Drug target identification and quantitative proteomics

Review

10.1517/17460441.1.5.477 © 2006 Informa UK Ltd ISSN 1746-0441 477

Drug target identification and quantitative proteomicsTao He†, Yeoun Jin Kim, Jenny L Heidbrink, Paul A Moore & Steven M RubenProtein Therapeutics, Celera Genomics, Rockville, MD 20850, USA

The emerging technologies in proteomic analysis provide great opportunityfor the discovery of novel therapeutic drug targets for unmet medical needsthrough delivering of key information on protein expression, post-transla-tional modifications and protein–protein interactions. This review presentsa summary of current quantitative proteomic concepts and massspectrometric technologies, which enable the acceleration of target discov-ery. Examples of the strategies and current technologies in the target iden-tification/validation process are provided to illustrate the successfulapplication of proteomics in target identification, in particular for mono-clonal antibody therapies. Current bottlenecks and future directions of pro-teomic studies for target and biomarker identification are also discussed tobetter facilitate the application of this technology.

Keywords: mass spectrometry, monoclonal antibody therapy, oncology, quantitative proteomics, target identification

Expert Opin. Drug Discov. (2006) 1(5):477-489

1. Introduction

The modern drug discovery pipeline has the following key components: i) targetidentification/validation, ii) lead candidate identification/optimisation and iii)preclinical and clinical development. The completion of the human genomeproject [1,2] coupled with the wealth of data regarding global gene mRNA expres-sion [3] has greatly influenced the target discovery process through the identifica-tion of many novel therapeutic targets associated with disease at the level ofmRNA. Although this approach has been successful in identifying potential tar-gets, the messenger RNA expression level does not always correlate with the pro-tein expression level [4,5]. Therefore, targets produced from mRNA profiling havean intrinsic attrition rate during target expression validation at the protein level.In contrast, the emerging technologies in proteomic analysis provide informationbased on a direct relationship to protein expression level, interactions and func-tionality [6,7], offering a more direct approach towards drug discovery and in par-ticular target identification and early validation. In addition, applications basedon proteomic analysis have begun to enter into the field of biomarker discoveryfor patient treatment and care monitoring [8]. This review focuses on proteomic analysis for protein expression in target identifica-tion. Readers interested in functional proteomics are referred to a recent publishedreview [9]. With this in mind, the initial discussion concentrates on the current technolo-gies and methodologies in the quantitative proteomics field, especially those in massspectrometry. This is followed by an in-depth discussion about strategies in comparativeproteomics and their application in target identification and early validation.

2. Proteomic concepts/technology

2.1 BackgroundThis section of the review summarises a number of new or well-establishedproteomic technologies that enable the identification of candidate drug targets.

1. Introduction

2. Proteomic concepts/technology

3. Applications of quantitative

proteomics in drug target

identification

4. Conclusion

5. Expert opinion

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Drug target identification and quantitative proteomics

478 Expert Opin. Drug Discov. (2006) 1(5)

Researchers are taking advantage of the completion of thehuman genome project in combination with the develop-ment of mass spectrometric platforms. A mass spectrometeris a powerful tool for proteomic analysis that is used tomeasure the mass of molecules that have been converted intoions. The invention of matrix-assisted laser desorption ioni-sation (MALDI) [10] and electrospray ionisation (ESI) [11]

technologies have played a key role in high-throughput massspectrometry based proteomics. There are two mainfractionation technologies commonly used prior to massspectrometric analysis: gel-based and liquid chromatography(LC) based separation.

