Download pdf - Proteomics: drug target discovery on an industrial scale

Terence E. RyanScott D. Patterson*Celera Genomics Group,

45 West Gude Drive,

Rockville, MD 20850, USA.

*e-mail: scott.patterson@

celera.com

http://www.trends.com S45

Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002 A TRENDS Guide to Proteomics | Review

0167-7799/02/$ – see front matter ©2002 Elsevier Science Ltd. All rights reserved. PII: S0167-7799(02)02089-9

Proteomics represents the systematic and broad applica-

tion of technologies that have traditionally supported the

field of protein biochemistry. In its most common appli-

cation, proteomics is used to characterize differences in

protein expression between biological specimens.

Although proteomics technologies can be used to catalog

protein differences after metabolic perturbation, its great-

est therapeutic value lies in the comparison of cells from

normal tissue with those representing a disease state

(e.g. [1]). Such comparisons could enable the identifica-

tion of disease-specific biomarkers that could be used for

diagnostic or prognostic tests, or target proteins that have

the potential for drug intervention.

Owing to the variability of natural protein expression in

the same tissue between individuals (owing to inherent

genetic, metabolic, diurnal, environmental and nutritional

differences, among others), the disease specificity of an

observed protein-expression differential needs to be rigor-

ously demonstrated.This can be achieved by characterizing

the frequency of a differential expression across a range of

samples taken from many individuals with the disease, as

well as by the relative absence of the differential expression

in other normal tissues in the same individual. These

requirements, which demonstrate the specificity for the

disease state, require an experimental design that can

encompass large numbers of experimental samples and

controls, and effective interassay comparisons between

individual samples and among sample groups.The number

of individual samples required to generate statistical confi-

dence results from a complex mixture of biological and

laboratory process considerations. For example, a single

disease can be represented by various degrees of disease

progression or characteristic phenotype. Patients with acute

myeloid leukemia (AML) can generally be classified into

one of seven French–American–British (FAB)-AML classi-

fication disease groups [2,3]. Examination of AML sam-

ples requires that they be grouped accordingly to produce

meaningful results, or that a larger number of AML

samples are examined to identify ‘pan-disease’ patterns of

differential protein expression. In addition to relevant

disease subtypes, samples need to be grouped according

to tumor staging, degree of metastasis, and known

genetic lesions. The reproducibility (variability in repli-

cate processes) and sensitivity of laboratory processes also

contributes to the number of samples needed for exami-

nation; differentials at the limits of signal-to-noise identi-

fication will require a greater number of samples to

achieve statistical importance. Statistical evaluation of the

differentially expressed proteins will establish appropriate

levels of confidence for each observation; for the processes

outlined here, we have found that ∼ 20 samples per study

point is usually sufficient. However, in general, the greater

the frequency of representation of a particular protein-

expression differential in a range of samples correlates not

only with the degree of statistical significance, but also

with the level of interest in that differential as representative

of the disease process under study. The rigor required for

these comparisons suggests that proteomics approaches

need to become standardized within each laboratory and,

in addition, the laboratory should be able to process the

requisite number of samples required to provide statistical

confidence in the results.These requirements make a ‘factory’

approach to proteomic discovery essential: a facility where

standard protocols are applied to large numbers of samples,

with the ‘product’ being the generation of information

with a high statistical confidence.

Proteomics: drug target discoveryon an industrial scaleTerence E. Ryan and Scott D. PattersonThe discovery of targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Through theyears, several approaches have been used with varying degrees of success. These include target-independent screening oftumor-derived cell lines (disease-dependent), reductionist approaches to identifying crucial elements of disease-affectedpathways, disease-independent screening of homologs of previously drugged targets, disease-dependent ‘global’examination of gene transcript levels, and disease-dependent global examination of protein expression levels. Theseendeavors have been enabled by several major advancements in technology, most recently, the sequencing of the humangenome. This review identifies the technical issues to be addressed for industrial-scale protein-based discovery in theidentification of targets for therapeutic (or diagnostic) intervention. Such approaches aim to direct discovery in a way thatincreases the probability of robust target identification, and decreases the probability of failure owing to variable expressionin this emerging field.

http://www.trends.comS46

Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002Review | A TRENDS Guide to Proteomics

Standardization and methodologyThe need for standard protocols that are reproducible in

both their execution and data output heightens the

importance of methodology in large-scale proteomics.

The complexity of biological samples, as well as the capa-

bilities of the current-generation mass spectrometers,

enables the separation of proteins or peptides into discrete,

analyzable entities. Traditionally, this high-resolution

separation step has been achieved using 2D gel elec-

trophoresis (Fig. 1). Comparisons between samples must

therefore be made on the basis of separate 2D gel experi-

ments; this requires an extraordinary level of care to

ensure that protocols for gel preparation, sample prepara-

tion, sample loading, electrophoresis conditions, and pro-

tein ‘spot’ staining and identification, precisely match [4].

