Proteomics: From Technology Developments to Biological Applications

Proteomics: From Technology Developments toBiological 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 ofBiochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada

Review Contents

Quantitative Proteomics 4585Identification of Protein-Protein Interactions 4586Quantitative Protein-Protein Interaction 4587Post-Translational Modifications 4588

Phosphorylation 4588Glycosylation 4589Lipidation 4590Ubiquitination and SUMOylation 4590Acetylation and Methylation 4591

Chemical Proteomics 4591Probe Structure Improvement 4591Applications of Chemical Proteomics 4592Analytical Techniques 4593

Electrophoresis 4593Protein Chip 4593Liquid Chromatography and Mass Spectrometry 4593

Bioinformatics 4594Protein-Protein Interactions 4594

Quantitative Software Development 4594Database Resources 4595Management System 4595Conclusion 4596Literature Cited 4596

Since our 2005/2006 review, the field of proteomics hasremained very dynamic with new technologies, applications, andchallenges. The mapping of protein-ligand interactions hasremained a solid pillar of proteomics. Certain organisms’ inter-actomes, such as that of Saccharomyces cerevisiae, have almostreached saturation; while others, like the human interactome, areextensively being studied. The technology for quantitative pro-teomics has also advanced rapidly and has shown tremendouspotential for studying the dynamic nature of biological processes.Other exciting news is also coming from the identification of post-translational modifications (PTMs) and the development of noveltechnologies. Also, some of the more taboo subjects of proteomicsare being addressed. For example, the lack of statistical analysesand the reproducibility of proteomic data sets have plaguedproteomics in the pass. Fortunately, extensive research hasfocused on these issues and has greatly benefited the field ofproteomics. In this review, we will attempt to cover majordevelopments in proteomics in the past 2 years. We will highlightmajor success stories in the field, while outlining the challengesthat need to be overcome as it moves into the future.

QUANTITATIVE PROTEOMICSOne 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 gelto gel free methodologies, for example, the biological relevanceof generating protein lists was not always obvious. In reality, theRosetta Stone for understanding biological systems is finding thebiomolecules that are altered in quantity and/or quality (PTM,localization. . .). Therefore, proteomic quantification became an-other driving force in proteomics.

Isotopic labeling coupled to mass spectrometry (MS) tech-niques is the dominant approach in quantitative proteomics (1).Isotopic labeling uses nonradioactive and nearly chemicallyequivalent isotopes that have a mass difference which can beseparated by MS or MS/MS. This mass difference can be usedas a marker to find related peptides while the intensity/area ofthe peaks is used for relative quantification (2). Proteins andpeptides can be labeled with isotopes in vitro or in vivo (tissueculture, small organisms, and most recently small mammals).

The most common example of in vivo labeling is stable isotopelabeling by amino acids in cell cultures (SILAC) (3). It involveslabeling two cell populations with either “light” (natural) aminoacids 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 extensivelyadopted and has proven to be very useful for quantitativeproteomics (4). Furthermore, SILAC has been used to quantifypost-translational modifications (PTMs) such as phosphoryla-tion (5), acetylation (6), and methylation (7). A study lookingat these modifications in histones was done by Bonenfant etal. (8). They used extracted histones digested with a combina-tion of different proteases to study changes in their PTMsduring the cell cycle. By growing HeLa cells and arresting themat different stages, they demonstrated the dynamic nature ofhistone phosphorylation, acetylation, and methylation duringthe cell cycle (8).

The use of isotopic arginine poses a challenge because of theconversion of arginine to proline in eukaryotes (9). This processresults in the conversion of 13C6-arginine and 13C6, 15N4-arginineto 13C5-proline and 13C5, 15N1-proline, respectively, whichcompromises the accuracy of quantification (9). Many solutionshave been proposed to solve this problem such as using a lowerarginine concentration (10) and correcting for arginine conver-sion manually or by mathematical equations (11). Anotherapproach attempts to solve the problem of arginine conversion

* To whom correspondence should be addressed. Daniel Figeys, phone: 613-562-5800ext 8674. Fax: 613-562-5655. E-mail: [email protected].

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by providing an internal correction factor through the produc-tion of heavy proline converted from arginine in both light andheavy media (9). A more direct solution to eliminate theproblem can be achieved by adding L-proline directly intothe SILAC media (12). This idea is based on the fact that theconversion of arginine to proline is a result of the media beingdepleted of proline. Therefore, the addition of proline to themedia will reduce this back conversion. Maintaining a highenough concentration of proline will maintain the cellularhomeostasis rendering de novo synthesis of proline unfavorable(12).

Another issue is that although SILAC can be used in cellcultures or microorganisms, it cannot be used in tissues andorgans. The recent development of a SILAC mouse offers a partialsolution to this issue (13). Kruger et al. reported a completeisotopic labeling of mouse F2 generation using a mouse diet thatcontains 13C-lysine. The special diet is made by mixing either12C-lysine or 13C-lysine with lysine-free mouse diet (13). Theauthors demonstrated this method by comparing proteomesof different knockouts, allowing them to determine proteinfunction at a systems level. They studied the integrin pathwayusing �1integrin, �-Parvin, and Kindlin-3-deficient mice. Bycomparing proteomes of deficient mice with ones from the wildtype, they were able to demonstrate an important role ofKindlin-3 in the assembly of proteins in the red blood cellmembrane (13). Despite the excitement surrounding thisdevelopment, SILAC labeling of mouse tissue is an expensivetechnique. The price of the isotopic mouse diet is beyond whatmost laboratories can afford. Although it has been suggestedthat 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 morewidely used in tissues and small organisms, it will remain off-limits for use in humans and large primates.

Other examples of isotope labeling techniques include chemi-cal labeling using isotopic tags and proteolytic 18O labeling. Thesetechniques postmetabolically label proteins or peptides eitherchemically or by an enzymatic reaction. Proteolytic 18O involvesthe labeling of digested peptides by incorporating two 18O onthe carboxy terminus of each peptide (15). Comparisonbetween two different samples is achieved by digesting themseparately in the presence of heavy H2

18O or light H216O. The

simple nature of this technique makes it extremely attractive.Nonetheless, it still suffers from major setbacks such as back-exchange, lack of automated quantitative software, and thesmall 4 Da mass difference between the two states (1).

Chemical labeling techniques using isotopic tags is a morewidespread quantitative approach. These techniques are moreversatile because they allow many tags to be used. They are moreattractive to scientists because isotopic tags can be selectivelyadded. Most popular examples of these techniques are the ICATand iTRAQ. Since our last review, new modifications have beenadded to these techniques to answer different biological questionsand to improve previous methods. We will briefly touch on themajor changes and how they have been used in the field ofquantitative proteomics.

ICAT (isotope-coded affinity tag) labels cystine residuesthrough a sulfohydryl-reactive iodoacetate group (16). The other

end of the molecule contains a biotin group for affinity purification,while the isotope-coded region lies between the biotin and theiodoacetate group (16). In a recent study, Chen et al. identified116 differentially expressed proteins in chronic pancreatitis usingICAT. Their data shows a 40% overlap between the identifiedproteins and those differentially expressed in pancreatic cancer(17). To identify the effect of treating p53 (K317R) knock-in micethymocytes with ionizing radiation relative to the wild type, Jenkinset al. used ICAT. They identified 46 proteins whose expressionvaried in the p53 (K317R) knock-in relative to the wild type uponionizing radiation (18). A modified technique, termed cleavableICAT (cICAT), incorporates an acid-cleavable site that allows forthe removal of the ICAT reagent’s biotin tag prior to analysis byMS (19). Tan et al. used a combination of cICAT and iTRAQ tosuccessfully show differential protein expression in colorectalcancer cells upon butyrate treatment (20). Another modificationthat was implemented is the use of oxidation ICAT (OxiICAT).Developed by Leichert, et al., OxiICAT can be used to determinethe oxidation state of proteins (21). ICAT was also modified byHagglund 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 hasthe ability to label all N-terminal amines and lysine residues in apeptide mixture (23). One of its great advantages is the ability tolabel four or even eight (24) different cellular states. However,this can become a disadvantage, as it leads to increasing complex-ity of the analysis. ITRAQ has been used to study various diseasessuch as breast cancer (25, 26), hepatocellular carcinoma (27),type 2 diabetes (28), and Alzheimer’s disease (29).