2.2 Separation technologiesAlthough the shortcomings of two-dimensional polyacryla-mide gel electrophoresis (2DE) have been recognised [12],2DE is widely used as a separation step before mass spec-trometric analysis. The first dimension of the 2DEapproach is isoelectric focusing (IEF), which allows separa-tion of proteins based on their pI values. The seconddimension is separation on a sodium dodecyl sulfate poly-acrylamide gel (SDS-PAGE), in which proteins are sepa-rated based on their molecular weight. Protein spots arethen visualised by staining with either Commassie blue, sil-ver or fluorescence dyes. Proteins are identified using pro-teolysis (e.g., with trypsin) and mass spectrometry oramino acid sequence analysis. Limitations of this methodinclude the inability to automate the analysis, a smalldynamic range, low sensitivity and difficulty in investigat-ing extremely acidic or basic proteins and membraneproteins [12]. In contrast, many non-2DE platforms use multidimensionalLC which is much more amenable to automation. ‘Shotgun’proteomics consists of the identification of all peptides result-ing from the digestion of intact protein mixtures by LC-tan-dem mass spectrometry (LC-MS/MS) [13]. To improve thedetection sensitivity, nanoflow high pressure liquid chroma-tography (HPLC) has been developed as the front-end forintroducing samples to the mass spectrometer. Whereas mul-tiple stages of chromatographic separation (e.g., strong cationexchange followed by reversed phase) at the peptide levelallows the identification of numerous proteins, the complexityof an intact protein digest is much greater than which wouldallow for a complete proteome analysis. Therefore, techniqueshave been developed to enrich samples for a specific popula-tion at the cellular, protein and peptide levels, therebysimplifying the mixture [14].

2.3 InstrumentationA typical mass spectrometer consists of three main compo-nents: an ion source for sample introduction, the mass ana-lyser that separates ions based on their mass-to-charge ratios(m/z) and the detector to measure the ion flux. Although thereare various types of instruments on the market, whatdifferentiates them is effectively the mass analyser. Table 1

shows the characteristics of several types of mass spectrome-ters commonly used in proteomics. Mass accuracy (the abilityto measure the correct molecular mass) and mass resolvingpower (the ability to distinguish ions which are close togetherin molecular mass) are two important performancemeasurements of a mass spectrometer.

Initial shotgun proteomic experiments were carried outwith ESI and ion trap (IT) mass spectrometers, which havethe advantage of good sensitivity and fast scanning rates(leading to a higher throughput) while being relatively lowin cost [15]. However, IT instruments suffer from inherentlow resolution and poor mass accuracy due to low ion capac-ity and/or space charging effects. In contrast, time-of-flight(ToF) mass spectrometers now have greatly improved resolu-tion (routinely ≥ 10,000) and mass accuracy (with propercalibration). The linear ion trap (LIT) analyser is animprovement over traditional IT in that it has a larger ioncapacity and trapping efficiency (improving sensitivity andmass accuracy) and can also operate in a higher resolutionmode [16]. The various types of analysers are often combined toenhance their capabilities. For example, quadrupole (Q) ana-lysers have been added to the front end of ToF analysers tocreate Q-Q-ToF mass spectrometers, which can be used inconjunction with either MALDI or ESI sources. The advan-tage of this configuration is the capability of performing tan-dem MS (MS/MS) experiments for primary structuralelucidation. MALDI is typically used as the source for theToF-ToF mass spectrometer, which can generate high energyfragmentation spectra used for distinguishing isobaric aminoacids [17]. The Q-Q-LIT hybrid design combines scanningcapabilities of the triple quadrupole mass spectrometer (MS)including precursor ion and neutral loss scans as well as multi-ple reaction monitoring (MRM) with improved sensitivityover the conventional 3D ion trap [18,19]. MRM experimentscan result in very selective detection and quantification ofknown peptides due to the process of monitoring ion charac-teristics of the precursor, as well as its fragments within thesame experiment.

Several high-performance hybrid mass spectrometers haverecently emerged that lend themselves to improving the qual-ity of proteomic data and expanding the potential applica-tions for drug target identification. The introduction [20] andlater commercialisation [21] of a Fourier transform ion cyclo-tron resonance mass spectrometer (FT-ICR-MS) with anexternal ion accumulation source has led to the availability ofliquid chromatography time scale instrumentation withhigher mass resolution and accuracy, while permitting a moreroutine/automated analysis of proteomic samples with thistype of mass analyser. One of the commercially available ver-sios, the LTQ-FT-MS, is a hybrid of a linear quadrupole iontrap and an ICR mass spectrometer. The use of LTQ in thisconfiguration results in better scan speeds needed for complexmixture analysis and improved fragmentation spectracompared with the dual octupole ion injection source. The

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He, Kim, Heidbrink, Moore & Ruben

Expert Opin. Drug Discov. (2006) 1(5) 479

advantage of using the FT-ICR MS is that very high massaccuracy and resolution can be obtained. Whereas many stud-ies using this hybrid instrument use the FT-ICR portion ofthe instrument for MS only full scans, the LTQ side is oftenused to acquire MS/MS spectra due to its faster scan ratescompared with the FT-ICR analyser in this configuration.

