Proteomics: From Technology Developments to ... - ACS Publications

Anal. Chem. 2009, 81, 4585–4599

Proteomics: From Technology Developments to Biological Applications Mohamed Abu-Farha, Fred Elisma, Houjiang Zhou, Ruijun Tian, Hu Zhou, Mehmet Selim Asmer, and Daniel Figeys* Ottawa Institute of Systems Biology (OISB), University of Ottawa, Ottawa, Ontario, Canada, and Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada Review Contents Quantitative Proteomics Identification of Protein-Protein Interactions Quantitative Protein-Protein Interaction Post-Translational Modifications Phosphorylation Glycosylation Lipidation Ubiquitination and SUMOylation Acetylation and Methylation Chemical Proteomics Probe Structure Improvement Applications of Chemical Proteomics Analytical Techniques Electrophoresis Protein Chip Liquid Chromatography and Mass Spectrometry Bioinformatics Protein-Protein Interactions Quantitative Software Development Database Resources Management System Conclusion Literature Cited

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Since our 2005/2006 review, the field of proteomics has remained very dynamic with new technologies, applications, and challenges. The mapping of protein-ligand interactions has remained a solid pillar of proteomics. Certain organisms’ interactomes, such as that of Saccharomyces cerevisiae, have almost reached saturation; while others, like the human interactome, are extensively being studied. The technology for quantitative proteomics has also advanced rapidly and has shown tremendous potential for studying the dynamic nature of biological processes. Other exciting news is also coming from the identification of posttranslational modifications (PTMs) and the development of novel technologies. Also, some of the more taboo subjects of proteomics are being addressed. For example, the lack of statistical analyses and the reproducibility of proteomic data sets have plagued proteomics in the pass. Fortunately, extensive research has focused on these issues and has greatly benefited the field of proteomics. In this review, we will attempt to cover major developments in proteomics in the past 2 years. We will highlight major success stories in the field, while outlining the challenges that need to be overcome as it moves into the future. * To whom correspondence should be addressed. Daniel Figeys, phone: 613562-5800ext 8674. Fax: 613-562-5655. E-mail: [email protected]. 10.1021/ac900735j CCC: $40.75  2009 American Chemical Society Published on Web 04/16/2009

QUANTITATIVE PROTEOMICS One of the driving forces during the first decade of proteomics was the need to identify an increasing number of proteins. Although the technical developments were tremendous, from gel to gel free methodologies, for example, the biological relevance of generating protein lists was not always obvious. In reality, the Rosetta Stone for understanding biological systems is finding the biomolecules that are altered in quantity and/or quality (PTM, localization. . .). Therefore, proteomic quantification became another driving force in proteomics. Isotopic labeling coupled to mass spectrometry (MS) techniques is the dominant approach in quantitative proteomics (1). Isotopic labeling uses nonradioactive and nearly chemically equivalent isotopes that have a mass difference which can be separated by MS or MS/MS. This mass difference can be used as a marker to find related peptides while the intensity/area of the peaks is used for relative quantification (2). Proteins and peptides can be labeled with isotopes in vitro or in vivo (tissue culture, small organisms, and most recently small mammals). The most common example of in vivo labeling is stable isotope labeling by amino acids in cell cultures (SILAC) (3). It involves labeling two cell populations with either “light” (natural) amino acids or “heavy” (isotope-labeled) amino acids in culture medium (3). Most commonly substituted stable isotopic nuclei are 2H, 13C, and 15N (1). Since our last review, SILAC has been extensively adopted and has proven to be very useful for quantitative proteomics (4). Furthermore, SILAC has been used to quantify post-translational modifications (PTMs) such as phosphorylation (5), acetylation (6), and methylation (7). A study looking at these modifications in histones was done by Bonenfant et al. (8). They used extracted histones digested with a combination of different proteases to study changes in their PTMs during the cell cycle. By growing HeLa cells and arresting them at different stages, they demonstrated the dynamic nature of histone phosphorylation, acetylation, and methylation during the cell cycle (8). The use of isotopic arginine poses a challenge because of the conversion of arginine to proline in eukaryotes (9). This process results in the conversion of 13C6-arginine and 13C6, 15N4-arginine to 13C5-proline and 13C5, 15N1-proline, respectively, which compromises the accuracy of quantification (9). Many solutions have been proposed to solve this problem such as using a lower arginine concentration (10) and correcting for arginine conversion manually or by mathematical equations (11). Another approach attempts to solve the problem of arginine conversion Analytical Chemistry, Vol. 81, No. 12, June 15, 2009

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by providing an internal correction factor through the production of heavy proline converted from arginine in both light and heavy media (9). A more direct solution to eliminate the problem can be achieved by adding L-proline directly into the SILAC media (12). This idea is based on the fact that the conversion of arginine to proline is a result of the media being depleted of proline. Therefore, the addition of proline to the media will reduce this back conversion. Maintaining a high enough concentration of proline will maintain the cellular homeostasis rendering de novo synthesis of proline unfavorable (12). Another issue is that although SILAC can be used in cell cultures or microorganisms, it cannot be used in tissues and organs. The recent development of a SILAC mouse offers a partial solution to this issue (13). Kruger et al. reported a complete isotopic labeling of mouse F2 generation using a mouse diet that contains 13C-lysine. The special diet is made by mixing either 12 C-lysine or 13C-lysine with lysine-free mouse diet (13). The authors demonstrated this method by comparing proteomes of different knockouts, allowing them to determine protein function at a systems level. They studied the integrin pathway using β1integrin, β-Parvin, and Kindlin-3-deficient mice. By comparing proteomes of deficient mice with ones from the wild type, they were able to demonstrate an important role of Kindlin-3 in the assembly of proteins in the red blood cell membrane (13). Despite the excitement surrounding this development, SILAC labeling of mouse tissue is an expensive technique. The price of the isotopic mouse diet is beyond what most laboratories can afford. Although it has been suggested that labeled organs can be used instead of the whole organism, variability between samples would inevitably be high (14). While this development paves the road for SILAC to be more widely used in tissues and small organisms, it will remain offlimits for use in humans and large primates. Other examples of isotope labeling techniques include chemical labeling using isotopic tags and proteolytic 18O labeling. These techniques postmetabolically label proteins or peptides either chemically or by an enzymatic reaction. Proteolytic 18O involves the labeling of digested peptides by incorporating two 18O on the carboxy terminus of each peptide (15). Comparison between two different samples is achieved by digesting them separately in the presence of heavy H218O or light H216O. The simple nature of this technique makes it extremely attractive. Nonetheless, it still suffers from major setbacks such as backexchange, lack of automated quantitative software, and the small 4 Da mass difference between the two states (1). Chemical labeling techniques using isotopic tags is a more widespread quantitative approach. These techniques are more versatile because they allow many tags to be used. They are more attractive to scientists because isotopic tags can be selectively added. Most popular examples of these techniques are the ICAT and iTRAQ. Since our last review, new modifications have been added to these techniques to answer different biological questions and to improve previous methods. We will briefly touch on the major changes and how they have been used in the field of quantitative proteomics. ICAT (isotope-coded affinity tag) labels cystine residues through a sulfohydryl-reactive iodoacetate group (16). The other 4586