The introduction of these different quantitative proteomicsmethods has tremendously altered the proteomics landscape.Further development of new methods, combined with advance-ments in analysis software and new MS instruments, will yieldnew discoveries. As we will see in the next section, combiningquantitativeproteomicswithothermethods,suchasprotein-proteininteraction techniques, promises to expand their scope of use andhelp answer different biological questions.

IDENTIFICATION OF PROTEIN-PROTEININTERACTIONS

Mapping of protein interactions is essential in proteomics. Bybuilding interaction maps, we gain a better understanding of howindividual proteins function through their participation in differentprotein complexes under different conditions (30). In our pastreview, we focused on large scale protein interaction studies. Inthe past 2 years, a more targeted approach has been adopted,shifting the focus to more in-depth studies. Since the S. cerevisiaeis nearly completed, the focus has shifted from simpler to morecomplex organisms and toward human protein interaction map-ping (31). Our group has published one of the largest humanprotein-protein interaction mapping studies (32). In this study,338 human proteins were FLAG-tagged and expressed in humanembryonic kidney cells. With the use of anti-FLAG agarose beads,baits were immunopurified and resolved on SDS-PAGE. Gel bandswerethenanalyzedusingESI-LC-MS/MStoidentifyprotein-proteininteractions in HEK293 cells. MS analyses resulted in theidentification of 24 540 potential protein interactions. This protein

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list was filtered to increase confidence, and it generated 6 463interactions between 2 235 unique proteins (32).

Wang et al. identified the interaction map of Ras-MAPK/PI3Ksignaling pathways (33). Using a yeast two hybrid (Y2H) system,they used 44 different baits selected from protein families suchas Ras family members of small G proteins, MAP kinases, PI3Ksubunits, protein tyrosine phosphatases, adapter proteins, ortransducers as well as other molecules that interact with membersof Ras-MAPK/PI3K signaling pathways. After removing nonspe-cific protein interactors, they were left with 200 protein interactors.Only a few of those interactors were verified by coimmunopuri-fication and colocalization assays (33).

A proteome-wide protein interaction map of the food-bornepathogen Campylobacter jejuni NCTC11168 was generated byParrish et al. (34). They used Y2H to map protein interactions of80% of the predicted C. jejuni ORFs. This map offers an invaluabletool to annotate C. jejuni proteins, especially since about 50% ofthose proteins are currently unknown or poorly characterized. Theauthors employed different strategies to ensure a low false positiverate. First, they repeated their experiments twice, and then theycalculated a probability score that measured the confidence ofeach interaction according to its biological relevance, resultingin a list of 2 884 high confidence interactions (34). Despite theseattempts, a number of false positive interactions were reported.

In an attempt to understand bacterial motility, Rajagopala etal. surveyed the whole genome of two hybrid arrays of Treponemapallidum and C. jejuni against known flagellar apparatus proteinsto map their interactions (35). Motility genes were selected byscreening 3 985 gene deletion strains of Escherichia coli. Dataobtained from this screen was integrated with other data obtainedfrom similar screens in other bacteria (35). As a result, 176interactions involving 110 proteins in T. pallidum were identifiedalong with another 140 interactions involving 133 proteins fromC. jejuni (35). They were also able to identify 23 uncharacterizedproteins as components of bacterial motility. Taken together, theirdata shows that although this pathway is well conserved inbacteria, many components of this pathway have gone throughevolutionary adaptation.

As an example of protein interaction studies in plants, Popescuet al. has generated a high density protein microarray to studythe binding of calmodulin (CaM) and calmodulin-like (CML)proteins in Arabidopsis thaliana (36). The protein microarray wasconstructed using 1 133 proteins and probed with three CaM andfour CMLs. It revealed about 173 novel in vitro interactions. Theirdata 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 usingprotein microarray to study the protein interactions in vitro andthe potential to expand this technique to larger sets of proteins.

The S. cerevisiae interactome shows how completed andrefined protein interaction data can further our understanding ofunderlying biological processes. Although the S. cerevisiae inter-actome has been extensively studied, new studies are directed atrefining the interaction data. Two such studies were recentlypublished in the journal, Science. Using Y2H, Yu et al. were ableto identify 1 809 binary interactions, most of which were novel(37). The other study, by Tarassov et al., used protein-fragmentcomplementation assays (PCA) to identify 2 770 binary interac-

tions, most of which were previously unknown (38). Rigorousquality assessments were performed in both studies. This allowedthe groups to confidently claim that only a small percentage ofthe identified proteins could be false positives.

QUANTITATIVE PROTEIN-PROTEININTERACTION

Studying protein interactions by affinity purification coupledto MS (AP-MS) has the great advantage of identifying proteincomplexes. Identification of protein complexes rather than binaryinteractions allows for the placement of proteins within theirbiologically relevant settings. AP-MS is directly affected byadvances in mass spectrometry, sample preparation, and bioin-formatics (39). However, as MS development allows for highersensitivity in complex identification, the number of contaminantproteins identified will also inevitably increase. This will lead to alarge false positive rate unless more stringent conditions are usedto reduce contaminants. Yet, as stringency increases, so does theloss of the weak but biologically relevant interactors. To addressthis issue of high false positive identification rate, quantitativeproteomics has been used. In these studies, mild conditions wereused to preserve weak interactions (39).

Many studies use quantitative proteomics to study proteininteractions. Examples of quantitative techniques in proteininteractions include the use of ICAT (40-42), ITRAQ (43-45),and SILAC (46-50). One example of using SILAC to identify weakprotein-protein interactions was published by Trinkle-Mulcahyet al. (51). They used green fluorescent protein (GFP) as anaffinity purification tag. This tag uses the newly derived GFPbinder 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 differentmatrixes (Sepharose, agarose, and magnetic beads), Trinkle-Mulcahy et al. identified the matrixes’ most common contaminantsin SILAC labeled mammalian cells. Identification of these proteinsfrom either whole cell, cytoplasmic, or nuclear extracts composeswhat is called a “bead proteome” (51). Such studies will helpestablish lists of common nonspecific contaminants that arecommonly 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 nonspecificcontrol identified most of SMN’s known interactors. However,challenges still remain. For example, this immunopurificationmethod identified a number of validated SMN binding proteinswith a ratio similar to those of nonspecific interactors. Some ofthese specific interactors even had a ratio of less than 1, like SmD1and SmD2, which are known interactors of SMN (53-55).

Another example of quantitative protein-protein interactioncan be seen in the new method developed by Wepf et al. (56). Inthis method, they integrate a small peptide as part of the affinitytag in the construct which, upon digestion, can act as a referencepeptide. Similar to AQUA (57), absolute quantification of thereference peptide can be performed. The peptide used is calledSH-quant (AADITSLYK). After affinity purification, a certainconcentration of the heavy form of the peptide is added. Thispeptide, SH-quant*, contains a heavy isotope-labeled lysine form.The amount of bait in different affinity purifications is calculatedas a ratio of the precursor-ion signals of SH-quant* and SH-quant,respectively. Another form of the SH-quant peptide “SH-quant**”,

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which contains heavy isotope-labeled lysine and leucine, was usedas a correction factor for sample loss during processing (56). TheSH-quant** peptide was added just before LC-MS/MS analysis,and the correction factor was calculated as a ratio between theprecursor ion signals for SH-quant* and SH-quant**. This methodwill offer a great advantage in following the changes in proteincomplexes under changing environments such as differentiationor a drug treatment (56).

POST-TRANSLATIONAL MODIFICATIONSMost proteins are enzymatically modified after their translation

by a number of chemical groups. These PTMs such as phospho-rylation, glycosylation, and acetylation are key players in modulat-ing proteins function(s). The study of PTMs has also been adriving force of proteomics in the past decade. Unfortunately, ourability to predict most PTMs is limited at best. Furthermore,although the databases of PTMs are growing in numbers, thecoverage is rather low. To date, most PTMs need to be discoveredde novo using techniques such as MS or a series of conventionalbiochemical approaches. Improving and developing new methodsto detect PTMs has been the focus of many research groups inthe past decade. In this part of the review, we will discuss newmethods developed to study PTMs.

Phosphorylation. Protein phosphorylation has been thecentral hinge of signal transduction and regulation in biologicalprocesses. We have seen a continuous drive for the developmentof phosphoproteomics technologies throughout the past 2 years.Phosphoproteomics has taken a great leap in concomitance withtechnological advancements in MS instrumentation. It has ben-efited from the improvement in mass accuracy, better resolutionand sensitivity, methods for phosphopeptide specific enrichment,and associated bioinformatics. MS-based phosphoproteomics isshowing increased capability of deciphering complex signalingpathways in various biological organisms under different biologicalconditions.