Another hybrid mass spectrometer based on a new massanalyser called the Orbitrap [22,23] has very recently been com-mercially developed as the LTQ-Orbitrap [24]. In this analyser,ions are trapped in an electric field created by a voltageapplied between an inner spindle-like electrode and a concen-tric outer barrel-like electrode. High resolution and massaccuracy can be achieved with the Orbitrap mass spectrome-ter, with the added advantage of lower cost and larger spacecharge capacity compared with the LTQ-FT-ICR MS.

2.4 Protein identificationMS-based discovery has the ability to determine the identityof proteins based on the amino acid sequence of peptides.

The goal of the MS/MS experiment is to determine theprotein or gene from which a peptide is derived. A conven-tional MS/MS spectrum is created by collisonally induced dis-sociation (CID) of peptide ions (precursor ions) withmolecules of an inert gas. Following CID, the precursor ionfragments at the amide bonds, giving rise to mainly y- (C-ter-minal ion) and b-ions (N-terminal ion) (Figure 1). The result-ing acquired spectrum can be used to automatically determinethe amino acid sequence of the peptide by matching the spec-trum to a database using algorithms [25] such as SEQUEST[26], MASCOT [27] or ProteinPilot [28]. The spectra database isgenerated by in silico digestion and MS/MS fragmentation ofprotein databases (e.g., NCBI, UniProt, SwissProt). Thepotential approaches to improve shotgun proteomics data forprotein identification, for example, distinguishing proteinisoforms, are discussed in a recent publication [29].

2.5 QuantificationThe ability to quantify changes in protein expression levels(differential analysis) resulting from internal or external per-turbations of biological samples is essential to the meaningfulidentification of potential drug targets. Differential analysis

can be performed in a coupled (labelling) or decoupled(label-free) setting. Each method has its advantages. Table 2compares the characteristic features of each strategy as appliedto comparative proteomics.

In the decoupled strategy, relative expression levels can bedetermined by aligning peptide ion features from differentMS experiments (LC/MS maps), if data collection conditionsare consistent for both the liquid chromatography and massspectrometry steps [30]. In this scheme, a peptide ion’s charac-teristic m/z, z and retention time (RT) from one LC/MS mapare used to match it to the same ion in another LC/MS mapand the ion intensities are compared. These three attributes(m/z, z, RT) define a feature (peptide ion).

As various molecules, including peptides, are ionised withvarying efficiencies, the ion intensities in mass spectra are notinherently quantitative. Thus, relative quantitation using dif-ferent isotopic labelling schemes (coupled) is often used todetermine changes in expression levels of pooled control andcase samples.

As an example of differential analysis using the coupledmethod, isotope-coded affinity tagging (ICAT) methodol-ogy has become a common way of quantifying proteins,while at the same time reducing the complexity of pro-teomic samples [31]. In this method, cysteine residues aremodified with a reagent containing an isotopically codedlinker and an affinity tag, which allows one to capture onlythe cysteine-containing peptides out of a protein digest.The isotopically coded linker can be used to compare therelative level of protein expression between different sam-ples by labelling each sample with reagent containingeither eight hydrogen atoms (d0: ICAT ‘light’) or eightdeuterium atoms (d8: ICAT ‘heavy’). A second generationof ICAT, cleavable ICAT (cICAT), is now commerciallyavailable [32]. This new reagent uses nine 13C atoms as theheavy isotopic linker rather than deuterium atoms, so thatthe heavy and light modified peptides will coelute byreversed phase chromatography, thus, simplifying thequantitation. Furthermore, the cICAT reagent incorpo-rates an acid-cleavable site which allows one to remove thebiotin moiety. The benefit of this step is that it results inthe addition of a much smaller tag to the cysteine residue

Table 1. Commonly used mass spectrometers in quantitative proteomics.

IT LIT Q-Q-LIT Q-Q-ToF ToF-ToF LIT-ICR LIT-Orbitrap

Mass accuracy and resolution + + + + + + + + + + + + + + + + +

Dynamic range + + + + + + + + + + + + + + +

Sensitivity + + + + + + + + + + + + + + + + +

Duty cycle for MS/MS experiment + + + + + + + + + + + + + +

Cost $ $ $$ $$ $$ $$$$ $$$

MSn>2 capability Yes Yes Yes No No Yes Yes

IT: Ion trap; LIT: Linear ion trap; LIT-ICR: Linear ion trap-ion cyclotron resonance; MS: Mass spectrometer; Q-Q-ToF: Quadrupole-quadrupole-time of flight; ToF-ToF: Time of flight-time of flight.