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end of the molecule contains a biotin group for affinity purification, while the isotope-coded region lies between the biotin and the iodoacetate group (16). In a recent study, Chen et al. identified 116 differentially expressed proteins in chronic pancreatitis using ICAT. Their data shows a 40% overlap between the identified proteins and those differentially expressed in pancreatic cancer (17). To identify the effect of treating p53 (K317R) knock-in mice thymocytes with ionizing radiation relative to the wild type, Jenkins et al. used ICAT. They identified 46 proteins whose expression varied in the p53 (K317R) knock-in relative to the wild type upon ionizing radiation (18). A modified technique, termed cleavable ICAT (cICAT), incorporates an acid-cleavable site that allows for the removal of the ICAT reagent’s biotin tag prior to analysis by MS (19). Tan et al. used a combination of cICAT and iTRAQ to successfully show differential protein expression in colorectal cancer cells upon butyrate treatment (20). Another modification that was implemented is the use of oxidation ICAT (OxiICAT). Developed by Leichert, et al., OxiICAT can be used to determine the oxidation state of proteins (21). ICAT was also modified by Hagglund et al. to study Thioredoxin-mediated disulfide reduction (22). ITRAQ (isobaric tags for relative and absolute quantification) (23) is another widely used chemical labeling technique. It has the ability to label all N-terminal amines and lysine residues in a peptide mixture (23). One of its great advantages is the ability to label four or even eight (24) different cellular states. However, this can become a disadvantage, as it leads to increasing complexity of the analysis. ITRAQ has been used to study various diseases such as breast cancer (25, 26), hepatocellular carcinoma (27), type 2 diabetes (28), and Alzheimer’s disease (29). The introduction of these different quantitative proteomics methods has tremendously altered the proteomics landscape. Further development of new methods, combined with advancements in analysis software and new MS instruments, will yield new discoveries. As we will see in the next section, combining quantitative proteomics with other methods, such as protein-protein interaction techniques, promises to expand their scope of use and help answer different biological questions. IDENTIFICATION OF PROTEIN-PROTEIN INTERACTIONS Mapping of protein interactions is essential in proteomics. By building interaction maps, we gain a better understanding of how individual proteins function through their participation in different protein complexes under different conditions (30). In our past review, we focused on large scale protein interaction studies. In the past 2 years, a more targeted approach has been adopted, shifting the focus to more in-depth studies. Since the S. cerevisiae is nearly completed, the focus has shifted from simpler to more complex organisms and toward human protein interaction mapping (31). Our group has published one of the largest human protein-protein interaction mapping studies (32). In this study, 338 human proteins were FLAG-tagged and expressed in human embryonic kidney cells. With the use of anti-FLAG agarose beads, baits were immunopurified and resolved on SDS-PAGE. Gel bands werethenanalyzedusingESI-LC-MS/MStoidentifyprotein-protein interactions in HEK293 cells. MS analyses resulted in the identification of 24 540 potential protein interactions. This protein

list was filtered to increase confidence, and it generated 6 463 interactions between 2 235 unique proteins (32). Wang et al. identified the interaction map of Ras-MAPK/PI3K signaling pathways (33). Using a yeast two hybrid (Y2H) system, they used 44 different baits selected from protein families such as Ras family members of small G proteins, MAP kinases, PI3K subunits, protein tyrosine phosphatases, adapter proteins, or transducers as well as other molecules that interact with members of Ras-MAPK/PI3K signaling pathways. After removing nonspecific protein interactors, they were left with 200 protein interactors. Only a few of those interactors were verified by coimmunopurification and colocalization assays (33). A proteome-wide protein interaction map of the food-borne pathogen Campylobacter jejuni NCTC11168 was generated by Parrish et al. (34). They used Y2H to map protein interactions of 80% of the predicted C. jejuni ORFs. This map offers an invaluable tool to annotate C. jejuni proteins, especially since about 50% of those proteins are currently unknown or poorly characterized. The authors employed different strategies to ensure a low false positive rate. First, they repeated their experiments twice, and then they calculated a probability score that measured the confidence of each interaction according to its biological relevance, resulting in a list of 2 884 high confidence interactions (34). Despite these attempts, a number of false positive interactions were reported. In an attempt to understand bacterial motility, Rajagopala et al. surveyed the whole genome of two hybrid arrays of Treponema pallidum and C. jejuni against known flagellar apparatus proteins to map their interactions (35). Motility genes were selected by screening 3 985 gene deletion strains of Escherichia coli. Data obtained from this screen was integrated with other data obtained from similar screens in other bacteria (35). As a result, 176 interactions involving 110 proteins in T. pallidum were identified along with another 140 interactions involving 133 proteins from C. jejuni (35). They were also able to identify 23 uncharacterized proteins as components of bacterial motility. Taken together, their data shows that although this pathway is well conserved in bacteria, many components of this pathway have gone through evolutionary adaptation. As an example of protein interaction studies in plants, Popescu et al. has generated a high density protein microarray to study the binding of calmodulin (CaM) and calmodulin-like (CML) proteins in Arabidopsis thaliana (36). The protein microarray was constructed using 1 133 proteins and probed with three CaM and four CMLs. It revealed about 173 novel in vitro interactions. Their data shows that different CaMs and CMLs target different proteins, with transcription factor accounting for most targeted proteins (60 out of 173). This study demonstrates the potential of using protein microarray to study the protein interactions in vitro and the potential to expand this technique to larger sets of proteins. The S. cerevisiae interactome shows how completed and refined protein interaction data can further our understanding of underlying biological processes. Although the S. cerevisiae interactome has been extensively studied, new studies are directed at refining the interaction data. Two such studies were recently published in the journal, Science. Using Y2H, Yu et al. were able to identify 1 809 binary interactions, most of which were novel (37). The other study, by Tarassov et al., used protein-fragment complementation assays (PCA) to identify 2 770 binary interac-

tions, most of which were previously unknown (38). Rigorous quality assessments were performed in both studies. This allowed the groups to confidently claim that only a small percentage of the identified proteins could be false positives. QUANTITATIVE PROTEIN-PROTEIN INTERACTION Studying protein interactions by affinity purification coupled to MS (AP-MS) has the great advantage of identifying protein complexes. Identification of protein complexes rather than binary interactions allows for the placement of proteins within their biologically relevant settings. AP-MS is directly affected by advances in mass spectrometry, sample preparation, and bioinformatics (39). However, as MS development allows for higher sensitivity in complex identification, the number of contaminant proteins identified will also inevitably increase. This will lead to a large false positive rate unless more stringent conditions are used to reduce contaminants. Yet, as stringency increases, so does the loss of the weak but biologically relevant interactors. To address this issue of high false positive identification rate, quantitative proteomics has been used. In these studies, mild conditions were used to preserve weak interactions (39). Many studies use quantitative proteomics to study protein interactions. Examples of quantitative techniques in protein interactions include the use of ICAT (40-42), ITRAQ (43-45), and SILAC (46-50). One example of using SILAC to identify weak protein-protein interactions was published by Trinkle-Mulcahy et al. (51). They used green fluorescent protein (GFP) as an affinity purification tag. This tag uses the newly derived GFP binder protein which was derived from llama heavy chain antibody (52). GFP binder has a high affinity and specificity to GFP (51). By using this affinity tag in combination with three different matrixes (Sepharose, agarose, and magnetic beads), TrinkleMulcahy et al. identified the matrixes’ most common contaminants in SILAC labeled mammalian cells. Identification of these proteins from either whole cell, cytoplasmic, or nuclear extracts composes what is called a “bead proteome” (51). Such studies will help establish lists of common nonspecific contaminants that are commonly seen in affinity purification. To validate their method, they used survival of motor neurons (SMN) protein as an example. GFP affinity purification of this protein against a nonspecific control identified most of SMN’s known interactors. However, challenges still remain. For example, this immunopurification method identified a number of validated SMN binding proteins with a ratio similar to those of nonspecific interactors. Some of these specific interactors even had a ratio of less than 1, like SmD1 and SmD2, which are known interactors of SMN (53-55). Another example of quantitative protein-protein interaction can be seen in the new method developed by Wepf et al. (56). In this method, they integrate a small peptide as part of the affinity tag in the construct which, upon digestion, can act as a reference peptide. Similar to AQUA (57), absolute quantification of the reference peptide can be performed. The peptide used is called SH-quant (AADITSLYK). After affinity purification, a certain concentration of the heavy form of the peptide is added. This peptide, SH-quant*, contains a heavy isotope-labeled lysine form. The amount of bait in different affinity purifications is calculated as a ratio of the precursor-ion signals of SH-quant* and SH-quant, respectively. Another form of the SH-quant peptide “SH-quant**”, Analytical Chemistry, Vol. 81, No. 12, June 15, 2009