Bodenmiller et al. (58) systemically assessed the ability ofthree common phosphopeptide isolation methods including phos-phoramidate chemistry, immobilized metal affinity chromatogra-phy (IMAC), and titanium dioxide (TiO2). They compare thereproducibility, specificity, and the ability of the techniques toisolate phosphopeptides from complex mixtures. Their findingsshow that different methods have a preference to different typesof phosphopeptides. Nonetheless, partial overlapping segmentsof the phosphoproteome have been found in the three methods.Over the last 2 years, continuous efforts have been made toimprove the specificity and sensitivity of phosphopeptideenrichment. Thingholm et al. (59) presented SIMAC (sequen-tial elution from IMAC) which combined the enrichmentselectivity of IMAC and TiO2 to efficiently separate monophos-phopeptides and multiply phosphopeptides under differentelution conditions. The SIMAC approach greatly improved thedetection of multiple phosphopeptides by LC-MS/MS. Mc-Nulty and Annan (60) exploited the strong hydrophilicity ofthe phosphate group to prefractionate phosphopeptides basedon their retention under hydrophilic interaction chromatogra-phy (HILIC) and subsequent phosphopeptide enrichment byIMAC. HILIC separation prior to IMAC improved the phos-phopeptide enrichment to more than 99% without additionalderivatization or chemical modification. It also improved the

phosphopeptide recovery in comparison with IMAC alone.HILIC with IMAC also demonstrated a more uniform distribu-tion than SCX with IMAC. Blair et al. (61) developed a newmethod for high selective enrichment of phosphopeptides bycalcium phosphate precipitation of phosphopeptides combinedwith established IMAC enrichment. The application of thismethod to a complex peptide sample derived from mice embryoresulted in more than 90% of the phosphopeptides identifiedin the enriched sample from LC-MS/MS. Ficarro et al. (62)exemplified niobium pentoxide (Nb2O5) for efficiently enrichingand recovering phosphopeptides. In another method, Zr4+ andTi4+-IMAC, which relies on the strong interaction between themetal (Zr4+ or Ti4+) phosphonate and the phosphate group onphosphopeptides, was used to enrich phosphopeptides fromsimple sample mixtures and real biological samples (63-65).Both of Zr4+ and Ti4+-IMAC demonstrated superior selectivityand efficiency of phosphopeptide enrichment compared totraditional IMAC with nitrilotriacetic acid (NTA) or iminodi-acetic acid (IDA) such as chelating ligands and metal oxides(TiO2 and ZrO2). Sugiyama et al. (66) developed novel methodsfor phosphopeptide enrichment using lactic acid-modifiedtitania and �-hydroxypropanoic acid-modified zirconia metaloxide chromatography.

The commercial availability of hybrid MS machines such asLTQ-FT and LTQ-orbitrap and alternative peptide dissociationtechniques have improved the analysis of phosphoproteomics overthe past 2 years. For example, collision induced dissociation (CID)for doubly charged peptides with an automatic alternating modehas proved to produce better results in profiling phosphorylationsites from complex biological samples (67-69). Recently, Sweetet al. (70) employed online electron capture dissociation (ECD)for the large-scale identification and localization of phosphorylationsites and compared it with CID. They found that the combinationof ECD and CID analyses results in high confident identificationof phosphopeptides and the localization of phosphorylation sites.Olsen et al. (71) demonstrated that peptides can be fragmentedin an LTQ-orbitrap MS with high resolution and full-mass-rangeMS/MS by higher-energy C-trap dissociation (HCD). This ap-proach proved more effective for identifying tyrosine phosphory-lated peptides by detecting the phosphotyrosine-specific immo-nium ion at m/z 216.0426. The additional octopole collision cellcan also facilitate de novo sequencing.

As technology has continued to improve, interesting applica-tions of large-scale mapping of phosphoproteome, assessmentsof phosphorylation-based signaling networks, and the decipheringof the kinome on a systemwide scale have been presented.Bodenmiller et al. (58) presented Phospep, a database containingmore than 10 000 unique high-confidence phosphorylation sites,mapping nearly 3 500 gene models and 4 600 distinct phosphop-roteins of the Drosophila melanogaster Kc167 cell line. Villen etal. (72) reported the identification of 5 635 nonredundant phos-phorylation sites from 2 328 proteins from mouse liver. Swaneyet al. (73) performed a large-scale analysis of phosphorylation inhuman ES cells using both CID and electron transfer dissociation(ETD) MS/MS dissociation methods. This study resulted in theidentification of 11 995 unique phosphopeptides, which correspondto 10 844 nonredundant phosphorylation sites. They also reportedobserving 16 previously unreported phosphorylation motifs found


only in the ETD-sequenced data set. Kruger et al. (74) studiedthe tyrosine-phosphoproteome of the insulin signaling pathwayusing an LTQ-FT in combination with phosphotyrosine immu-nopurification and SILAC in differentiated brown adipocytes. Theydiscovered seven new insulin-induced effectors including SDR,PKC-binding protein, LRP-6, and PISP/PDZK11 (a potentialcalcium ATPase binding protein), from 40 identified receptorsassociated with different branches of the insulin pathway.

A recent systematic investigation of a potential gas-phasephosphate group rearrangement reaction was conducted byPalumbo et al. They found that this reaction occurs with typicalion trap CID-MS/MS due to the lengthy time scale (millisecond)involved for its activation in ion trap MS and the accessibility ofunmodified hydroxyl-containing amino acid (75). Furthermore,the conventional CID-MS3 initiated from the neutral loss ofH3PO4 provides ambiguous phosphorylation site assignments.Clearly this is a serious issue because all the MS searchengines would still return high confidence scores and low false-positive rates due to gas phase chemistry issues. Furthermore,the majority of phosphorylation sites reported by high-throughput experiments have not been validated. Therefore,it is important to be aware of these issues when relying onpreviously identified phosphorylation sites. It might well be thatalternative fragmentation methods (ETD or ECD), comparisonswith the standard synthesized phosphopeptides sequences,searches for the presence of a sequence of a known kinasemotif, quantitative phosphoproteomics, and focused experi-mental designs will help in accurately identifying phosphory-lation sites.

In the last 2 years, quantitative MS-based phosphoproteomicswas applied to study the kinome and kinase inhibitors. Wissinget al. reported the immobilization of kinase inhibitors for theselective affinity capture of protein kinases (76). They identified140 different members of protein kinases and more than 200phosphorylation sites after phosphopeptide enrichment andLC-MS/MS. Moreover, they studied the quantitative changes of219 protein kinases between S and M phase-arrested humancancer cells using kinase enrichment with SILAC based quantita-tive MS. Some of these phosphorylations include many proteinkinases that are implicated in mitotic progression (77). Alterna-tively, researchers at Cellzome applied the Kinobeads withmultiple immobilized broad selectivity kinase inhibitors to enrichfor protein kinases. They treated cells and cell lysate with specifickinase inhibitors at varying concentrations (78). Potential targetsof these specific kinase inhibitors and their respective Kd valueswere obtained by MS in combination with iTRAQ labelingapproach. They profiled signaling pathways downstream oftarget kinases induced by specific inhibitors. Clearly, quantita-tive phosphoproteomics in combination with enrichment tech-niques is likely to provide invaluable biological information.

Glycosylation. In-depth knowledge of protein glycosylationat the proteomics level, (such as structural information aboutglycan microheterogeneity), peptide sequence, and functionalanalyses of the glycoproteome all play important roles in under-standing biological processes and their clinical applications.Continuous technology developments in MS-based glycoproteom-ics and re-engineered glycoproteins such as metabolic labelinghave 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 agargantuan task. Here, we take a look at some of these emergingdevelopments and their applications in glycoproteomics.

Sun et al. (79) reported an enhanced method for hydrazide-based glycopeptides enrichment. This approach has the advantageof reducing sample complexity, sample loss, and improving theMS detection sensitivity and accuracy for low abundance glyco-sylated proteins. It also demonstrated the versatility of O-linkedglycoprotein analysis in combination with O-glycosidase or �-e-limination. A systematic optimization of all experimental proce-dures and data analyses has been performed by Zhang et al. (80).After repeating the experiment with an optimized protocol, morethan 85% of potential glycosylation sites were identified from theheavily glycosylated human immunodeficiency virus (HIV) enve-lope proteins JR-FL, gp140, and ∆CF. Kubota et al. (81) developeda rapid and sensitive method to analyze glycopeptides using lectinand cellulose affinity chromatography in combination with matrix-assisted laser desorption ionization-time-of-flight mass spectrom-etry (MALDI-TOF MS) and MALDI-quadrupole ion trap (QIT)-TOF MS. Recently, Zhou et al. (82) developed a glycoproteomicreactor in which affinity enriched glycoproteins are digested intopeptides. The highest efficiency in identification of glycopeptideswas demonstrated by analyzing microliters of human plasmasample.