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enabling an improvement in the quality of MSfragmentation spectra, especially for larger peptides.

Since ICAT was developed, a myriad of labelling schemeshave emerged that allow for comparative proteomic analysis,where relative protein expression levels of control versus casesamples are determined (see Table 2). In comparative proteo-lytic 18O labelling, either two 18O or two 16O atoms are incor-porated into the carboxyl termini of all tryptic peptidesduring the proteolytic cleavage of proteins from differentsamples [33,34]. In a way analogous to the ICAT workflow, thetwo peptide mixtures are combined, subjected to LC/MS andthe masses and isotope ratios of each peptide pair are used tocompare the relative protein expression level of the two sam-ple populations. In this approach the label is potentially incor-porated into all tryptic peptides instead of onlycysteine-containing peptides as with ICAT. Better coveragecan be achieved and the confidence for protein identificationand quantification will be improved. At the same time,additional separation steps may be necessary to reduce thesample complexity.

In the stable isotope labelling by amino acids in cell culture(SILAC) strategy, stable isotope-containing amino acids aresubstituted for the standard essential amino acids of cell

growth media [35]. Incorporating stable isotopes into samplesfor quantification in this way allows one to combine case andcontrol cells at an early stage in sample preparation, reducingindividual sample variability in the subsequent preparationsteps. Therefore, more accurate quantification can beachieved. The SILAC strategy has recently been expanded toinclude three cell populations (control, case 1 and case 2) byusing growth medium containing three forms of arginine –Arg0 (normal 12C6, 14N4), Arg6 (13C6, 14N4) or Arg10 (13C6,15N4) [36]. Although this technique allows multiple separationsteps without compromising reproducibility, it is onlyamenable to cells grown in vitro.

The iTRAQTM reagents (amine-reactive isobaric taggingreagents) were commercially developed to simultaneouslycompare multiple samples in the same experiment (up toeight) and also allow quantitation at the MS/MS stage [37].The chemistry of these reagents yields isobaric peptides thathave been derivatised at the N-termini and lysine side chainsof a digest mixture. Despite these peptides being indistin-guishable by MS, following CID they fragment to producesignature ions at m/z 114, 115, 116 and 117. The intensityof these ions represents the relative abundance of the peptideof each sample. The identity of the peptide can be obtained

Figure 1. MS/MS spectrum of tryptic peptide (TIQFVDWCPTGFK). The set of all theoretical fragment ions (b- and y-) from thispeptide are shown in A. The experimental spectrum B. is used to search database for peptide identification. MS/MS: Tandem mass spectrometry.

TIQFVDWCPTGFK mm/z 771.378

FKGFK

DWCPTGFKWCPTGFK

CPTGFK

TGFK

VDWCPTGFK

QFVDWCPTGFK

IQFVDWCPTGFK

K

y9y8

y7y6y5y4

y3y2y1

y10

y11

TIQFVDWCPTGFTIQFVDWCPTGTIQFVDWCPTTIQFVDWCP

TIQFVDWTIQFVDTIQFVTIQF

TIQTI

T

FVDWCPTGFKb3

b4b5

b6b7b8b9b10b11

b2

b1

TIQFVDWC PTGFK

b12

y12

A.

B.

655.43 838.56y7

953.59y8

549.41y5490.36

b4

343.27b3

215.18b2

187.19a2

101.09QK

589.45b5

1052.67y9 1199.71

y10

1327.80y11

0

%

100

276.19 375.29

417.31659.48 805.56 954.73935.59

1053.55

1309.69

1200.92

1310.85

1378.711525.02

bMaxyMax

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500

M/z

T I Q F V D W CPTGFKITQFVDWCPTGFK

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simultaneously by using the standard CID spectrum tosearch a peptide database. Absolute quantitation is achievedby labelling a synthetic peptide of the protein of interestwith one of the iTRAQ reagents and introducing the knownpeptide into the multiplex mixture.