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which contains heavy isotope-labeled lysine and leucine, was used as a correction factor for sample loss during processing (56). The SH-quant** peptide was added just before LC-MS/MS analysis, and the correction factor was calculated as a ratio between the precursor ion signals for SH-quant* and SH-quant**. This method will offer a great advantage in following the changes in protein complexes under changing environments such as differentiation or a drug treatment (56). POST-TRANSLATIONAL MODIFICATIONS Most proteins are enzymatically modified after their translation by a number of chemical groups. These PTMs such as phosphorylation, glycosylation, and acetylation are key players in modulating proteins function(s). The study of PTMs has also been a driving force of proteomics in the past decade. Unfortunately, our ability to predict most PTMs is limited at best. Furthermore, although the databases of PTMs are growing in numbers, the coverage is rather low. To date, most PTMs need to be discovered de novo using techniques such as MS or a series of conventional biochemical approaches. Improving and developing new methods to detect PTMs has been the focus of many research groups in the past decade. In this part of the review, we will discuss new methods developed to study PTMs. Phosphorylation. Protein phosphorylation has been the central hinge of signal transduction and regulation in biological processes. We have seen a continuous drive for the development of phosphoproteomics technologies throughout the past 2 years. Phosphoproteomics has taken a great leap in concomitance with technological advancements in MS instrumentation. It has benefited from the improvement in mass accuracy, better resolution and sensitivity, methods for phosphopeptide specific enrichment, and associated bioinformatics. MS-based phosphoproteomics is showing increased capability of deciphering complex signaling pathways in various biological organisms under different biological conditions. Bodenmiller et al. (58) systemically assessed the ability of three common phosphopeptide isolation methods including phosphoramidate chemistry, immobilized metal affinity chromatography (IMAC), and titanium dioxide (TiO2). They compare the reproducibility, specificity, and the ability of the techniques to isolate phosphopeptides from complex mixtures. Their findings show that different methods have a preference to different types of phosphopeptides. Nonetheless, partial overlapping segments of the phosphoproteome have been found in the three methods. Over the last 2 years, continuous efforts have been made to improve the specificity and sensitivity of phosphopeptide enrichment. Thingholm et al. (59) presented SIMAC (sequential elution from IMAC) which combined the enrichment selectivity of IMAC and TiO2 to efficiently separate monophosphopeptides and multiply phosphopeptides under different elution conditions. The SIMAC approach greatly improved the detection of multiple phosphopeptides by LC-MS/MS. McNulty and Annan (60) exploited the strong hydrophilicity of the phosphate group to prefractionate phosphopeptides based on their retention under hydrophilic interaction chromatography (HILIC) and subsequent phosphopeptide enrichment by IMAC. HILIC separation prior to IMAC improved the phosphopeptide enrichment to more than 99% without additional derivatization or chemical modification. It also improved the 4588


phosphopeptide recovery in comparison with IMAC alone. HILIC with IMAC also demonstrated a more uniform distribution than SCX with IMAC. Blair et al. (61) developed a new method for high selective enrichment of phosphopeptides by calcium phosphate precipitation of phosphopeptides combined with established IMAC enrichment. The application of this method to a complex peptide sample derived from mice embryo resulted in more than 90% of the phosphopeptides identified in the enriched sample from LC-MS/MS. Ficarro et al. (62) exemplified niobium pentoxide (Nb2O5) for efficiently enriching and recovering phosphopeptides. In another method, Zr4+ and Ti4+-IMAC, which relies on the strong interaction between the metal (Zr4+ or Ti4+) phosphonate and the phosphate group on phosphopeptides, was used to enrich phosphopeptides from simple sample mixtures and real biological samples (63-65). Both of Zr4+ and Ti4+-IMAC demonstrated superior selectivity and efficiency of phosphopeptide enrichment compared to traditional IMAC with nitrilotriacetic acid (NTA) or iminodiacetic acid (IDA) such as chelating ligands and metal oxides (TiO2 and ZrO2). Sugiyama et al. (66) developed novel methods for phosphopeptide enrichment using lactic acid-modified titania and β-hydroxypropanoic acid-modified zirconia metal oxide chromatography. The commercial availability of hybrid MS machines such as LTQ-FT and LTQ-orbitrap and alternative peptide dissociation techniques have improved the analysis of phosphoproteomics over the past 2 years. For example, collision induced dissociation (CID) for doubly charged peptides with an automatic alternating mode has proved to produce better results in profiling phosphorylation sites from complex biological samples (67-69). Recently, Sweet et al. (70) employed online electron capture dissociation (ECD) for the large-scale identification and localization of phosphorylation sites and compared it with CID. They found that the combination of ECD and CID analyses results in high confident identification of phosphopeptides and the localization of phosphorylation sites. Olsen et al. (71) demonstrated that peptides can be fragmented in an LTQ-orbitrap MS with high resolution and full-mass-range MS/MS by higher-energy C-trap dissociation (HCD). This approach proved more effective for identifying tyrosine phosphorylated peptides by detecting the phosphotyrosine-specific immonium ion at m/z 216.0426. The additional octopole collision cell can also facilitate de novo sequencing. As technology has continued to improve, interesting applications of large-scale mapping of phosphoproteome, assessments of phosphorylation-based signaling networks, and the deciphering of the kinome on a systemwide scale have been presented. Bodenmiller et al. (58) presented Phospep, a database containing more than 10 000 unique high-confidence phosphorylation sites, mapping nearly 3 500 gene models and 4 600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. Villen et al. (72) reported the identification of 5 635 nonredundant phosphorylation sites from 2 328 proteins from mouse liver. Swaney et al. (73) performed a large-scale analysis of phosphorylation in human ES cells using both CID and electron transfer dissociation (ETD) MS/MS dissociation methods. This study resulted in the identification of 11 995 unique phosphopeptides, which correspond to 10 844 nonredundant phosphorylation sites. They also reported observing 16 previously unreported phosphorylation motifs found

only in the ETD-sequenced data set. Kru¨ger et al. (74) studied the tyrosine-phosphoproteome of the insulin signaling pathway using an LTQ-FT in combination with phosphotyrosine immunopurification and SILAC in differentiated brown adipocytes. They discovered seven new insulin-induced effectors including SDR, PKC-binding protein, LRP-6, and PISP/PDZK11 (a potential calcium ATPase binding protein), from 40 identified receptors associated with different branches of the insulin pathway. A recent systematic investigation of a potential gas-phase phosphate group rearrangement reaction was conducted by Palumbo et al. They found that this reaction occurs with typical ion trap CID-MS/MS due to the lengthy time scale (millisecond) involved for its activation in ion trap MS and the accessibility of unmodified hydroxyl-containing amino acid (75). Furthermore, the conventional CID-MS3 initiated from the neutral loss of H3PO4 provides ambiguous phosphorylation site assignments. Clearly this is a serious issue because all the MS search engines would still return high confidence scores and low falsepositive rates due to gas phase chemistry issues. Furthermore, the majority of phosphorylation sites reported by highthroughput experiments have not been validated. Therefore, it is important to be aware of these issues when relying on previously identified phosphorylation sites. It might well be that alternative fragmentation methods (ETD or ECD), comparisons with the standard synthesized phosphopeptides sequences, searches for the presence of a sequence of a known kinase motif, quantitative phosphoproteomics, and focused experimental designs will help in accurately identifying phosphorylation sites. In the last 2 years, quantitative MS-based phosphoproteomics was applied to study the kinome and kinase inhibitors. Wissing et al. reported the immobilization of kinase inhibitors for the selective affinity capture of protein kinases (76). They identified 140 different members of protein kinases and more than 200 phosphorylation sites after phosphopeptide enrichment and LC-MS/MS. Moreover, they studied the quantitative changes of 219 protein kinases between S and M phase-arrested human cancer cells using kinase enrichment with SILAC based quantitative MS. Some of these phosphorylations include many protein kinases that are implicated in mitotic progression (77). Alternatively, researchers at Cellzome applied the Kinobeads with multiple immobilized broad selectivity kinase inhibitors to enrich for protein kinases. They treated cells and cell lysate with specific kinase inhibitors at varying concentrations (78). Potential targets of these specific kinase inhibitors and their respective Kd values were obtained by MS in combination with iTRAQ labeling approach. They profiled signaling pathways downstream of target kinases induced by specific inhibitors. Clearly, quantitative phosphoproteomics in combination with enrichment techniques is likely to provide invaluable biological information. Glycosylation. In-depth knowledge of protein glycosylation at the proteomics level, (such as structural information about glycan microheterogeneity), peptide sequence, and functional analyses of the glycoproteome all play important roles in understanding biological processes and their clinical applications. Continuous technology developments in MS-based glycoproteomics and re-engineered glycoproteins such as metabolic labeling have advanced glycoproteomics. Although we have seen an