Alternative approaches based on novel chemical tagging havealso been proposed for the comprehensive profiling of glycopro-teomics and for understanding the biological functions of glyco-sylation. Hanson et al. (83) attempted to introduce a newglycoproteomic strategy for saccharide-selective glycoproteinidentification. The chemical-tagged glycans are metabolicallyincorporated into proteins during cell culture. The re-engineeredglycoproteins are separated by affinity purification and thenidentified by LC-MS/MS. In a milder method reported recently,periodate oxidation was exploited to generate an affinity aldehydetag at sialic acid-containing glycans labeled glycoproteins. The tagis then easily enriched and separated by aniline-catalyzed oximeligation reaction (84). These technologies are also expected tolabel, enrich, isolate, and identify sialoglycoconjugates. However,one serious deficiency of the chemical tagging approach is that itcannot be applied to intrinsic proteomes (e.g., human plasma)because the affinity tag present on the modified glycan is notavailable in natural glycan. In the pursuit of improving glycopep-tides detection by MS, Catalina et al. (85) investigated the ETDMS spectra of multiply protonated N-glycopeptides using horse-radish peroxidise. Wu et al. (86) and Alley et al. (87) also exploredthe use of ETD with CID for characterizing glycopeptides. Theyboth concluded that the combination of ETD and CID is morepowerful for the elucidation of the glycan structure and theaccurate localization of glycosylation sites.

The application of glycoproteomics is challenged by the factthat, to date, glycoproteomics has been predominantly limited tofinding the sites of attachment of glycosylation. We have seen agradual increase of specific biological questions, especially for theanalysis of clinical samples. Qiu et al. (88) developed a methodfor glycoproteomic identification of potential plasma markersaimed at detecting colorectal cancer. The potential markers thatseem to distinguish colorectal cancer from adenoma and normal


cells include elevated sialylation and fucosylation in complementC3, histidine-rich glycoprotein, and kininogen-1. Sturiale et al. (89)presented multiplexed glycoproteomic analyses of congenitaldisorders of glycosylation (CGD) by yolk immunoglobulinsimmunoaffinity separation and MALDI-TOF MS analysis. Theyalso found that CGD-Ia patients showed a typical profile of under-glycosylation where the fully glycosylated glycoforms are alwaysmost abundant in plasma with lesser amounts of partially andunglycosylated isoforms. Hulsmeier et al. (90) quantified N-glycosylation occupancy in healthy control samples and in CGDsamples based on multiple reaction monitoring LC-MS/MS. Theyobserved a reduction in N-glycosylaton site occupancy thatcorrelated with the severity of the disease. Vercoutter-Edouart etal. (91) applied a glycoproteomic approach based on Con A lectinaffinity chromatography, MS analysis by MALDI-MS, and GC/MS analyses of permethylated derivatives to investigate HT-29epithelial colon cancer cells. They found that, in addition to themodifications of sialic content of individually identified N-glyco-proteins involved in cellular adhesion and permeability of intestinalepithelial cells, the major changes are the expression of GlcNAc-ended N-glycans that occur in enterocyte-type cells. It is fair tosay that although many technical hurdles have been addressed,time will only tell if the potential biomarkers discovered byglycoproteomics are specific to the disease of interest or if theyare reflective of a systemic response that is general to manydiseases.

Lipidation. Protein lipidation is a crucial protein modificationthat involves covalent attachment of hydrophobic carbon skeletonsof the various lipid classes (fatty acids, sterols, glycero-, phospho-and glycolipids) (92). This type of modification affects 2-4% ofall proteins in a given proteome. A number of methods have beendeveloped to study protein lipidation. The high molecular weightnature of many lipid groups and few functionalities act as handlesfor antibody-based recognition, chemoselective, or enzymatictagging of lipidation (93). In this section we will discuss newtechnologies that emerged in the past 2 years that have providedsignificant advantages for studying protein lipidations. Roth et al.(94) described a new proteomic method that purified andidentified palmitoylated proteins with orthogonal tags to character-ize the palmitoyl proteome of S. cerevisiae using LC-MS/MS.Using this approach, they identified 12 of the 15 known palmitoylproteins plus 35 new palmitoylated proteins including manysoluble (N-ethylmaleimide sensitive factor (NSF) attachmentprotein receptors (SNARE) proteins and amino acid permeases.They also identified many other proteins that are involved incellular signaling and membrane trafficking. In a different method,a modified 17-octadecynoic acid with an alkyne affinity tag wasused to metabolically label palmitoylated proteins, which were thenenriched and identified by LC-MS/MS. As a result, a total of125 predicted palmitoylated proteins, including G proteins, recep-tors, and a family of uncharacterized hydrolases, whose plasmamembrane localization depends on palmitoylation, were identified(95). Sakurai et al. (96) demonstrated that in vitro translation ofcDNA coding for N-myristoylated protein in the presence of 3H-myristic acid followed by SDS-PAGE and fluorography is arapid detection method for protein N-myristoylation. Thisseems to be a relatively simple and effective strategy to detectpost-translational protein N-myristoylation in combination with

LC-MS/MS. Nguyen et al. (97) have profiled the eukaryoticprenylome by structure-guiding of engineered protein prenyl-transferases and their universal synthetic substrate, biotin-geranylpyrophosphate. The engineered protein prenyltrans-ferases can faithfully deliver biotin-geranyl to their cognatesubstrates lysates, thus allowing identification and quantifica-tion of unmodified prenylations. This approach allows femto-molar quantities of prenylation substrates to be detected andcan be used for relative quantification of protein prenylationand their modulation by therapeutic agents. One limitation ofthe metabolic labeling approaches is that they are not applicablein animal models or human samples. However, we expect thatthese tools and other tools that are developed for lipidomicstudies will greatly enhance our understanding of the role ofprotein lipidation and lipids in biological processes.

Ubiquitination and SUMOylation. Ubiquitin (Ub) and smallUb-like modifier (SUMO) proteins covalently attach to the sidechain of lysine residues via an isopeptide bond. They regulatemany essential cellular processes including protein degradation,cell cycle, transcription, DNA repair, and membrane trafficking.Disrupted Ub signaling may have broad consequences for neu-ronal function and survival (98). Generally, most literature in thisfield emphasizes the importance of the biological pathways thatare induced by ubiquintation and sumoylation rather than thedevelopment of methodology for analyzing protein ubiquitinationor sumoylation in the past 2 years. Denis et al. (99) improved anMS-based strategy of ubiquitination analysis by identifying twosignature peptides containing a GG-tag (114.1 Da) and an LRGG-tag (383.2 Da) on internal lysine residues, as well as a GG-tagfound on the C-terminus of ubiquitinated peptides. Vasilescu etal. (100) applied the proteomic reactor to facilitate affinitypurification and identification of ubiquitinated proteins by LC-MS/MS. Xu et al. (101) presented a middle-down MS strategy tocharacterize the length and linkage of polyUb chain structures.Recently, Blomster et al. (102) developed a novel method basedon SUMO protease treatment to remove the desumolyation ofsubstrates that reduced the complexity of SUMO substrates onSDS-PAGE separation before LC-MS/MS analysis. Matic et al.(103) developed an alternative MS strategy with the assistanceof high resolution MS to localize sumoylation sites. Moreover,the approach demonstrated the prospects for analyzing highlycomplex biological samples. Maor et al. (104) performed largescale affinity purification and identification of ubiquitinatedproteins from Arabidopsis thaliana with multidimensional proteinidentification technology. The application of this approach resultedin 382 SUMO-2 targets, of which more than half of the consensussites are unknown. Mayor et al. (105) quantitatively profiledubiquitinated proteins by perturbing the Rpn 10 receptor usingreference cultures of S. cerevisiae with SILAC. Meierhofer et al.(106) performed quantitative analyses of global ubiquitination bytwo steps of Ni2+-NTA and hexahistidine-biotin tag affinitypurification and LC-MS/MS analysis for untreated cells andcells treated with the proteasome inhibitor MG132. Bennett etal. (107) exploited an MS-based method to quantify globalchanges to the ubiquitination system in Huntington’s disease.