The technologies mentioned in this section have the com-mon workflow step of digesting proteins into peptide mix-tures for analysis by mass spectrometry and are thus, named‘bottom up proteomics’. An approach that allows the analy-sis of ionised proteins that are limited in their dissociation,preserving valuable biological protein information, is theso-called ‘top down proteomics’ approach [38]. In the classi-cal top down approach, intact proteins are directly dissoci-ated, by electron capture dissociation or CID, and theproducts measured using a mass spectrometer with very highresolving power, the FT-ICR-MS. Recently, the top downconcept has been applied using a combination of electrontransfer dissociation, proton transfer charge reduction and alinear ion trap mass spectrometer [39]. The advantage of thetop down method is that biological information at the

protein level, such as protein post-translationalmodifications (PTMs) and protein isoforms, are retained.

3. Applications of quantitative proteomics in drug target identification

As discussed in the introduction, mass spectrometry basedproteomic analysis provides a powerful tool for identifyingtargets that are expressed differently on the disease than on thenormal tissue. This can provide a therapeutic and diagnosticopportunity based on the differential expression.

This section of the review illustrates a typical workflow of aproteomics-driven target discovery process. The mainstrategies in each step of the process are discussed. In additionto a summary of the literature, the current oncologyprogramme at Celera is used as an example.

3.1 Target discovery for therapeuticsThe proteomic discovery of potential new drug targets isbased on identification of changes in protein expression in

Table 2. Quantitative analysis schemes.

2DE* LC based

Coupled Decoupled

18O SILAC cICAT iTRAQ™ Label-free

Quantification Spot visualisation

16O/18O 15N/14N, 13C/12C

C0/C9 Reporter MS/MS fragments

Peak intensity from individual map

Label location N/A C-terminal Specific amino acids

Cysteine Amine (lysine side chain and N-terminus group of peptide)

N/A

Enriched sample population

N/A N/A N/A Cysteine-containing Amine-containing N/A

Multiplexing Unlimited 2 3 2 4/8 Unlimited

Labelling stage N/A Peptide Cell culture Protein or peptide Peptide/protein N/A

Accuracy in comparative analysis

+ +++ ++++ +++ +++ ++

Flexibilty and extensibility

++ + ++ + ++ ++++

Peptide/protein alignment and normalisation

Strict regulation and validation required

Aligned and normalised internally Strict regulation and validation required

Sample requirement for multiple comparison

Additional samples needed for each application One time map generation per sample can be used for multiple applications

* DIGE method is similar to coupled scheme and has not been considered in this table.cICAT: Cleavable TRAQ:Amine-reactive isobaric tagging reagents; LC: Liquid chromatography; MS/MS: Mass spectrometry/mass spectrometry; SILAC: Stable isotope labelling by amino acids in cell culture.

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disease and their relationship in disease progression. Thetechnological development in this field has, therefore,focused on building a comprehensive, robust and large scaleproteomic platform that can accurately profile the expres-sion level of proteins in the human proteome. This technol-ogy is suitable for identification of potential targets withvarious biological functions and from different subcellularlocations [40,41]. Cell surface target identification for mono-clonal antibody (mAb) development, the focus of Celera’soncology programme, is a good example of this approach.

One of the fastest growing areas for therapeuticdevelopment, fueled by the recent approvals of several mAbproducts, is mAb therapeutics for cancer therapy [42,43]. A listof the FDA-approved mAbs for cancer therapy is shown inTable 3. In proteomic approaches for mAb target discovery,proteins specifically overexpressed in disease populations areidentified. Depending on their biological role, suchcandidates could serve as targets for a functional mAb thatneutralises or activates a target-mediated biological pathway[44,45]. Alternatively, candidates could serve as targets for acytolytic mAb capable of inducing cytotoxic pathways such asantibody-dependent cell-mediated cytotoxicity, complementdependent cytotoxicity [46] or directly inducing cell killingthrough toxin-conjugation [47].

3.2 Focus on the subproteomeDirect analysis of the entire human proteome is technologi-cally challenging due to the extreme dynamic range of the setof proteins and complexity of the protein mixture [48]. There-fore, it is desirable to focus on a specific component of theproteome. Multiple steps of separation and/or enrichment areoften required to reduce the sample complexity.