Chromatographic approaches are increasingly used for

proteomic studies as they provide this precision, are rela-

tively easy to automate, and the instrument software is

robust (Fig. 1).

The complexity of protein mixtures from cellular

lysates or fractions can undergo only limited reduction

using ion exchange, molecular sieving, or affinity chro-

matography. However, mixtures of proteins from limited

chromatographic fractionation can be proteolyzed as a

group, and the resulting peptides separated by reverse-

phase chromatography with online mass spectrometric

detection [5–9]. This ‘complex-mixture’ method of gen-

erating peptides for tandem mass spectrometric identifi-

cation has been widely used in academia and industry

because of its reproducibility and ease of automation [10].

It has gained further favor over gel-based methods

because it can detect low-abundance peptides [11], and

also gives a more complete representation of cellular

proteins (particularly membrane proteins). This review

discusses issues surrounding the large-scale application of

complex-mixture proteomic analysis for drug target dis-

covery: the first step in the drug discovery and develop-

ment pipeline (Fig. 2). However, it should be noted that

the platform described here can be applied not only to the

initial stages of the pipeline, but also to all subsequent

steps (except filing and marketing).

Normal Disease

Enrichment of cell type, subcellular organelle or protein class

Image analysis

Selected spot excision

Digestion and/or MALDI-MS

MALDI-MS

Identification

Digestion of proteins

Peptide capture (e.g. ICAT-peptides on avidin)

LC–MS (quantitative analysis)

LC–MS–MS

Identification

Stable isotope labeling (e.g. ICAT™)of samples separately (combine)

d0-ICAT d8-ICAT

TRENDS in Biotechnology

Figure 1. The two most commonly used analytical approaches in proteomics

Complex mixture analysis using 2D gel electrophoresis, liquid chromatography and isotope-coded affinitytag (ICAT) reagent are the current standards for analysis of protein expression levels on a broad scale.


Datamanagementand analysis

Data captureQuantitation IdentificationSeparation Fractionation

Preparation for analysis Sample processing Data analysis

Target discovery

Sampleacquisition

Sampleacquisition

Targetidentification

Targetvalidation

Lead IDoptimization

Preclinicaldevelopment

Clinicaldevelopment

Filing, salesand marketing

Figure 2. Workflow for large-scale proteomics approach for target discovery within a pharmaceutical setting

Although the schematic infers proteomics is applied only in target discovery, the platform can also be used for all additional parts of the traditional drug discovery pipeline (theuppermost flow-chart) except the filing and subsequent sales and marketing components. Of note is the increasing use of proteomics in the toxicology aspects of pre-clinicaldrug development.



Biological material – a variable start to aconstant processTo begin the discovery process, samples must first be

acquired or generated in-house; this is the point at which

the variability of biological material could potentially

confound experimental analysis and therefore demands

careful experimental design.

In a large-scale proteomics factory, the process design

should account for this variability by minimizing all con-

trollable variables once the sample has entered the factory

process (Fig. 2, Table 1). When established cell lines are

used, all elements of sample preparation can be con-

trolled, from growth medium through culture conditions

to cell fractionation. Additional data can be collected from

the cell line that are useful in making the most informed

and instructive comparisons. For example, part of the

sample preparation process could include the evaluation

of cell lines for their rates of apoptosis and DNA synthe-

sis, among other measures of physiology (Table 1). Using

such applied culture conditions ensures that cell line

comparisons have minimized differences.

For human clinical material, sample-collection specifi-

cation can play only a limited role, because such materials

are usually obtained as an adjunct to a necessary medical

procedure. Biofluid collection, particularly serum, has a

considerably simpler path to sample collection control

because the collection procedure is non-invasive and rela-

tively routine. However, diverse elements ranging from

the collection vessel to the posture of the patient, and

even the rank order of sample draw in a multiple-tube

phlebotomy, can affect the quality and protein content of

serum [12,13]. In addition, the elapsed time before cen-

trifugation, the storage temperature, and serum thawing

method have all been shown to play a role in the repro-

ducibility of clinical chemistry profiles. Attention to these

‘trivial’ but known issues can be crucial in the evaluation

of proteomic data from serum.

Human (as well as animal) tissue provides an additional

series of challenges to the proteomic researcher, particu-

larly those analyzing a large number of samples. In addition

to the obvious variability in tissue harvesting procedures,

and inherent patient variability, it is important to recognize

that tissue is generally heterogeneous, comprising many

different cell types. In some cases, the cell type under study

will comprise only a minor portion of the tissue, and

would therefore be difficult to isolate in sufficient quanti-

ties without resorting to mechanical disaggregation and/or

enzymatic digestion of extracellular matrix and adhesion

molecules. Both procedures result in some degree of cell

death and damage; methods must therefore account for

these effects, and quality standards need to be set for cell

preparations derived from disaggregated tissue (Table 1).