Although MS has become a powerful tool for mapping the Uband SUMO, it is very important to highlight the potentialmisidentification 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 thelysine residues. This mass shift is isobaric with the diglycinelinkage for Ub and SUMO and therefore can lead to misidentifi-cation. To address this problem, the authors recommendedchanging the reagent from iodoacetamide to chloroacetamide orto other alkylating reagents that do not produce the artifact mimicsof ubiquintination in MS. Meanwhile, the previously reportedubiquitination sites based on diglycine linkage in the presence ofiodoacetamide should be re-evaluated. However, this does notapply to sites identified with LRGG linkages and diglycine linkageswhich were validated by other biochemical means.

Acetylation and Methylation. Acetylation is highly dynamicand has been linked to many cellular processes such as genesilencing, cell cycle progression, apoptosis, differentiation, andDNA replication (109-111). Methylation, in contrast, has beenconsidered a stable modification that regulates transcriptionalrepression and activation, transcriptional elongation, heterochro-matin formation, X-inactivation, and polycomb-mediated genesilencing (112). Although these modifications are very important,the MS-based analytical methods to characterize protein acetyla-tion and methylation are still limited. Furthermore, very few papersreport methodology development to analyze acetylation andmethylation by MS. Wu et al. (113) proposed a strategy todetermine the acetylation sites of proteins using MS to trace massdifferences resulting from the in vitro acetylation reactions withisotope-labeled and unlabeled acetyl groups. Methylation ofproteins on lysine or arginine is expected to increase the mass ofthe residue by a multiple of 14 Da depending on the number ofmethyl added. Therefore, a tempting strategy is to look for thismass shift in MS/MS spectra of peptides to identify proteinmethylation. Jung et al. (114) found that a large number ofpeptides can be modified on the lysine, arginine, histidine, andglutamic acid residues with a mass increase of 14 or 28 Da. Sothat seems problematic, even though the mechanism for this massincrease eluded their report. Amino acid substitution can alsocause mass increases that are multiples of 14 Da while acetylationhas a mass very close to trimethylation. Here again, there is astrong potential risk of misidentification of methylation. Therefore,one should consider strategies that are combined with massspectrometric identification using in vivo isotope labeling asdescribed by Ong et al. (115). Moreover, regardless of the strategyselected, other biochemical means should be used to validatemethylation sites.

A combination of metabolic labeling and top-down massspectrometry has also been used to study the regulation andfunction of methylation of histone H4 at lysine 20 (116). Top downmass spectrometric coupled with 2D-HPLC was used to character-ize different forms of Histone 4 from HeLa cell (116). The relativequantification of 42 H4 isoforms which were uniquely modifiedby methylation and acetylation was also performed. In anotherreport, an MS and genomewide analysis was used to verify theacetylation of Lysine 56 of Histone 3 in the core transcriptionalnetwork in human embryonic stem cells (117).

Methylations of proteins’ nonhistones were also reported. Inparticular, specific sites of co- and post-translational modificationof cytosolic ribosomal proteome in A. thaliana including acety-lation, 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 Hsp90by MALDI-TOF MS and MS/MS that are hyperacetylated oncethe histone deacetylase (HDAC) 6 and the pan-HDAC inhibitorare knocked-down in eukaryotic cells. Hyperacetylation is closelyrelated to the modulation of the intracellular and extracellularfunction of Hsp90. The identification of N-terminal protein me-thylation of the regulator of chromatin condensation 1 wasdemonstrated by high-resolution and accurate ECD-MS (120).Finally, Sprung et al. (121) reported the identification of in vivoaspartate and glutamate methylation in eukaryotic cells by nano-HPLC-MS/MS.

Despite the technological progress for studying protein PTMsusing MS, the number of biological applications remain verylimited, especially for the high-throughput experiments. Proteinphosphorylations have been the most studied modification withan increasing number of phosphorylation sites being mapped byhigh-throughput approaches. Although these results populatedatabases, the biological context for the experiment is oftenmissing. Perhaps more benefits will come from the coupling ofthese approaches with affinity purification methods to targetpathways and complexes.

CHEMICAL PROTEOMICSChemical proteomics attempts to identify and characterize the

interactions between small molecules and proteins. Typically, thisis done by labeling or immobilizing certain small molecules (e.g.,drug, biomolecule, and reactive chemical structure), which is usedto enrich a group of proteins. MS is then used to identify theseproteins. This approach greatly reduces the complexity of thesample, which facilitates the detection of lower abundanceproteins. With aid from MS-based quantitative proteomics, chemi-cal proteomics is becoming a powerful technique for drugdiscovery and monitoring protein functions (122, 123).

PROBE STRUCTURE IMPROVEMENTGenerally, 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 fromthe complex proteome. For enrichment, a binding group can beimmobilized directly onto various kinds of hydrophilic matrixes.This is a well-established approach for studying drug-proteininteraction. The biotin group is the most commonly used tag forenrichment due to its high affinity to avidin and its biocompat-ibility. Recently, Eden et al. applied fluorous affinity tags toselectively enrich metabolites and peptides (124). The advantageof the fluorous tag is its high affinity to fluorous environmentsrather than an organic or aqueous phase. Saxena et al. introduceda peptide containing FLAG epitope into a chemical probe, whichcan be recognized by antibodies with high selectivity (125).Fluorescence-based tags are commonly used in chemical probesfor detection (126). Chen and co-workers further explored theunique characteristics of this type of tag as a reporter ion forenrichment purposes and MS/MS (127). One disadvantage of thistagging approach is the bulky chemical structure which generatesextral 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


are the negligible chemical structure and the high-efficiency ofadding different reporter groups under mild biocompatible condi-tions. Finally, stable isotopes have also been introduced intovarious chemical probe structures for quantitative chemicalproteomics (130, 131).

The chemical properties of the linker can affect the resultsfrom chemical proteomics. For example, linkers that are too shortor too bulky can cause steric hindrance, while the hydrophilicand hydrogen binding properties of the linker can cause nonspe-cific interactions. A hydrophobic chain based on an alkyl unit isa common linker for chemical probe synthesis. Because of itshydrophilicity, the use of a polyethylene glycol (PEG) is ideal asa linker for protein enrichment. However, both alkyl and PEGunits exhibit unexpected folding if the length of the chain becomestoo long. Recently, Sato et al. developed a long, rigid polyprolinehelix as a linker for target protein enrichment (132). By introduc-tion of nine L-prolines, a stable left-handed helix linker with alength of 27 Å was obtained. To avoid nonspecific adsorption ofthe chemical probe with abundant proteins, Verhelst et al. reporteda mild chemically cleavable linker system based on diazobenzenederivatives (133). By simple incubation of the chemical probecarrying the cleavable linker with 100 mM Na2S2O4 for 30 min,the linker can be fully cleaved. Another type of cleavable linkerbased on a disulfide bond was also reported recently (134, 135).In this method, the target proteins can be eluted selectively byDTT, thus minimizing the nonspecific binding proteins. Tominimize the possible loss of target proteins during the enrichingand washing steps, a photoaffinity labeling group is more routinelyused in chemical probes to covalently bind the target proteinsunder mild UV irradiation (136-140). On the basis of thestructure-activity relationship study, Kawamura and co-workersfound that higher conformational flexibility, but not higherbinding-affinity, is more important for efficient photolabeling (141).

APPLICATIONS OF CHEMICAL PROTEOMICSTo 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 thefrontline chemotherapeutic agents that were recently introducedfor treating chronic myeloid leukemia. Rix et al. developed threechemical probes based on these drugs to study their potentialkinase targets. On the basis of their study, several kinases,including tryosine kinase DDR1, oxidoreductase NQO2, and alarge number of Tyr- and Ser/Thr-kinases, were identified asinteractors of the drugs (142). Another study published byHantschel et al. demonstrated that Tec kinases Btk and Tec arethe major binders to dasatinib-based affinity matrix (143). Furtherbiological validation demonstrated the potential immunosuppres-sive side-effects of this drug by the inhibition of Tec kinases.Immobilized ATP and competition assays have been used byDuncan et al. to characterize the inhibitor’s specificity andpotential protein targets of three kinds of CK2 inhibitors (144).