The first stage of the proteomic discovery process is theacquisition of appropriate samples (tissue, cell line, serumetc.). To incorporate different types of samples into proteomicanalysis, a variety of technologies have been developed. Lasercapture microdissection (LCM) was developed to analyse

specific cell types in tissue samples [49]. However, thedrawback of LCM is the limited amount of sample recovered.To use archived samples, methods were developed to extractproteins out of formalin-fixed and paraffin-embedded (FFPE)tissues and those samples were submitted to massspectrometer-based proteomic analysis [50,51].

At Celera, both tissue and cell line samples are used forproteomic analysis. In the case of tissue samples, freshtumour tissue (from surgical resection) and the adjacentnormal tissue are acquired. When using cell lines, thestandardisation of growth conditions is critical. Once cellsare prepared from disaggregated tissue samples, they can beseparated based on the cell types or based on specific mark-ers on subpopulations of cells (e.g., epithelial cell,endothelial cell, or immune cells). Figure 2 shows anexample of the results of cell sorting that separates variouscell populations.

One strategy to reduce complexity is to enrich for pro-teins from a particular subcellular location. Plasma Mem-brane (PM) proteins include proteins involved in manyaspects of cell homeostasis and the structural organisation oftissues. Proteins associated with the plasma membrane areresponsible for a diverse set of functions including transportof ions and molecules, adhesion to other cells and extracellu-lar matrix, and signalling triggered by ligand–receptor inter-actions. They represent a significant component of currentdrug targets [52]. For example, the majority of targets ofcommercially available mAb therapies in the Table 3 are PMproteins, and PM proteins account for ∼ 50% of drugs pres-ently on the market [53]. The Celera target discoveryplatform focuses on PM proteins.

Although ∼ 25% of the open reading frames in fullysequenced genomes are estimated to encode integral membraneproteins [54], only a very small percentage of the proteins arelocalised to the PM. Enrichment of cell surface proteins is,therefore, an effective strategy to reduce sample complexity.The reduction in complexity simplifies each step downstream

Table 3. FDA-approved monoclonal antibody products in oncology.

Generic name Trade name Company Target disease Target Approval date

Rituximab Rituxan Genentech Non-Hodgkin's lymphoma CD20 26/11/1997

Trastuzumab Herceptin Genentech Metastatic breast cancer HER2 25/09/1998

Gemtuzumab ozogamicin

Mylotarg Wyeth Leukaemia CD33 17/05/2000

Alemtuzumab Campath-1H Genzyme Leukaemia CD52 07/05/2001

Ibritumomab tiuzetan Zevalin Biogen Idec Non-Hodgkin's lymphoma CD20 19/02/2002

Tositumomab_I131 Bexxar Corixa Non-Hodgkin's lymphoma CD20 27/06/2003

Cetuximab Erbitux Imclone Systems Advanced colorectal cancer EGFR 12/02/2004

Bevacizumab Avastin Genentech Metastatic colorectal cancer

VEGF 26/02/2004

EGFR: Epidermal growth factor receptor; HER2: Human epidermal growth factor receptor 2; VEGF: Vascular endothelial growth factor.

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of sample preparation, including chromatographic separations,mass spectrometric identification/quantification andbioinformatic analysis.

Various methods to enrich PM proteins have beendeveloped. The traditional method of enrichment of PM pro-teins is fractionation based on density (sucrose densitygradient) [55,56]. Lectin-based affinity capture has been usedfor glycosylation pattern analysis of cell surface proteins [57-59].A mixture of PM protein specific antibodies linked to mag-netic beads has been used to isolate PM [60]. Specific taggingtechnologies for PM proteins have been developed including:i) biotinylation of cell surface proteins followed by avidinaffinity selection [61-63]; and ii) periodation of sugar moietiesusing hydraside chemistry in conjunction with PNGase Fcleavage to capture N-glycosylated proteins [64].

As discussed earlier in Sections 2.4 and 2.5, various strat-egies and methods for protein identification and quantita-tion have been developed and implemented in proteomicstudies. The shotgun method is broadly used in industrialsettings due to its capability of high-throughput analysis andease of automation [65]. Inclusion of all peptides increasessample complexity and can lead to limitations of the quanti-tative analysis. Therefore, peptide level enrichment targetingspecific peptides is desirable.