increase in our ability to map the site of glycosylation on proteins, the study of the glycans attached to these sites remains a gargantuan task. Here, we take a look at some of these emerging developments and their applications in glycoproteomics. Sun et al. (79) reported an enhanced method for hydrazidebased glycopeptides enrichment. This approach has the advantage of reducing sample complexity, sample loss, and improving the MS detection sensitivity and accuracy for low abundance glycosylated proteins. It also demonstrated the versatility of O-linked glycoprotein analysis in combination with O-glycosidase or β-elimination. A systematic optimization of all experimental procedures and data analyses has been performed by Zhang et al. (80). After repeating the experiment with an optimized protocol, more than 85% of potential glycosylation sites were identified from the heavily glycosylated human immunodeficiency virus (HIV) envelope proteins JR-FL, gp140, and ∆CF. Kubota et al. (81) developed a rapid and sensitive method to analyze glycopeptides using lectin and cellulose affinity chromatography in combination with matrixassisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) and MALDI-quadrupole ion trap (QIT)TOF MS. Recently, Zhou et al. (82) developed a glycoproteomic reactor in which affinity enriched glycoproteins are digested into peptides. The highest efficiency in identification of glycopeptides was demonstrated by analyzing microliters of human plasma sample. Alternative approaches based on novel chemical tagging have also been proposed for the comprehensive profiling of glycoproteomics and for understanding the biological functions of glycosylation. Hanson et al. (83) attempted to introduce a new glycoproteomic strategy for saccharide-selective glycoprotein identification. The chemical-tagged glycans are metabolically incorporated into proteins during cell culture. The re-engineered glycoproteins are separated by affinity purification and then identified by LC-MS/MS. In a milder method reported recently, periodate oxidation was exploited to generate an affinity aldehyde tag at sialic acid-containing glycans labeled glycoproteins. The tag is then easily enriched and separated by aniline-catalyzed oxime ligation reaction (84). These technologies are also expected to label, enrich, isolate, and identify sialoglycoconjugates. However, one serious deficiency of the chemical tagging approach is that it cannot be applied to intrinsic proteomes (e.g., human plasma) because the affinity tag present on the modified glycan is not available in natural glycan. In the pursuit of improving glycopeptides detection by MS, Catalina et al. (85) investigated the ETD MS spectra of multiply protonated N-glycopeptides using horseradish peroxidise. Wu et al. (86) and Alley et al. (87) also explored the use of ETD with CID for characterizing glycopeptides. They both concluded that the combination of ETD and CID is more powerful for the elucidation of the glycan structure and the accurate localization of glycosylation sites. The application of glycoproteomics is challenged by the fact that, to date, glycoproteomics has been predominantly limited to finding the sites of attachment of glycosylation. We have seen a gradual increase of specific biological questions, especially for the analysis of clinical samples. Qiu et al. (88) developed a method for glycoproteomic identification of potential plasma markers aimed at detecting colorectal cancer. The potential markers that seem to distinguish colorectal cancer from adenoma and normal Analytical Chemistry, Vol. 81, No. 12, June 15, 2009

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cells include elevated sialylation and fucosylation in complement C3, histidine-rich glycoprotein, and kininogen-1. Sturiale et al. (89) presented multiplexed glycoproteomic analyses of congenital disorders of glycosylation (CGD) by yolk immunoglobulins immunoaffinity separation and MALDI-TOF MS analysis. They also found that CGD-Ia patients showed a typical profile of underglycosylation where the fully glycosylated glycoforms are always most abundant in plasma with lesser amounts of partially and unglycosylated isoforms. Hu¨lsmeier et al. (90) quantified Nglycosylation occupancy in healthy control samples and in CGD samples based on multiple reaction monitoring LC-MS/MS. They observed a reduction in N-glycosylaton site occupancy that correlated with the severity of the disease. Vercoutter-Edouart et al. (91) applied a glycoproteomic approach based on Con A lectin affinity chromatography, MS analysis by MALDI-MS, and GC/ MS analyses of permethylated derivatives to investigate HT-29 epithelial colon cancer cells. They found that, in addition to the modifications of sialic content of individually identified N-glycoproteins involved in cellular adhesion and permeability of intestinal epithelial cells, the major changes are the expression of GlcNAcended N-glycans that occur in enterocyte-type cells. It is fair to say that although many technical hurdles have been addressed, time will only tell if the potential biomarkers discovered by glycoproteomics are specific to the disease of interest or if they are reflective of a systemic response that is general to many diseases. Lipidation. Protein lipidation is a crucial protein modification that involves covalent attachment of hydrophobic carbon skeletons of the various lipid classes (fatty acids, sterols, glycero-, phosphoand glycolipids) (92). This type of modification affects 2-4% of all proteins in a given proteome. A number of methods have been developed to study protein lipidation. The high molecular weight nature of many lipid groups and few functionalities act as handles for antibody-based recognition, chemoselective, or enzymatic tagging of lipidation (93). In this section we will discuss new technologies that emerged in the past 2 years that have provided significant advantages for studying protein lipidations. Roth et al. (94) described a new proteomic method that purified and identified palmitoylated proteins with orthogonal tags to characterize the palmitoyl proteome of S. cerevisiae using LC-MS/MS. Using this approach, they identified 12 of the 15 known palmitoyl proteins plus 35 new palmitoylated proteins including many soluble (N-ethylmaleimide sensitive factor (NSF) attachment protein receptors (SNARE) proteins and amino acid permeases. They also identified many other proteins that are involved in cellular signaling and membrane trafficking. In a different method, a modified 17-octadecynoic acid with an alkyne affinity tag was used to metabolically label palmitoylated proteins, which were then enriched and identified by LC-MS/MS. As a result, a total of 125 predicted palmitoylated proteins, including G proteins, receptors, and a family of uncharacterized hydrolases, whose plasma membrane localization depends on palmitoylation, were identified (95). Sakurai et al. (96) demonstrated that in vitro translation of cDNA coding for N-myristoylated protein in the presence of 3Hmyristic acid followed by SDS-PAGE and fluorography is a rapid detection method for protein N-myristoylation. This seems to be a relatively simple and effective strategy to detect post-translational protein N-myristoylation in combination with 4590


LC-MS/MS. Nguyen et al. (97) have profiled the eukaryotic prenylome by structure-guiding of engineered protein prenyltransferases and their universal synthetic substrate, biotingeranylpyrophosphate. The engineered protein prenyltransferases can faithfully deliver biotin-geranyl to their cognate substrates lysates, thus allowing identification and quantification of unmodified prenylations. This approach allows femtomolar quantities of prenylation substrates to be detected and can be used for relative quantification of protein prenylation and their modulation by therapeutic agents. One limitation of the metabolic labeling approaches is that they are not applicable in animal models or human samples. However, we expect that these tools and other tools that are developed for lipidomic studies will greatly enhance our understanding of the role of protein lipidation and lipids in biological processes. Ubiquitination and SUMOylation. Ubiquitin (Ub) and small Ub-like modifier (SUMO) proteins covalently attach to the side chain of lysine residues via an isopeptide bond. They regulate many essential cellular processes including protein degradation, cell cycle, transcription, DNA repair, and membrane trafficking. Disrupted Ub signaling may have broad consequences for neuronal function and survival (98). Generally, most literature in this field emphasizes the importance of the biological pathways that are induced by ubiquintation and sumoylation rather than the development of methodology for analyzing protein ubiquitination or sumoylation in the past 2 years. Denis et al. (99) improved an MS-based strategy of ubiquitination analysis by identifying two signature peptides containing a GG-tag (114.1 Da) and an LRGGtag (383.2 Da) on internal lysine residues, as well as a GG-tag found on the C-terminus of ubiquitinated peptides. Vasilescu et al. (100) applied the proteomic reactor to facilitate affinity purification and identification of ubiquitinated proteins by LC-MS/ MS. Xu et al. (101) presented a middle-down MS strategy to characterize the length and linkage of polyUb chain structures. Recently, Blomster et al. (102) developed a novel method based on SUMO protease treatment to remove the desumolyation of substrates that reduced the complexity of SUMO substrates on SDS-PAGE separation before LC-MS/MS analysis. Matic et al. (103) developed an alternative MS strategy with the assistance of high resolution MS to localize sumoylation sites. Moreover, the approach demonstrated the prospects for analyzing highly complex biological samples. Maor et al. (104) performed large scale affinity purification and identification of ubiquitinated proteins from Arabidopsis thaliana with multidimensional protein identification technology. The application of this approach resulted in 382 SUMO-2 targets, of which more than half of the consensus sites are unknown. Mayor et al. (105) quantitatively profiled ubiquitinated proteins by perturbing the Rpn 10 receptor using reference cultures of S. cerevisiae with SILAC. Meierhofer et al. (106) performed quantitative analyses of global ubiquitination by two steps of Ni2+-NTA and hexahistidine-biotin tag affinity purification and LC-MS/MS analysis for untreated cells and cells treated with the proteasome inhibitor MG132. Bennett et al. (107) exploited an MS-based method to quantify global changes to the ubiquitination system in Huntington’s disease. Although MS has become a powerful tool for mapping the Ub and SUMO, it is very important to highlight the potential misidentification of iodoacetamide adducts as ubiquitination sites