Chemical proteomics can be applied in profiling the noncova-lent interactome of different types of biomolecules. Using im-mobilized cAMP and an optimized protocol for purification andelution, Scholten et al. reported direct identification of lowabundance cAMP signaling proteins from mouse ventricular tissue(145). Mallikaratchy et al. developed an aptamer-based chemicalproteomic approach for exploring target proteins from live cell

membrane (146). A photoaffinity aptamer probe for targetingRamos cells was developed, and its target protein on the cellmembrane was identified as immunoglobin heavy mu chain.Kalisiak et al. reported the application of chemical proteomicapproach for the identification of proteins targeted by endogenousmetabolites found through untargeted metabolomics (147).Through identification and immobilization of a new endogenousmetabolite, N4-(N-acetylaminopropyl) spermidine, 11 potentialtarget proteins were identified by MS. By introduction of a biotingroup into peptide fragments from R-synuclein, McFarland et al.studied protein-protein interactions of the protein and the effectsof different phosphorylations (148).

In biological systems, various cellular functions are mediatedby the covalent interaction of small molecules and proteins suchas PTMs and active site-based regulation. It is possible to takeadvantage of covalent interaction properties for chemical pro-teomics. For example, activity-based probe (ABP), in which anenzyme family is covalently labeled at the active site, is a validchemical proteomic approach (149). Gillet et al. achieved in-celllabeling of serine hydrolases using a novel fluorophosphonatebased ABP and applied this ABP to profile serine proteaseinhibitors (150). By implementing a reporter-tagged fluorophos-phonate-based ABP, Li et al. reported the discovery of an inhibitorfor an unannotated serine hydrolase, R/�-hydrolase domain 6,from a library of inhibitors based on the carbamate reactive group(151). Barglow et al. used high-resolution crystallography andmolecular modeling to characterize the active site of Nit2 nitrilaseand its binding to dipeptide-chloroacetamide activity-based pro-teomics probes (152). Everley et al. developed a cleavable ABPtoward serine hydrolase and applied it in a quantitative chemicalproteomic study in combination with SILAC (134). The first ABPsfor steroid sulfatases were reported by Lu et al. who alsodemonstrated the potential of dot blot analysis for inhibitorscreening of steroid sulfatase (153). Hwang et al. reported thedevelopment of an ABP to specifically tag chloroacetyl coenzymeA dependent proteins (154). After developing two pairs ofbiotinylated, cleavable, isotope-coded ABPs toward endoglycosi-dases, Hekmat and co-workers applied them to study the relativeexpression/activity levels of endoglycanases (131).

PTMs are covalent modification of proteins with differentchemical structures. Recently, chemical proteomics has beenapplied 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 biotingroup for the enrichment and identification of the labeled proteins(155). Khidekel et al. reported the quantitative proteomic studyof O-GlcNAc glycosylation in the brain using a chemoenzymatictagging and isotopic labeling strategy (156). Using click chemistrytechnology, this laboratory developed an advanced strategy tofacilitate in-gel and in vivo labeling of O-GlcNAc glycosylation(129). Hanson et al. developed bio-orthogonally tagged alkynylsaccharides for saccharide-selective glycoprotein identification andglycan site mapping (83). Hang et al. developed a serial of ω-azido-fatty acids for visualizing protein fatty acylation, including N-myristoylation and S-acrylation (157). Utilizing 17-octadecynoicacid as a chemical probe, Martin et al. reported the in situ labeling,identification, and verification of protein palmitoylation on a globalscale (95). Nguyen et al. revisited the protein prenylation using a


synthetic substrate, called biotin-geranylpyrophosphate (97). Bymodifying the protein activity of both farnesyltransferase andprotein geranylgeranyltransferase type I based on a structure-guided protein engineering approach, the authors identified theprenylation substrates catalyzed by all of the three kinds ofprenyltransferases. On the basis of metabolic labeling, Dieterichet al. reported a chemical proteomic method to label and identifynewly synthesized proteomes by using the noncanonical aminoacid azidohomoalanine (158).

Clearly, the repertoire of binding groups that can be integratedinto chemical probes has rapidly increased, and the field ofchemical proteomics is promising to be very successful for thestudy of different aspects of biology such as target proteinprofiling, postmodification characterization, etc. One could foreseebeing able to assess the whole proteome through a series ofchemical probes. This would greatly simplify the analysis of theproteome.

ANALYTICAL TECHNIQUES

Proteomics technologies have been very successful for study-ing lower complexity proteomes (fungi, etc. . . .). However, thetechnology is still the limiting factor for the exhaustive analysisof proteomes from more complex organisms. The developmentof technologies to extend the dynamic range, and the peakcapacity of analytical techniques is actively being pursued by manygroups around the world; in particular, the development ofelectrophoresis, protein chips, and liquid chromatography and MS.

Electrophoresis. Sample preparation is important for a suc-cessful proteomic analysis. Electrophoresis is still the mostcommonly used technique for sample separation. Over the years,electrophoresis techniques have improved to meet the increasingdemand for sample separation. Protein samples are usuallyextracted from the cells or tissues using different lysis bufferscontaining salts, denaturants and detergents. However, directanalysis of these extracts by MS is often not possible due to theincompatibility of the lysis buffers with MS.

To handle these incompatibilities, Liu et al. (159) developed amethod named Three-layer Sandwich Gel Electrophoresis (TSGE)that allows the proteins to be rapidly cleaned-up. Briefly, a three-layer sandwich gel is assembled in a 4 mL Electro-Eluter glasstube with an acrylamide sealing layer (bottom), an acrylamideconcentration layer (middle) and an agarose loading layer (top)(159). By electrophoretically driving the proteins from the agarosematrix into the concentration layer, the proteins of interest aredesalted and concentrated, which facilitates downstream pro-teolytic digestion and LC-MS analysis (159).

Electrophoresis can be used not only for protein samplecleanup, but also for size-based protein separations. To achieve abroad mass range proteome separation in a fast, effective,reproducible, and high-yield format, Tran and Doucette (160)established a separation technique, termed gel-eluted liquidfraction entrapment electrophoresis (GELFrEE). The GELFrEEdevice is composed of four major parts: a cathode chamber, thegel column, a collection chamber, and an anode chamber (160).The gel column is used to separate proteins according to theirintrinsic molecular weights, and the proteins are ultimately elutedfrom the column and gathered in the solution phase. This methodallows for rapid (as fast as 1 h), broad mass range (from below

10 to 250 kDa) proteome separations in the low-microgram tomilligram range.

Conventional two-dimensional gel electrophoresis systems aretime-consuming and labor-intensive for protein separation. Aminiaturized, fully automated 2DE system was developed byHiratsuka et al. (161). Once the samples and buffers are installed,all of the subsequent procedures are performed automaticallywithin 1.5 h (161). This system can offer fast, practical, portable,and automatic two-dimensional electrophoresis (161). Further-more, Emrich et al. developed a microfluidic separation systemfor performing two-dimensional differential gel electrophoretic(DIGE) separations of protein complexes (162). However, thesegel-based methods suffer from a few shortcomings. They areunable to isolate proteins with extreme molecular weight and pIvalue; and they have narrow dynamic range and limited sensitivity.

Protein Chip. Protein chips, also known as protein microar-rays, have been employed for antibody-based assays or purifica-tions and for studying protein-protein interaction mapping,protein kinases substrates, and targets of biologically active smallmolecules. Fan et al. (163) developed an integrated blood barcodechip that can achieve sensitive measurements of a large panel ofprotein biomarkers over broad concentration ranges. One impor-tant caveat is that miniaturize devices can often only handle verysmall sample volume, which means that biomarkers of lowerconcentration are not likely to be observed. Beyer et al. (164)developed a new method for combinatorial synthesis of peptidearrays onto a microchip. This method can perform particle-basedin situ synthesis of peptides by embedding activated amino acidswithin particles. These particles are addressed onto a chip byelectrical fields generated by individual pixel electrodes. Ram-achandran et al. (165) described a next-generation, high-density,self-assembling functional protein array for producing fresh proteinin situ, with high yields of protein expression and capture withminimal variation and good reproducibility. This will pave the wayfor the study of protein function in both large scale and highthroughput.

Liquid Chromatography and Mass Spectrometry. Therehave not been any recent breakthrough developments in liquidchromatography (LC) techniques. One novel chromatographicmethod to be highlighted (RePlay system) allows online reanalysisof 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 directlyanalyzed, 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 improve-ments in ion chromatography, such as combinations of weak-anionexchange (WAX) and SCX resins (167), monolithic columns forstrong cation exchange (SCX) chromatography (168), and pHgradient elutions (169, 82, 170, 171).