Table 1. Summary of processes used at each stage of protein-based target discovery andQA/QC approaches used to monitor these processesa

Preparation for analysis Sample processing Data analysis

Sampleacquisition

Separation Fractionation Quantitation Identification Data capture Datamanagementand analysis

In vivo samples Dissociation Enrichment for proteins Quantitative 2D gel electrophoresis spot Sample data ProjectHuman tissues into desired of specific classes protein analysis excision and enzymatic Biological data managementHuman fluids cell type Reduction of protein by 2D gel digestion, followed by MS Fractionation data Correlation ofModel organisms (e.g. flow mixture complexity electrophoresis or online LC–MS–MS for MS data data obtained (tissues and cytometry, through separation or LC–MS using simultaneous quantitation QA and/or QC with clinical fluids) LCM) Enzymatic digestion of ICAT reagents and identification or data andXenograft tissues Subcellular protein mixture, with quantitation followed by Pipeline (data experimentalIn vitro samples fractionation or without reduction identification capture) software stateCultured cells of peptide complexity Candidates forConditioned media evaluation

Monitoring – QA/QCFlow cytometry for Flow cytometry Protein quantitation Image analysis MS instrument calibration Data integrity LIMS systems markers for markers Data and protein Chromatographic Chromatographic data LIMS systems Data reviewApoptotic rate Markers for qualitative data data analysis analysis Confirmation of

subcellular analysis resultsProliferative rate organelles Chromatographic

data analysis

aAbbreviations: ICAT, isotope-coded affinity tag; LCM, laser capture microscopy; LC–MS, liquid chromatography–mass spectrometry; LC–MS–MS,liquid chromatography–tandem mass spectrometry; LIMS, Laboratory Information Management System; MS, mass spectrometry.



Laser capture microscopy (LCM) has been used to

isolate specific cells from sections but yields only a very

small number of cells. mRNA-expression level analysis

has been successful but only limited proteomic studies

have been published [14–16]. Cell separation using fluo-

rescently or magnetically tagged antibodies to obtain

only the desired population of cells are powerful tools

that enable a focused study, and in addition, can generate

sufficient material for the proteomic analysis. However,

each enrichment step involved in isolating the desired

population begs the question: ‘What have I lost?’ In the

proteomic analysis of tumor disaggregation, sorting for

the cancer cells alone will enable a direct comparison

with normal cells of the same type from a non-malignant

site in the same organ. However, tumor-specific differ-

ences in vasculature [17,18], stromal cells or extracellu-

lar matrix will all be lost because of the restriction to

cancerous cells. Protein expression differences in these

‘lost’ tumor compartments might reveal useful biomark-

ers for diagnosis, or potential drug targets for tumor

therapy.These gains and losses from specific cell isolation

from tissues must be carefully balanced during experi-

mental design because of their profound effect on the

type and quality of data that result. Each step in tissue,

cell or protein fractionation in large-scale proteomic

processes must reconcile the depth of analysis with the

experimental aim.

Protein fractionation – divide and conquerWhether using 2D-gels or chromatographic methods, a

high-resolution separation step is essential to produce an

appropriate analyte complexity for mass spectrometry. For

‘profiling’ experiments (those aimed at identifying as

many components in a sample as possible), too high a

level of complexity will result in a less than complete sur-

vey of peptides in the mixture owing to the duty-cycle

time of the mass spectrometer (that is, not all ions will be

selected for fragmentation). By contrast, if the mixture is

too simple the mass spectrometer would operate less

efficiently (through wasted cycles), therefore resulting in

fewer identifications per unit time, which would be

unsuitable in an efficient, large-scale facility. As men-

tioned previously, limited fractionation of whole proteins

is possible using chromatographic matrices; however, it is

possible to also simplify peptide mixtures substantially to

provide appropriate levels of complexity. For example,

modification of cysteine residues with an affinity reagent

enables the capture of only those peptides containing this

amino acid [19,20]. Based upon the observed cysteinyl

content of proteins, capture of only these peptides would

substantially simplify the peptide mixture before running

mass spectrometeric analyses.

Complexity reduction using side-group modification

is an important part of the peptide quantitation method

using the isotope-coded affinity tag (ICAT ) reagent [21].

This method is a variation of the traditional stable isotope

dilution theory [22] wherein cysteinyl residues in protein

mixtures are modified by one of two isotopic forms

(d0 or d8) of a common alkylating molecule (see Fig. 1).