which was recently reported by Nielsen et al. (108). Basically, two molecules of iodoacetamide can produce a mass shift at the lysine residues. This mass shift is isobaric with the diglycine linkage for Ub and SUMO and therefore can lead to misidentification. To address this problem, the authors recommended changing the reagent from iodoacetamide to chloroacetamide or to other alkylating reagents that do not produce the artifact mimics of ubiquintination in MS. Meanwhile, the previously reported ubiquitination sites based on diglycine linkage in the presence of iodoacetamide should be re-evaluated. However, this does not apply to sites identified with LRGG linkages and diglycine linkages which were validated by other biochemical means. Acetylation and Methylation. Acetylation is highly dynamic and has been linked to many cellular processes such as gene silencing, cell cycle progression, apoptosis, differentiation, and DNA replication (109-111). Methylation, in contrast, has been considered a stable modification that regulates transcriptional repression and activation, transcriptional elongation, heterochromatin formation, X-inactivation, and polycomb-mediated gene silencing (112). Although these modifications are very important, the MS-based analytical methods to characterize protein acetylation and methylation are still limited. Furthermore, very few papers report methodology development to analyze acetylation and methylation by MS. Wu et al. (113) proposed a strategy to determine the acetylation sites of proteins using MS to trace mass differences resulting from the in vitro acetylation reactions with isotope-labeled and unlabeled acetyl groups. Methylation of proteins on lysine or arginine is expected to increase the mass of the residue by a multiple of 14 Da depending on the number of methyl added. Therefore, a tempting strategy is to look for this mass shift in MS/MS spectra of peptides to identify protein methylation. Jung et al. (114) found that a large number of peptides can be modified on the lysine, arginine, histidine, and glutamic acid residues with a mass increase of 14 or 28 Da. So that seems problematic, even though the mechanism for this mass increase eluded their report. Amino acid substitution can also cause mass increases that are multiples of 14 Da while acetylation has a mass very close to trimethylation. Here again, there is a strong potential risk of misidentification of methylation. Therefore, one should consider strategies that are combined with mass spectrometric identification using in vivo isotope labeling as described by Ong et al. (115). Moreover, regardless of the strategy selected, other biochemical means should be used to validate methylation sites. A combination of metabolic labeling and top-down mass spectrometry has also been used to study the regulation and function of methylation of histone H4 at lysine 20 (116). Top down mass spectrometric coupled with 2D-HPLC was used to characterize different forms of Histone 4 from HeLa cell (116). The relative quantification of 42 H4 isoforms which were uniquely modified by methylation and acetylation was also performed. In another report, an MS and genomewide analysis was used to verify the acetylation of Lysine 56 of Histone 3 in the core transcriptional network in human embryonic stem cells (117). Methylations of proteins’ nonhistones were also reported. In particular, specific sites of co- and post-translational modification of cytosolic ribosomal proteome in A. thaliana including acetylation, methylation, and phosphorylation were measured through

a combination of in silico approaches coupled to MS analysis (118). Yang et al. (119) identified seven lysine residues in Hsp90 by MALDI-TOF MS and MS/MS that are hyperacetylated once the histone deacetylase (HDAC) 6 and the pan-HDAC inhibitor are knocked-down in eukaryotic cells. Hyperacetylation is closely related to the modulation of the intracellular and extracellular function of Hsp90. The identification of N-terminal protein methylation of the regulator of chromatin condensation 1 was demonstrated by high-resolution and accurate ECD-MS (120). Finally, Sprung et al. (121) reported the identification of in vivo aspartate and glutamate methylation in eukaryotic cells by nanoHPLC-MS/MS. Despite the technological progress for studying protein PTMs using MS, the number of biological applications remain very limited, especially for the high-throughput experiments. Protein phosphorylations have been the most studied modification with an increasing number of phosphorylation sites being mapped by high-throughput approaches. Although these results populate databases, the biological context for the experiment is often missing. Perhaps more benefits will come from the coupling of these approaches with affinity purification methods to target pathways and complexes. CHEMICAL PROTEOMICS Chemical proteomics attempts to identify and characterize the interactions between small molecules and proteins. Typically, this is done by labeling or immobilizing certain small molecules (e.g., drug, biomolecule, and reactive chemical structure), which is used to enrich a group of proteins. MS is then used to identify these proteins. This approach greatly reduces the complexity of the sample, which facilitates the detection of lower abundance proteins. With aid from MS-based quantitative proteomics, chemical proteomics is becoming a powerful technique for drug discovery and monitoring protein functions (122, 123). PROBE STRUCTURE IMPROVEMENT Generally, the chemical probe for chemical proteomics has three major molecular structures: tag, linker, and binding group. The tag is used for detecting and enriching target proteins from the complex proteome. For enrichment, a binding group can be immobilized directly onto various kinds of hydrophilic matrixes. This is a well-established approach for studying drug-protein interaction. The biotin group is the most commonly used tag for enrichment due to its high affinity to avidin and its biocompatibility. Recently, Eden et al. applied fluorous affinity tags to selectively enrich metabolites and peptides (124). The advantage of the fluorous tag is its high affinity to fluorous environments rather than an organic or aqueous phase. Saxena et al. introduced a peptide containing FLAG epitope into a chemical probe, which can be recognized by antibodies with high selectivity (125). Fluorescence-based tags are commonly used in chemical probes for detection (126). Chen and co-workers further explored the unique characteristics of this type of tag as a reporter ion for enrichment purposes and MS/MS (127). One disadvantage of this tagging approach is the bulky chemical structure which generates extral steric hindrance for the molecular recognition (128, 129). One potential solution is the use of tags based on click chemistry. The unique characteristics of the click chemistry tagging approach Analytical Chemistry, Vol. 81, No. 12, June 15, 2009

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are the negligible chemical structure and the high-efficiency of adding different reporter groups under mild biocompatible conditions. Finally, stable isotopes have also been introduced into various chemical probe structures for quantitative chemical proteomics (130, 131). The chemical properties of the linker can affect the results from chemical proteomics. For example, linkers that are too short or too bulky can cause steric hindrance, while the hydrophilic and hydrogen binding properties of the linker can cause nonspecific interactions. A hydrophobic chain based on an alkyl unit is a common linker for chemical probe synthesis. Because of its hydrophilicity, the use of a polyethylene glycol (PEG) is ideal as a linker for protein enrichment. However, both alkyl and PEG units exhibit unexpected folding if the length of the chain becomes too long. Recently, Sato et al. developed a long, rigid polyproline helix as a linker for target protein enrichment (132). By introduction of nine L-prolines, a stable left-handed helix linker with a length of 27 Å was obtained. To avoid nonspecific adsorption of the chemical probe with abundant proteins, Verhelst et al. reported a mild chemically cleavable linker system based on diazobenzene derivatives (133). By simple incubation of the chemical probe carrying the cleavable linker with 100 mM Na2S2O4 for 30 min, the linker can be fully cleaved. Another type of cleavable linker based on a disulfide bond was also reported recently (134, 135). In this method, the target proteins can be eluted selectively by DTT, thus minimizing the nonspecific binding proteins. To minimize the possible loss of target proteins during the enriching and washing steps, a photoaffinity labeling group is more routinely used in chemical probes to covalently bind the target proteins under mild UV irradiation (136-140). On the basis of the structure-activity relationship study, Kawamura and co-workers found that higher conformational flexibility, but not higher binding-affinity, is more important for efficient photolabeling (141). APPLICATIONS OF CHEMICAL PROTEOMICS To date, intense efforts in chemical proteomics have focused on the characterization of the cellular targets of kinase inhibitors. For example, the drugs imatinib, nilotinib, and dasatinib are the frontline chemotherapeutic agents that were recently introduced for treating chronic myeloid leukemia. Rix et al. developed three chemical probes based on these drugs to study their potential kinase targets. On the basis of their study, several kinases, including tryosine kinase DDR1, oxidoreductase NQO2, and a large number of Tyr- and Ser/Thr-kinases, were identified as interactors of the drugs (142). Another study published by Hantschel et al. demonstrated that Tec kinases Btk and Tec are the major binders to dasatinib-based affinity matrix (143). Further biological validation demonstrated the potential immunosuppressive side-effects of this drug by the inhibition of Tec kinases. Immobilized ATP and competition assays have been used by Duncan et al. to characterize the inhibitor’s specificity and potential protein targets of three kinds of CK2 inhibitors (144). Chemical proteomics can be applied in profiling the noncovalent interactome of different types of biomolecules. Using immobilized cAMP and an optimized protocol for purification and elution, Scholten et al. reported direct identification of low abundance cAMP signaling proteins from mouse ventricular tissue (145). Mallikaratchy et al. developed an aptamer-based chemical proteomic approach for exploring target proteins from live cell 4592