One challenge in LC-MS is improving the limit of detectionto effectively analyze low-abundance proteins. The peptide hydro-phobicity is a main determinant of the electrospray ionizationresponse. To increase the electrospray ionization response, Frahmet al. (172) demonstrated a strategy named ALiPHAT (augmentedlimits of detection for peptides with hydrophobic alkyl tags), i.e.,adding an octylcarboxyamidomethyl modification via alkylation


chemistry to cysteine-containing peptides. Another challenge forMALDI-MS is sample preparation and the choice of matrix.Garaguso et al. (173) improved the analysis of peptides by MALDI-MS by using the 2,5-dihydroxybenzoic acid matrix and prestruc-tured sample supports (matrix layer).

Imaging MS (IMS) has been one of the most exciting newapplications of MS in recent years. It can be used to investigatethe distribution of molecules within biological systems throughthe direct analysis of thin tissue sections (174-176). MALDI andsecondary-ion MS (SIMS) are mainly employed for imaging MS.However, they have some shortcomings: the spatial resolution ofMALDI-IMS is limited due to the matrix crystal size (typicallymore than 10 µm), while secondary-ion MS has extremely highlateral resolution (100 nm) but leads to fragmentations of analytemolecules. An alternative approach was developed by Northen etal. (177) that uses “initiator” molecules trapped in nanostructuredsurfaces or “clathrates” to release and ionize intact moleculesadsorbed on the surface, nanostructure-initiator MS (NIMS). Thismethod allows high lateral resolution (about 150 nm), has highsensitivity, is matrix-free, and reduces fragmentation.

There are other interesting developments in MS worthy ofmention. The introduction of HCD in the LTQ Orbitrap XL hasbeen used to perform peptide fragmentation in the nitrogen-filledoctopole collision cell (71). After fragmentation, the resultingproduct ions re-enter the C-trap and are then analyzed by theOrbitrap. The HCD technique can be applied for peptide identi-fication, for peptide de novo sequencing, and for the sequencingand quantification of iTRAQ labeled peptides. Besides conventionalCID, ECD, and ETD, Chen et al. (178) introduced a newfragmentation method, named ambient thermal dissociation. Thisnew technique allows the separation and reionization of neutralfragments at ambient pressure outside of the mass spectrometer.It can provide useful sequence information from both ionic andneutral fragments via direct thermal dissociation and from neutralfragment reionization (178). In addition to fragmentation methods,algorithms are also critical in tandem MS. A data-dependentdecision tree algorithm (DT) was developed by Swaney et al. tomake unsupervised, real-time decisions of which fragmentationmethod to use based on the precursor charge and m/z (179).

BIOINFORMATICSProtein-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 theway for the development of computational methods that uses thisdata as a learning set to predict protein interactions (186, 187).In this section, we will explore the most recent computationalmethods developed to predict protein structures and interactions.

The use of three-dimensional (3D) protein structures to predictphysical binding has become increasingly common over the pastyears (188, 189). Recently, Berman et al. have reported thefoundation of the worldwide Protein Data Bank (wwPDB) (190).The wwPDB was not only created to recognize the internationalnature of the PDB archive but also to ensure that the contentand the format of data remain uniform. The PDB archive nowhas more than 40 000 structures. Moreover, the wwPDB hascreated tools (ADIT, ADIT-NMR, and AutoDep) that are able to

capture, annotate, and process 3D structures using a commonstandard (190).

Another approach which can be used to identify PPIs is calledprimary protein structure. This method is based on the hypothesisthat PPIs are mediated through a specific number of shortpolypeptide sequences. Support vector machine (SVM)-basedtechniques have shown that the primary sequence can be usedto predict PPIs (191, 192). Shen et al. introduced an SVM-basedmethod combined with a kernel function and a conjoint triadfeature abstract that helps to predict PPI in human proteins (193).In order to reduce the problem of overfitting, which occurs whenthe learning performed is too long or when the training examplesare rare, they used more than 16 000 PPI pairs to generateprediction models. With this method, Shen and his colleague havebeen able to obtain an average prediction accuracy of 83.90%.

Wang et al. have recently developed an automated methodcalled InSite (Interaction Site) for identifying PPI binding siteson a proteomewide scale. This method used multiple categoriesof characteristics/approaches as input information to predictspecific binding regions such as a library of conserved sequencemotifs, a heterogeneous data set of PPI obtained from multipleassays, and any other indirect evidence of PPI and motif-motifinteractions (194). After integration of the information in thesedata sets, InSite generates a prediction in the form of “Motif Mon protein A binding to protein B”. InSite’s algorithm is based onthe following three assumptions: (i) the interaction between pairsis created from high-affinity sites on the protein sequences, (ii)the binding sites are covered and characterized by motifs ordomains (supported by Caffrey et al.) (195), and (iii) the samemotif is participating in the mediation of the multiple interactions.To determine a prediction, InSite models the noise from a high-throughput assay and from the possibility that two proteins fromthe same complex do not interact. Because of the use of both theassay and the noise for the identification of the complexes, theinteractions data set is bigger than any used before, thus,providing a higher coverage and an increase of robustness. TheInSite source code is publicly available at http://dags.stanford.edu/InSite/.

QUANTITATIVE SOFTWARE DEVELOPMENT

In the past 2 years, a couple of new quantitative softwareapplications and packages have been introduced to help research-ers resolve the computational challenges involved in quantitativeprocesses. Park et al. have developed a quantitative analysissoftware tool called Census (196). It can handle data that isderived from most types of quantitative proteomics labelingstrategies such as SILAC (3) and iTRAQ (197), as well as label-free experiments. The Census software is based on RelRex, aprogram previously built by the same group (198). Census iscapable of performing quantitative analysis of MS or MS/MSscans, and it currently supports MS1/MS2, DTASelect, mzXML,and pepXML as input file formats. Census includes features suchas (i) the ability to use high resolution and high mass accuracyto improve the quantification, (ii) the ability to perform quantifica-tion from spectral counting and LC-MS peak area, (iii) multiplealgorithms (weighted peptide measurements, dynamic peak find-ing, and postanalysis statistical filters) which are used in order toreduce the false positive and improve the protein/peptide ratio,


(iv) the detection of singleton peptides. This software is availableat http://fields.scripps.edu.

In 2007, Lu et al. described a novel technique for proteinquantification called absolute protein expression measurements(APEX) (199). This technique was developed to solve the problemof peptide physicochemical properties that affect peptide spectralcounts. This is mainly a quantitative technique used in MS basedon label free protein (199). The APEX approaches incorporate amachine learning method to classify the derived peptide detectionprobabilities that are used to predict the number of tryptic peptidesexpected and to create a correction factor for each protein toimprove the result over basic spectral counting. APEX features aZ-score analysis for the identification of protein expression, crossvalidation 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 packagefor label-free quantification of high mass resolution LC-MS data,named SuperHirn (200). This platform detects and tracks featuresin LC-MS patterns, combines them into a MasterMap, and thennormalizes the features’ intensity across samples. This willeventually lead to normalizing the feature profile trend byclustering. The clustering profiles are used to identify peptidesand proteins after making a statistical correlation with thetheoretical protein concentration (200). SuperHirn is compatiblewith mzXML formatted Qtof, FT-LTQ, and Orbitrap data andimports MS2 information in pepXML format. SuperHirn wasprogrammed in C++; the source code and all the documentationare available at http://tools.proteomecenter.org/Super-Hirn.php.

MaxQuant is an integrated suite of algorithms developed by Coxand Mann for high-resolution quantitative MS (201). MaxQuantfeatures a pipeline peak list generation algorithm, SILAC basedquantification algorithms that create a three-dimensional object inm/z for the peptide pairs, false positive rate determination algorithmsbased on search engine results, peptide to protein group assembly,data filtration, and visualization. Cox and his colleague have beenable to increase the proportion of identified fragmentation spectrato 73% for SILAC peptide pairs via the unambiguous assignment ofthe isotope and miscleavage state and individual mass precision(201). MaxQuant was developed for the.NET framework and iswritten in C++. The executables are available at http://www.maxquant.org/. The source code of the algorithms is available assupplementary data in Cox and Mann’s paper.