Enrichment is often achieved by capturing the followingthree types of peptides: cysteine-containing peptides(cys–peptide), phosphorylated peptides, and glycosylatedpeptides. For cys–peptide enrichment: i) alkylation-basedICAT labelling uses biotinylated alkylating reagentfollowed by avidin affinity purification [31,32]; and ii)reduction–oxidation reaction-based thiopropyl sepharosecoupling method is relatively new, yet very selective [66-68].Using immobilised metal affinity chromatography andphosphospecific antibodies are two major methods for theenrichment of phosphorylated peptides [69,70], whereglycosylated peptides can be enriched by lectin-basedaffinity columns [57]. Celera currently uses cys–peptidecapture to reduce complexity.

3.3 Differential analysisThe conventional shotgun approach for quantitative analysisentails sequencing of all possible ions in LC/MS/MS modeand quantifying the identified peptides using intensitiesobtained in the precursor scan [13]. However, this strategy isassociated with multiple problems including: i) a bias towardsthe sequencing of the most abundant proteins; ii) the result-ing peptides include high redundancy and; iii) most of thesequenced peptide ions are not differentially expressed. This

Figure 2. Separation of different cell types using flow cytometry. A. Fresh lung tumour tissue sample is separated into epithelialand immune cell populations based on specific cell surface markers. B. Epithelial and endothelial cell populations are purified from kidneytumour tissue sample. Multidimensional separation provides accurate selection of epithelial, endothelial and immune cell populationsfrom fresh tissue samples.

Lung tumour Kidney tumour

Epithelial Immune Epithelial Endothelial

98.1%Epithelial98.1%epithelial

97.4% CD4597.4% CD45

97.2%Epithelial97.2%epithelial

95.7%Endothelial95.7%endothelial

A. B.7.7%Epithelial7.7%epithelial

26.5%Endothelial26.5%endothelial

Endothelial markerCD45

Endothelial marker Endothelial markerCD45CD45

CD45

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4521.4% epithelial

63.4% CD45(immune)

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problem can be circumvented by performing differentialanalysis (pattern analysis) to generate a target ion list beforeconducting LC/MS/MS analysis [71-73].

As discussed in Section 2.5, differential analysis can beperformed in coupled [74] or decoupled mode. Each methodhas its advantages (Table 2). The authors focus their discussionhere on the decoupled method.

In the decoupled strategy (known as ‘label free’ method),aligning the features correctly is one of the most important stepsin quantitative analysis. As the quality of alignment depends onaccuracy of m/z and control of RT shift, QC of mass measure-ment and chromatography are equally important. After featurealignment, intensity normalisation is required to compensate forthe variability in the amount of starting material, sampleprocessing and instrument performance. Various softwarepackages have been developed to aid the data analysis in adecoupled scheme [75].

Figure 3 shows scatter plots of common features from proc-ess replicates. The scatter plot in the Figure 3a represents thenormalised intensities of the common features detected inprocess replicates of the control sample. The box plot repre-sents the ratio (intensity 1/intensity 2) distribution ofcommon features between the process replicates. This dataindicates that 95% of common features are within a twofolddifference in intensity. Figure 3b is the normalised intensityplot from process replicates of the disease sample. The repro-ducibility obtained in the disease replicates is similar to that ofthe control replicates. Based on the reproducibility achievedin this experiment, features with > twofold differencesbetween control versus disease samples are valid candidates forfurther sequencing efforts. In differential analysis of these twosamples, as shown by Figure 3c, higher dispersion indicatesthat those two samples are significantly different. It should benoticed that even in the disease versus control comparison,

Figure 3. Differential analysis using decoupled LC/MS maps. Log2 intensities of common features detected in A. Process replicatesof the control sample; B. Process replicates of the tumour sample; C. Control versus tumour samples is plotted. Corresponding box plot(in Log2 scale) of ratio distribution for each data set is shown next to the scatter plot. The box contains 50% of the features, where 95%of the features are within the horizontal bars.

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65% of common features between disease and control are notdifferentially expressed (within twofold differences).

A great advantage of decoupled methods is the flexibility indata analysis. Once an LC/MS map is generated for eachsample, the map can be used in a variety of comparisons.

In summary, excellent reproducibility in sample preparation,high resolution in LC separation and high accuracy in massdetection ensure the overall data quality and increase the chancefor identifying novel targets.