membrane (146). A photoaffinity aptamer probe for targeting Ramos cells was developed, and its target protein on the cell membrane was identified as immunoglobin heavy mu chain. Kalisiak et al. reported the application of chemical proteomic approach for the identification of proteins targeted by endogenous metabolites found through untargeted metabolomics (147). Through identification and immobilization of a new endogenous metabolite, N4-(N-acetylaminopropyl) spermidine, 11 potential target proteins were identified by MS. By introduction of a biotin group into peptide fragments from R-synuclein, McFarland et al. studied protein-protein interactions of the protein and the effects of different phosphorylations (148). In biological systems, various cellular functions are mediated by the covalent interaction of small molecules and proteins such as PTMs and active site-based regulation. It is possible to take advantage of covalent interaction properties for chemical proteomics. For example, activity-based probe (ABP), in which an enzyme family is covalently labeled at the active site, is a valid chemical proteomic approach (149). Gillet et al. achieved in-cell labeling of serine hydrolases using a novel fluorophosphonate based ABP and applied this ABP to profile serine protease inhibitors (150). By implementing a reporter-tagged fluorophosphonate-based ABP, Li et al. reported the discovery of an inhibitor for an unannotated serine hydrolase, R/β-hydrolase domain 6, from a library of inhibitors based on the carbamate reactive group (151). Barglow et al. used high-resolution crystallography and molecular modeling to characterize the active site of Nit2 nitrilase and its binding to dipeptide-chloroacetamide activity-based proteomics probes (152). Everley et al. developed a cleavable ABP toward serine hydrolase and applied it in a quantitative chemical proteomic study in combination with SILAC (134). The first ABPs for steroid sulfatases were reported by Lu et al. who also demonstrated the potential of dot blot analysis for inhibitor screening of steroid sulfatase (153). Hwang et al. reported the development of an ABP to specifically tag chloroacetyl coenzyme A dependent proteins (154). After developing two pairs of biotinylated, cleavable, isotope-coded ABPs toward endoglycosidases, Hekmat and co-workers applied them to study the relative expression/activity levels of endoglycanases (131). PTMs are covalent modification of proteins with different chemical structures. Recently, chemical proteomics has been applied to selectively label and identify different forms of PTMs. On the basis of kinase-catalyzed biotinylation, Green et al. successfully labeled protein phosphorylation sites with a biotin group for the enrichment and identification of the labeled proteins (155). Khidekel et al. reported the quantitative proteomic study of O-GlcNAc glycosylation in the brain using a chemoenzymatic tagging and isotopic labeling strategy (156). Using click chemistry technology, this laboratory developed an advanced strategy to facilitate in-gel and in vivo labeling of O-GlcNAc glycosylation (129). Hanson et al. developed bio-orthogonally tagged alkynyl saccharides for saccharide-selective glycoprotein identification and glycan site mapping (83). Hang et al. developed a serial of ω-azidofatty acids for visualizing protein fatty acylation, including Nmyristoylation and S-acrylation (157). Utilizing 17-octadecynoic acid as a chemical probe, Martin et al. reported the in situ labeling, identification, and verification of protein palmitoylation on a global scale (95). Nguyen et al. revisited the protein prenylation using a

synthetic substrate, called biotin-geranylpyrophosphate (97). By modifying the protein activity of both farnesyltransferase and protein geranylgeranyltransferase type I based on a structureguided protein engineering approach, the authors identified the prenylation substrates catalyzed by all of the three kinds of prenyltransferases. On the basis of metabolic labeling, Dieterich et al. reported a chemical proteomic method to label and identify newly synthesized proteomes by using the noncanonical amino acid azidohomoalanine (158). Clearly, the repertoire of binding groups that can be integrated into chemical probes has rapidly increased, and the field of chemical proteomics is promising to be very successful for the study of different aspects of biology such as target protein profiling, postmodification characterization, etc. One could foresee being able to assess the whole proteome through a series of chemical probes. This would greatly simplify the analysis of the proteome. ANALYTICAL TECHNIQUES Proteomics technologies have been very successful for studying lower complexity proteomes (fungi, etc. . . .). However, the technology is still the limiting factor for the exhaustive analysis of proteomes from more complex organisms. The development of technologies to extend the dynamic range, and the peak capacity of analytical techniques is actively being pursued by many groups around the world; in particular, the development of electrophoresis, protein chips, and liquid chromatography and MS. Electrophoresis. Sample preparation is important for a successful proteomic analysis. Electrophoresis is still the most commonly used technique for sample separation. Over the years, electrophoresis techniques have improved to meet the increasing demand for sample separation. Protein samples are usually extracted from the cells or tissues using different lysis buffers containing salts, denaturants and detergents. However, direct analysis of these extracts by MS is often not possible due to the incompatibility of the lysis buffers with MS. To handle these incompatibilities, Liu et al. (159) developed a method named Three-layer Sandwich Gel Electrophoresis (TSGE) that allows the proteins to be rapidly cleaned-up. Briefly, a threelayer sandwich gel is assembled in a 4 mL Electro-Eluter glass tube with an acrylamide sealing layer (bottom), an acrylamide concentration layer (middle) and an agarose loading layer (top) (159). By electrophoretically driving the proteins from the agarose matrix into the concentration layer, the proteins of interest are desalted and concentrated, which facilitates downstream proteolytic digestion and LC-MS analysis (159). Electrophoresis can be used not only for protein sample cleanup, but also for size-based protein separations. To achieve a broad mass range proteome separation in a fast, effective, reproducible, and high-yield format, Tran and Doucette (160) established a separation technique, termed gel-eluted liquid fraction entrapment electrophoresis (GELFrEE). The GELFrEE device is composed of four major parts: a cathode chamber, the gel column, a collection chamber, and an anode chamber (160). The gel column is used to separate proteins according to their intrinsic molecular weights, and the proteins are ultimately eluted from the column and gathered in the solution phase. This method allows for rapid (as fast as 1 h), broad mass range (from below

10 to 250 kDa) proteome separations in the low-microgram to milligram range. Conventional two-dimensional gel electrophoresis systems are time-consuming and labor-intensive for protein separation. A miniaturized, fully automated 2DE system was developed by Hiratsuka et al. (161). Once the samples and buffers are installed, all of the subsequent procedures are performed automatically within 1.5 h (161). This system can offer fast, practical, portable, and automatic two-dimensional electrophoresis (161). Furthermore, Emrich et al. developed a microfluidic separation system for performing two-dimensional differential gel electrophoretic (DIGE) separations of protein complexes (162). However, these gel-based methods suffer from a few shortcomings. They are unable to isolate proteins with extreme molecular weight and pI value; and they have narrow dynamic range and limited sensitivity. Protein Chip. Protein chips, also known as protein microarrays, have been employed for antibody-based assays or purifications and for studying protein-protein interaction mapping, protein kinases substrates, and targets of biologically active small molecules. Fan et al. (163) developed an integrated blood barcode chip that can achieve sensitive measurements of a large panel of protein biomarkers over broad concentration ranges. One important caveat is that miniaturize devices can often only handle very small sample volume, which means that biomarkers of lower concentration are not likely to be observed. Beyer et al. (164) developed a new method for combinatorial synthesis of peptide arrays onto a microchip. This method can perform particle-based in situ synthesis of peptides by embedding activated amino acids within particles. These particles are addressed onto a chip by electrical fields generated by individual pixel electrodes. Ramachandran et al. (165) described a next-generation, high-density, self-assembling functional protein array for producing fresh protein in situ, with high yields of protein expression and capture with minimal variation and good reproducibility. This will pave the way for the study of protein function in both large scale and high throughput. Liquid Chromatography and Mass Spectrometry. There have not been any recent breakthrough developments in liquid chromatography (LC) techniques. One novel chromatographic method to be highlighted (RePlay system) allows online reanalysis of protein sample (166). The method involves a very simple setup: an analytical column, a splitting valve, and a focusing column. Through postcolumn splitting, one portion of the sample is directly analyzed, and the other is transferred to a capture capillary (focusing column) where it is stored. After the first experiment, the sample collected in the focusing column can be reanalyzed (166). Other interesting developments include some improvements in ion chromatography, such as combinations of weak-anion exchange (WAX) and SCX resins (167), monolithic columns for strong cation exchange (SCX) chromatography (168), and pH gradient elutions (169, 82, 170, 171). One challenge in LC-MS is improving the limit of detection to effectively analyze low-abundance proteins. The peptide hydrophobicity is a main determinant of the electrospray ionization response. To increase the electrospray ionization response, Frahm et al. (172) demonstrated a strategy named ALiPHAT (augmented limits of detection for peptides with hydrophobic alkyl tags), i.e., adding an octylcarboxyamidomethyl modification via alkylation Analytical Chemistry, Vol. 81, No. 12, June 15, 2009