DATABASE RESOURCESIn the past decade, high-throughput techniques have generated

a massive amount of data. These data have been stored innumerous databases. The database development has been a highlyproductive segment in bioinformatics. While a complete listingof all database development innovations is not feasible in thispaper, we will attempt to focus on a few of those databases.Recently, Depledge et al. introduced a new database of amino acidrepeat sequence named RepSeq that clearly differentiates betweenall repeat types (202). RepSeq is a web-based database applicationdeveloped to identify repeat sequences from lower eukaryoticpathogens, but it can be used to study proteomes from any givenorganism. The RepSeq algorithm was developed in PERL and isable to identify both perfect and mismatch repeats. This applicationis 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 SRRsfrom a repeat of 3 residues and above (202). RepSeq is availableat http://repseq.gugbe.com.

UbiProt is one of the latest databases developed for ubiquiti-nation (203). The UbiProt web interface software was created fromPHP + SMARTY template framework, and it is managed onMySQL 4.0. This public database contains more than 400individual proteins from multiple organisms. This informationincludes the target protein, the ubiquitination sites, the structuresof multiubiquitin chains, and the features of the ubiquitinationmachinery. Unlike other databases that contains ubiquitinatedproteins, such as Swiss-Prot (204) and the Human ProteinReference Database (HPRD) (205), UbiProt supports complexqueries, it does not have the search redundancy problem (itreturns not only pure ubiquitinated proteins but also numerousenzymes from the ubiquitination cascade), and it does not lackinformation about ubiquitinated sites (203). UbiProt is availableat http://ubiprot.org.ru.

Kuntzer et al. have recently introduced BNDB (biochemicalnetwork database), a database that contains a complete semanticintegration 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 onan oriented object data model called BioCore and is implementedon the MySQL relational database. This warehouse takes advantageof three ways to access the data: (i) a web interface which allowsthe user to create multiple types of searches, (ii) a network visualizercalled BiNA that allows the user to visualize the metabolic andregulatory networks in a sophisticated graph layout, (iii) a program-ming interface that offers a collection of implemented analysisroutines. BNDB can be accessed at http://www.bndb.org.

Chatr-aryamontri et al. published a new version the MolecularInteraction (MINT) database in 2007. MINT has undergone aprofound reorganization of both the data model and the datastructure over the past 4 years (214). The database has adoptedthe IntAct (212) relational model that consists of representingthe protein complexes and the other types of molecules asinteraction partners (214). In addition, MINT is now compatiblewith all the tools developed by the IntAct consortium. MINT nowhas more then 95 000 physical interactions involving 27 461proteins from 325 organisms (214). MINT is based on thePostgreSQL database management system and the data can beaccessed as Java objects through the IntAct (212) by using theApache Object Relational Bridge (OBJ). The MINT data isgenerated from experimental protein-protein interaction dataextracted from curated literature. The MINT database is accessibleat http://mint.bio.uniroma2.it/mint/.

MANAGEMENT SYSTEMA laboratory information management system (LIMS) is a

software package that manages laboratory samples, instruments,users, and data. LIMS is divided into two categories: (i) enterpriseLIMS is designed to link the laboratory system to an organizationsystem and can generally be quite expensive and (ii) a freelyavailable LIMS, which is an open source so that the user canchange and improve the software. Mass Spectrometry AnalysisSystem (MASPECTRAS) is a new, free, management systemdeveloped by Hartler and his colleague for the analysis ofproteomics LC-MS/MS data (215). MASPECTRAS allows for


comparison of the results of multiple search engines such asSEQUEST (216), Mascot (217), Spectrum Mill, X! Tandem (218),and OMSSA (219). This software management system integratespeptide validation, Markov clustering algorithm, and ASAPRationalgorithm for quantification. Moreover, it includes customizabledata retrieval and visualization tools. MASPECTRAS is availableat http://genome.tugraz.at/maspectras.

IntelliMS is a basic management system developed by Kwonet al. at the Yonsei Proteome Research Center (220). The keyfunctions of this system are (i) data importation in mzXML andmzData fotmat, (ii) a score filtering system that uses the empiricalBayes (221) and target-decoy search approaches (222), (iii) anID Network that not only facilitates navigation through the proteinidentification process but also shows how the same mass spectrumcan identify different proteins or peptides from various searchengines, and (iv) a data sharing and conversion system that allowsa user to share a project and to convert the data to a PRIDE XMLor MS Excel file. IntelliMS is publicly available at http://intellims.proteomix.org and http://intellims.sourceforge.net.

CONCLUSIONThe 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 ofprotein interactions and the potential it has shown for the ongoingmapping of the human interactome (223). Nonetheless, there aresome serious issues in proteomics in terms of data quality andthe biological validations of the results. A blatant example hasbeen in the area of biomarker discovery, where many reports ofnovel biomarkers were never validated. Furthermore, as weincrease throughput and optimize techniques, we should remaincareful about the quality of the data generated and its usefulnessto the biological community. The generation of suboptimal datawill only slow our progress and damage the credibility ofproteomics. Fortunately, advancement in bioinformatics, massspectrometry, protein tagging, and affinity purification techniquesare getting us ever closer to tackling these issues.

ACKNOWLEDGMENTFred Elisma, Houjiang Zhou, Ruijun Tian, and Hu Zhou

contributed equally to this review.

Mohamed Abu-Farha completed his Honors B.S. degree in biochemistryand biotechnology at Carleton University. He also obtained his M.S. fromthe Biology Department at Carleton University. Currently, he is a Ph.D.candidate under Professor Daniel Figeys’s supervision at the Universityof Ottawa in the Department of Biochemistry, Microbiology and Im-munology. He was recognized as an NSERC scholar during his Ph.D.studies. Currently, he is working on studying chromatin modifying enzymesusing proteomics and molecular biology techniques.

Fred Elisma completed a B.S. degree in biochemistry, M.S. degree inbiology, and a Diplome d′Etudes Superieures Specialisees degree inbioinformatics at the Universite du Quebec A Montreal. He is currentlya bioinformatician for Professor Daniel Figeys at the Ottawa Institute ofSystems Biology, University of Ottawa.

Houjiang Zhou completed his B.S. degree in Chemistry at Shannxi NormalUniversity, Xi’an, China. He also obtained his M.S. degrees in analyticalchemistry from Southwest University, Chongqing, China. He is currently aPh.D. candidate at the Dalian Institute of Chemical Physics, Chinese Academyof Sciences, Dalian, China under the supervision of Prof. Hanfa Zou.Houjiang Zhou is presently involved in a collaborative research initiativefor developing new methods to study the dynamic change of phosphoproteomics

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

Ruijun Tian completed his B.S. degree in chemistry at Inner MongoliaUniversity, China in 2002. He obtained his Ph.D. degree in chemistryfrom the National Chromatographic R&A Center, Dalian Institute ofChemical Physics, The Chinese Academy of Sciences. During his Ph.D.studies, he got the President Award of Chinese Academy of Sciences. Heis currently a postdoctoral fellow working for Dr. Daniel Figeys at theOttawa Institute of Systems Biology, University of Ottawa. His currentresearch interests are quantitative proteomic study of human embryonicstem cells and combined proteomic and metabonomic study of proteininteractome.

Hu Zhou obtained his B.S. degree in biology at Nankai University, Chinain 2001. He received his Ph.D. degree in biochemistry and molecularbiology from Shanghai Institutes for Biological Sciences for methoddevelopments in liquid chromatography and mass spectrometry in thesummer of 2007. He is presently working as a postdoctoral fellow forProfessor Daniel Figeys at the Ottawa Institute of Systems Biology,University of Ottawa, and is focused on technology developments ofproteomics and lipidomics.

Mehmet Selim Asmer is currently completing his Honors B.S. inBiomedical Science at the University of Ottawa. During his undergraduatestudies, he has been awarded several distinctions including the Dean’slist awards, Faculty awards of excellence, NSERC OGI Research Fellow-ship. As an Honor’s student at the Ottawa Institute of Systems Biology,University of Ottawa, he is using mass spectrometry to study the role ofthe chromatin modifying enzymes.

Daniel Figeys is a professor in the Department of Biochemistry, theDirector of the Ottawa Institute of Systems Biology, and a Tier-1 CanadaResearch Chair in proteomics and systems biology. Daniel obtained a B.S.and a M.S. in chemistry from the Universite de Montreal. He obtained aPh.D. in chemistry from the University of Alberta and did his postdoctoralstudies at the University of Washington. Prior to his current position,Daniel was Senior VP of Systems Biology with MDS-Proteomics. From1998 to 2000, he was a Research Officer at the NRC-Canada. Daniel’sresearch involves developing proteomics technology and their applicationsin systems biology.

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Proteomics: From Technology Developments to Biological Applications