3.4 Confirmation of target overexpressionTarget validation of the identified differentials is an inte-gral component of the drug discovery pipeline and with

the higher number of potential targets generated by ‘omics’approaches is a continuing challenge. The role of targetvalidation is to confirm the overexpression and/or func-tional role of the potential target in the disease phenotype.Properly designed strategies for confirmation of differentialexpression will certainly reduce the subsequent failure infurther development efforts. For expression confirmation,measurement of protein level in clinical samples can beperformed by multiple approaches includingimmunohistochemistry (IHC) or flow cytometry assumingnecessary antibody reagents are available. An example oftarget confirmation for a protein identified from Celera’sproteomic analysis is shown in Figure 4. The overexpression

Figure 4. Overexpression of CRA-0239 in lung carcinoma is confirmed by IHC. A. Mass spectrometric analysis of a peptide ion fromCRA-0239. The top panel shows the extracted ion chromatograms of lung tissue samples from normal and tumour samples. The massspectra are shown in the lower panel. The red arrow shows the intensity level in normal tissue sample. B. IHC images of lung normal andcarcinoma samples. Significant overexpression is found in lung carcinoma versus normal lung samples.IHC: Immunohistochemistry; MS: Mass spectrometry; TOF: Time of flight.

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of the protein (CRA-0239) is confirmed by IHC in lungcarcinoma tissue samples. Functional assays involve detect-ing phenotypic changes (e.g., inhibition of proliferation, orinduction of apoptosis) after modulating targetfunction/expression either at protein (e.g., functional anti-bodies) or mRNA (e.g., short interfering RNA) level.Knockout or transgenic mouse models can provide furtherknowledge about the function and importance of the targetin development.

4. Conclusion

The rapid advances in proteomics-based technologies overthe past decade have begun to have an impact on the drugdiscovery process, especially in the field of target identifi-cation. In addition to genomic expression profiling, pro-teomic studies promise to deliver distinct and pertinenttargets for downstream evaluation and validation. Thedevelopment in quantitative proteomic research coupledwith efficient subcellular fractionation schemes will cer-tainly provide better protein expression data with deeperproteome coverage, which will translate into attractive tar-gets with a lower attrition rate through target validation.At the same time, the complexity and the extreme highdynamic range issues in interrogating disease systemsrequire the development of more sensitive instrumentationand new strategies for improved fractionation.

5. Expert opinion

Mass spectrometry based quantitative proteomic methodsare being used in target identification and early validation byboth pharmaceutical industries and academic institutes.

Although these technologies provide new hopes foreffective and relevant targets, significant challenges remain.First, the size of the human proteome and the complexity

are too large for routine analysis. Although the totalnumber of genes in the human genome is only ∼ 25,000,the proteome is extremely complicated due to alternativesplicing and post-translational modifications. To meet thischallenge, instruments with better performance (resolu-tion, sensitivity and accuracy) and advanced search soft-ware are needed for protein identification. Second, thedynamic range of protein expression levels (estimated as106 – 109) is far beyond the limit of current analyticaltools. For example, the dynamic range of a typical massspectrometer is only ∼ 103. Meanwhile, there is no technol-ogy equivalent to the polymerase chain reaction (one of thekey elements of success of genome project) for proteome toamplify low abundant proteins. As a result, analysis of lowabundant proteins in the presence of high abundant pro-teins is inherently impossible. Hence, it is necessary toselect a specific subpopulation of the proteome for analysisdepending on the biological questions to answer. Alterna-tively, improvements in mass spectrometers towards higherdynamic range or novel experimental schemes with currentinstruments will provide better identification and quantifi-cation of proteins of lower abundance in a complex mix-ture [76]. Third, it remains difficult to obtain highly reliableand absolute peptide/protein concentrations from complexbiological samples. An increasing number of researchershave started to evaluate the MRM technology in determin-ing the absolute amount of peptide/protein [77]. Theauthors expect to see rapid advances in this field in thenext few years as the demand increases for dependableabsolute quantitative proteomic analysis.

Acknowledgements

The authors wish to thank the following colleagues atCelera: K McKinnon for preparing Figure 2 and K VanOrden for kindly providing IHC images used in Figure 4.

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AffiliationTao He†, Yeoun Jin Kim, Jenny L Heidbrink, Paul A Moore & Steven M Ruben†Author for correspondenceProtein Therapeutics, Celera Genomics, Rockville, MD 20850, USAE-mail: [email protected]

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Documents

Drug target identification and quantitative proteomics