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chemistry to cysteine-containing peptides. Another challenge for MALDI-MS is sample preparation and the choice of matrix. Garaguso et al. (173) improved the analysis of peptides by MALDIMS by using the 2,5-dihydroxybenzoic acid matrix and prestructured sample supports (matrix layer). Imaging MS (IMS) has been one of the most exciting new applications of MS in recent years. It can be used to investigate the distribution of molecules within biological systems through the direct analysis of thin tissue sections (174-176). MALDI and secondary-ion MS (SIMS) are mainly employed for imaging MS. However, they have some shortcomings: the spatial resolution of MALDI-IMS is limited due to the matrix crystal size (typically more than 10 µm), while secondary-ion MS has extremely high lateral resolution (100 nm) but leads to fragmentations of analyte molecules. An alternative approach was developed by Northen et al. (177) that uses “initiator” molecules trapped in nanostructured surfaces or “clathrates” to release and ionize intact molecules adsorbed on the surface, nanostructure-initiator MS (NIMS). This method allows high lateral resolution (about 150 nm), has high sensitivity, is matrix-free, and reduces fragmentation. There are other interesting developments in MS worthy of mention. The introduction of HCD in the LTQ Orbitrap XL has been used to perform peptide fragmentation in the nitrogen-filled octopole collision cell (71). After fragmentation, the resulting product ions re-enter the C-trap and are then analyzed by the Orbitrap. The HCD technique can be applied for peptide identification, for peptide de novo sequencing, and for the sequencing and quantification of iTRAQ labeled peptides. Besides conventional CID, ECD, and ETD, Chen et al. (178) introduced a new fragmentation method, named ambient thermal dissociation. This new technique allows the separation and reionization of neutral fragments at ambient pressure outside of the mass spectrometer. It can provide useful sequence information from both ionic and neutral fragments via direct thermal dissociation and from neutral fragment reionization (178). In addition to fragmentation methods, algorithms are also critical in tandem MS. A data-dependent decision tree algorithm (DT) was developed by Swaney et al. to make unsupervised, real-time decisions of which fragmentation method to use based on the precursor charge and m/z (179).

capture, annotate, and process 3D structures using a common standard (190). Another approach which can be used to identify PPIs is called primary protein structure. This method is based on the hypothesis that PPIs are mediated through a specific number of short polypeptide sequences. Support vector machine (SVM)-based techniques have shown that the primary sequence can be used to predict PPIs (191, 192). Shen et al. introduced an SVM-based method combined with a kernel function and a conjoint triad feature abstract that helps to predict PPI in human proteins (193). In order to reduce the problem of overfitting, which occurs when the learning performed is too long or when the training examples are rare, they used more than 16 000 PPI pairs to generate prediction models. With this method, Shen and his colleague have been able to obtain an average prediction accuracy of 83.90%. Wang et al. have recently developed an automated method called InSite (Interaction Site) for identifying PPI binding sites on a proteomewide scale. This method used multiple categories of characteristics/approaches as input information to predict specific binding regions such as a library of conserved sequence motifs, a heterogeneous data set of PPI obtained from multiple assays, and any other indirect evidence of PPI and motif-motif interactions (194). After integration of the information in these data sets, InSite generates a prediction in the form of “Motif M on protein A binding to protein B”. InSite’s algorithm is based on the following three assumptions: (i) the interaction between pairs is created from high-affinity sites on the protein sequences, (ii) the binding sites are covered and characterized by motifs or domains (supported by Caffrey et al.) (195), and (iii) the same motif is participating in the mediation of the multiple interactions. To determine a prediction, InSite models the noise from a highthroughput assay and from the possibility that two proteins from the same complex do not interact. Because of the use of both the assay and the noise for the identification of the complexes, the interactions data set is bigger than any used before, thus, providing a higher coverage and an increase of robustness. The InSite source code is publicly available at http://dags.stanford. edu/InSite/. QUANTITATIVE SOFTWARE DEVELOPMENT

BIOINFORMATICS Protein-Protein Interactions. In the past decade, various methods have been used to create protein-protein interaction (PPI) maps, such as Y2H (180, 181), TAP-tagging (182, 183), and protein chips (184, 185). Creation of these maps paved the way for the development of computational methods that uses this data as a learning set to predict protein interactions (186, 187). In this section, we will explore the most recent computational methods developed to predict protein structures and interactions. The use of three-dimensional (3D) protein structures to predict physical binding has become increasingly common over the past years (188, 189). Recently, Berman et al. have reported the foundation of the worldwide Protein Data Bank (wwPDB) (190). The wwPDB was not only created to recognize the international nature of the PDB archive but also to ensure that the content and the format of data remain uniform. The PDB archive now has more than 40 000 structures. Moreover, the wwPDB has created tools (ADIT, ADIT-NMR, and AutoDep) that are able to 4594


In the past 2 years, a couple of new quantitative software applications and packages have been introduced to help researchers resolve the computational challenges involved in quantitative processes. Park et al. have developed a quantitative analysis software tool called Census (196). It can handle data that is derived from most types of quantitative proteomics labeling strategies such as SILAC (3) and iTRAQ (197), as well as labelfree experiments. The Census software is based on RelRex, a program previously built by the same group (198). Census is capable of performing quantitative analysis of MS or MS/MS scans, and it currently supports MS1/MS2, DTASelect, mzXML, and pepXML as input file formats. Census includes features such as (i) the ability to use high resolution and high mass accuracy to improve the quantification, (ii) the ability to perform quantification from spectral counting and LC-MS peak area, (iii) multiple algorithms (weighted peptide measurements, dynamic peak finding, and postanalysis statistical filters) which are used in order to reduce the false positive and improve the protein/peptide ratio,

(iv) the detection of singleton peptides. This software is available at http://fields.scripps.edu. In 2007, Lu et al. described a novel technique for protein quantification called absolute protein expression measurements (APEX) (199). This technique was developed to solve the problem of peptide physicochemical properties that affect peptide spectral counts. This is mainly a quantitative technique used in MS based on label free protein (199). The APEX approaches incorporate a machine learning method to classify the derived peptide detection probabilities that are used to predict the number of tryptic peptides expected and to create a correction factor for each protein to improve the result over basic spectral counting. APEX features a Z-score analysis for the identification of protein expression, cross validation utility, and a merge utility for multiple APEX results. The APEX software source code is available at http://pfgrc.jcvi.org/ index.php/bioinformatics/apex.html. Mueller et al. developed a new open source software package for label-free quantification of high mass resolution LC-MS data, named SuperHirn (200). This platform detects and tracks features in LC-MS patterns, combines them into a MasterMap, and then normalizes the features’ intensity across samples. This will eventually lead to normalizing the feature profile trend by clustering. The clustering profiles are used to identify peptides and proteins after making a statistical correlation with the theoretical protein concentration (200). SuperHirn is compatible with mzXML formatted Qtof, FT-LTQ, and Orbitrap data and imports MS2 information in pepXML format. SuperHirn was programmed in C++; the source code and all the documentation are available at http://tools.proteomecenter.org/Super-Hirn.php. MaxQuant is an integrated suite of algorithms developed by Cox and Mann for high-resolution quantitative MS (201). MaxQuant features a pipeline peak list generation algorithm, SILAC based quantification algorithms that create a three-dimensional object in m/z for the peptide pairs, false positive rate determination algorithms based on search engine results, peptide to protein group assembly, data filtration, and visualization. Cox and his colleague have been able to increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via the unambiguous assignment of the isotope and miscleavage state and individual mass precision (201). MaxQuant was developed for the.NET framework and is written in C++. The executables are available at http://www. maxquant.org/. The source code of the algorithms is available as supplementary data in Cox and Mann’s paper. DATABASE RESOURCES In the past decade, high-throughput techniques have generated a massive amount of data. These data have been stored in numerous databases. The database development has been a highly productive segment in bioinformatics. While a complete listing of all database development innovations is not feasible in this paper, we will attempt to focus on a few of those databases. Recently, Depledge et al. introduced a new database of amino acid repeat sequence named RepSeq that clearly differentiates between all repeat types (202). RepSeq is a web-based database application developed to identify repeat sequences from lower eukaryotic pathogens, but it can be used to study proteomes from any given organism. The RepSeq algorithm was developed in PERL and is able to identify both perfect and mismatch repeats. This application is able to identify SAARs and DPRs 100% of the time from a repeat

