromatography coupled to mass spectrometry-based shotgun proteomics was used to explore the submandibular/sublingual (SM/SL) saliva glycoproteome. A total of 262 N- and O-linked glycoproteins were identified by multidimensional protein identification technology (MudPIT). Only 38 were previously described in SM and SL salivas from the human salivary N-linked glycoproteome, while 224 were unique. Further comparison analysis with SM/SL saliva of the human saliva proteome, revealed 125 glycoproteins not formerly reported in this secretion. KEGG pathway analyses demonstrated that many of these glycoproteins are involved in processes such as complement and coagulation cascades, cell communication, glycosphingolipid biosynthesis neo-lactoseries, O-glycan biosynthesis, glycan structures-biosynthesis 2, starch and sucrose metabolism, peptidoglycan biosynthesis or others pathways. In summary, lectin-affinity chromatography coupled to MudPIT mass spectrometry identified many novel glycoproteins in SM/SL saliva. These new additions to the salivary proteome may prove to be a critical step for providing reliable biomarkers in the diagnosis of a myriad of oral and systemic diseases.     

Arabidopsis cell wall proteome defined using multidimensional protein identification technology

With the completion of the sequencing of the Arabidopsis genome and the recent advances in proteomic technology, the identification of proteins from highly complex mixtures is now possible. Rather than using gel electrophoresis and peptide mass fingerprinting, we have used multidimensional protein identification technology (MudPIT) to analyse the "tightly-bound" proteome for purified cell walls from Arabidopsis cell suspension cultures. Using bioinformatics for the prediction of signal peptides for targeting to the secretory pathway and for the absence of ER retention signal, 89 proteins were selected as potential extracellular proteins. Only 33% of these were identified in previous proteomic analyses of Arabidopsis cell walls. A functional classification revealed that a large proportion of the proteins were enzymes, notably carbohydrate active enzymes, peroxidases and proteases. Comparison of all the published proteomic analyses for the Arabidopsis cell wall identified 268 non-redundant genes encoding wall proteins. Sixty of these (22%) were derived from our analysis of tightly-bound wall proteins.