of 6 residues or longer, compared to 99.8% of the time with SRRs from a repeat of 3 residues and above (202). RepSeq is available at http://repseq.gugbe.com. UbiProt is one of the latest databases developed for ubiquitination (203). The UbiProt web interface software was created from PHP + SMARTY template framework, and it is managed on MySQL 4.0. This public database contains more than 400 individual proteins from multiple organisms. This information includes the target protein, the ubiquitination sites, the structures of multiubiquitin chains, and the features of the ubiquitination machinery. Unlike other databases that contains ubiquitinated proteins, such as Swiss-Prot (204) and the Human Protein Reference Database (HPRD) (205), UbiProt supports complex queries, it does not have the search redundancy problem (it returns not only pure ubiquitinated proteins but also numerous enzymes from the ubiquitination cascade), and it does not lack information about ubiquitinated sites (203). UbiProt is available at http://ubiprot.org.ru. Kuntzer et al. have recently introduced BNDB (biochemical network database), a database that contains a complete semantic integration of the data from Swiss-Prot (204), RefSeq (206), KEGG (207), BioCyc (208), TransPath (209), DIP (210), MINT (211), IntAct (212), HPRD (205), and TransFac (213). BNDB is based on an oriented object data model called BioCore and is implemented on the MySQL relational database. This warehouse takes advantage of three ways to access the data: (i) a web interface which allows the user to create multiple types of searches, (ii) a network visualizer called BiNA that allows the user to visualize the metabolic and regulatory networks in a sophisticated graph layout, (iii) a programming interface that offers a collection of implemented analysis routines. BNDB can be accessed at http://www.bndb.org. Chatr-aryamontri et al. published a new version the Molecular Interaction (MINT) database in 2007. MINT has undergone a profound reorganization of both the data model and the data structure over the past 4 years (214). The database has adopted the IntAct (212) relational model that consists of representing the protein complexes and the other types of molecules as interaction partners (214). In addition, MINT is now compatible with all the tools developed by the IntAct consortium. MINT now has more then 95 000 physical interactions involving 27 461 proteins from 325 organisms (214). MINT is based on the PostgreSQL database management system and the data can be accessed as Java objects through the IntAct (212) by using the Apache Object Relational Bridge (OBJ). The MINT data is generated from experimental protein-protein interaction data extracted from curated literature. The MINT database is accessible at http://mint.bio.uniroma2.it/mint/. MANAGEMENT SYSTEM A laboratory information management system (LIMS) is a software package that manages laboratory samples, instruments, users, and data. LIMS is divided into two categories: (i) enterprise LIMS is designed to link the laboratory system to an organization system and can generally be quite expensive and (ii) a freely available LIMS, which is an open source so that the user can change and improve the software. Mass Spectrometry Analysis System (MASPECTRAS) is a new, free, management system developed by Hartler and his colleague for the analysis of proteomics LC-MS/MS data (215). MASPECTRAS allows for Analytical Chemistry, Vol. 81, No. 12, June 15, 2009

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comparison of the results of multiple search engines such as SEQUEST (216), Mascot (217), Spectrum Mill, X! Tandem (218), and OMSSA (219). This software management system integrates peptide validation, Markov clustering algorithm, and ASAPRation algorithm for quantification. Moreover, it includes customizable data retrieval and visualization tools. MASPECTRAS is available at http://genome.tugraz.at/maspectras. IntelliMS is a basic management system developed by Kwon et al. at the Yonsei Proteome Research Center (220). The key functions of this system are (i) data importation in mzXML and mzData fotmat, (ii) a score filtering system that uses the empirical Bayes (221) and target-decoy search approaches (222), (iii) an ID Network that not only facilitates navigation through the protein identification process but also shows how the same mass spectrum can identify different proteins or peptides from various search engines, and (iv) a data sharing and conversion system that allows a user to share a project and to convert the data to a PRIDE XML or MS Excel file. IntelliMS is publicly available at http:// intellims.proteomix.org and http://intellims.sourceforge.net. CONCLUSION The past 2 years have shown that proteomics is quickly maturing and is able to offer real solutions to biological problems. A good example of proteomic advancement is the mapping of protein interactions and the potential it has shown for the ongoing mapping of the human interactome (223). Nonetheless, there are some serious issues in proteomics in terms of data quality and the biological validations of the results. A blatant example has been in the area of biomarker discovery, where many reports of novel biomarkers were never validated. Furthermore, as we increase throughput and optimize techniques, we should remain careful about the quality of the data generated and its usefulness to the biological community. The generation of suboptimal data will only slow our progress and damage the credibility of proteomics. Fortunately, advancement in bioinformatics, mass spectrometry, protein tagging, and affinity purification techniques are getting us ever closer to tackling these issues. ACKNOWLEDGMENT Fred Elisma, Houjiang Zhou, Ruijun Tian, and Hu Zhou contributed equally to this review. Mohamed Abu-Farha completed his Honors B.S. degree in biochemistry and biotechnology at Carleton University. He also obtained his M.S. from the Biology Department at Carleton University. Currently, he is a Ph.D. candidate under Professor Daniel Figeys’s supervision at the University of Ottawa in the Department of Biochemistry, Microbiology and Immunology. He was recognized as an NSERC scholar during his Ph.D. studies. Currently, he is working on studying chromatin modifying enzymes using proteomics and molecular biology techniques. Fred Elisma completed a B.S. degree in biochemistry, M.S. degree in biology, and a Diploˆme d′E´tudes Supe´rieures Spećialiseés degree in ` Montreál. He is currently bioinformatics at the Universite´ du Que´bec A a bioinformatician for Professor Daniel Figeys at the Ottawa Institute of Systems Biology, University of Ottawa. Houjiang Zhou completed his B.S. degree in Chemistry at Shannxi Normal University, Xi’an, China. He also obtained his M.S. degrees in analytical chemistry from Southwest University, Chongqing, China. He is currently a Ph.D. candidate at the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China under the supervision of Prof. Hanfa Zou. Houjiang Zhou is presently involved in a collaborative research initiative for developing new methods to study the dynamic change of phosphoproteomics 4596


in subproteome levels under the guidance of Prof. Daniel Figeys at the Ottawa Institute of Systems Biology, University of Ottawa.

Ruijun Tian completed his B.S. degree in chemistry at Inner Mongolia University, China in 2002. He obtained his Ph.D. degree in chemistry from the National Chromatographic R&A Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences. During his Ph.D. studies, he got the President Award of Chinese Academy of Sciences. He is currently a postdoctoral fellow working for Dr. Daniel Figeys at the Ottawa Institute of Systems Biology, University of Ottawa. His current research interests are quantitative proteomic study of human embryonic stem cells and combined proteomic and metabonomic study of protein interactome. Hu Zhou obtained his B.S. degree in biology at Nankai University, China in 2001. He received his Ph.D. degree in biochemistry and molecular biology from Shanghai Institutes for Biological Sciences for method developments in liquid chromatography and mass spectrometry in the summer of 2007. He is presently working as a postdoctoral fellow for Professor Daniel Figeys at the Ottawa Institute of Systems Biology, University of Ottawa, and is focused on technology developments of proteomics and lipidomics. Mehmet Selim Asmer is currently completing his Honors B.S. in Biomedical Science at the University of Ottawa. During his undergraduate studies, he has been awarded several distinctions including the Dean’s list awards, Faculty awards of excellence, NSERC OGI Research Fellowship. As an Honor’s student at the Ottawa Institute of Systems Biology, University of Ottawa, he is using mass spectrometry to study the role of the chromatin modifying enzymes. Daniel Figeys is a professor in the Department of Biochemistry, the Director of the Ottawa Institute of Systems Biology, and a Tier-1 Canada Research Chair in proteomics and systems biology. Daniel obtained a B.S. and a M.S. in chemistry from the Universite´ de Montreál. He obtained a Ph.D. in chemistry from the University of Alberta and did his postdoctoral studies at the University of Washington. Prior to his current position, Daniel was Senior VP of Systems Biology with MDS-Proteomics. From 1998 to 2000, he was a Research Officer at the NRC-Canada. Daniel’s research involves developing proteomics technology and their applications in systems biology.

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