Proteins from the comparative standard are then tagged

with the second isotopic form of the ICAT reagent. The

two samples can then be pooled, fractionated as desired,

proteolyzed to yield peptides that are then enriched for

ICAT -tagged peptides by avidin chromatography, and

analyzed by LC–MS (Fig. 3). The quantitative ratios of a

peptide found in both samples can be established by the

relative ion current signals for each mass tag. Ions display-

ing differential relative abundance can then be identified

by targeted MS–MS. Variations on this approach that are

now being explored include: (1) the incorporation of

mass tags through solid-phase isotope transfer [23];

(2) stable-isotope metabolic protein labeling before mass

spectrometric analysis of either intact proteins (using

high-resolution mass spectrometers [24]) or proteolytic

peptides [using matrix-assisted desorption–ionization

(MALDI)-MS–MS of gel-separated proteins or chromato-

graphically separated peptides] [25–28]; and (3) incor-

poration of 16O or 18O into the C-terminus of peptides by

tryptic protein digestion of samples in either H216O or

H218O, respectively [29–31].

Combining conventional protein fractionation with

isotope tagging means that sample complexity can be

matched with the available mass spectrometric analytical

capability of the laboratory, thus promoting operation at

peak efficiency. If desired, additional reductions in sample

complexity can be achieved using affinity reagents to

enrich for specific protein classes.

Antibodies represent an affinity reagent that can selec-

tively bind proteins with a defined primary or secondary

peptide sequence. However, antibodies rarely have the

broad specificity of capture required to produce protein

mixtures of a complexity that would enable proteomic

analysis (with the exception of antibodies directed against

post-translational modifications). In addition to capture

based upon protein sequence, various methods have been

developed to enrich for proteins or peptides bearing post-

translational modifications. For example, an extensive

literature has been developed for enrichment of phospho-

proteins [32,33], lectin-based capture of glycoproteins

[34,35] and other protein classes.

Molecules that interact with the functional character-

istics of proteins are more useful as affinity-capture

reagents. Such functional (or activity-based) affinity

reagents include, for example, substrates for an enzymatic



class, with capture based upon a high-affinity interaction

with a catalytic site. Indeed, classic ‘suicide substrates’ of

enzymes result in a covalent interaction of the substrate

and catalytic domain, enabling capture and exhaustive

washing of bound complexes. Stringent washing of com-

plexes is important for establishing the selective nature of

the affinity capture, and is effective in reducing the

amount of non-specific binders that would otherwise

complicate proteomic analysis. The availability of small-

molecule substrates for enzymes requiring activation also

promise to ‘profile’ the activation state for these enzymes

in differential analyses. Substrates that bind to activated

kinases [36,37], phosphatases [38], or proteases [39–42]

could be very useful reagents for proteomic studies because

the overall level of expression of these enzymes would

probably be relatively unchanged between the diseased

and normal cell state; instead, crucial differences might

exist in the proportion of ‘activated’ forms of these

enzymes. Such small-molecule affinity reagents might

include failed drug leads that lacked the high specificity

required for therapeutic use but that have a broad pattern

of reactivity, which is a desirable feature for generalized

capture reagents. Overall, the use of affinity capture reagents

has the potential to provide a much more coherent and

pharmaceutically useful catalog of differences between

cell states than non-selective comparisons could other-

wise enable. Furthermore, quality control of these

processes is more straightforward with the monitoring of

columns, buffers, chromatograms, and levels of enriched

or depleted proteins (Table 1)

Mass spectrometry – passing through the eyeof a needleDifferential protein expression analysis has two major

goals. The first is the relative quantitation of peptides and

the second is the identification of proteins by peptide-

mass fingerprinting of peptides, or tandem mass spec-

trometry of individual peptides. These goals are accom-

plished using mass spectrometry, and the numbers and

type of instrumentation have significant influence on the

throughput, accuracy and completeness of the protein ex-

pression comparisons that are the output of the extended

proteomic process.The depth of proteomic analysis of any

given sample is dependent on the amount and complexity

of the starting material, as well as the process and instru-

ment efficiency. Given the current sensitivity and dynamic

range of mass spectrometers, the detection of low copy

number proteins requires cellular starting material that is

590 592 594 596 598 600 602 604

596.0600.0

482 484 486 488 490 492 494

486.6

489.3

2+

3+

(a)

(b)

(c)

450

500

550

600

650

m/z

m/z

m/z

36 38

Time (min)

40


Rel

ativ

e in

tens

ityR

elat

ive

inte

nsity

Figure 3. Liquid chromatography-mass spectrometry data from an ICAT experiment to examinedifferential protein (peptide) expression

(a) A small portion of a liquid chromatography (LC)–mass spectrometry (MS) experiment represented as a 2D map, 35–41 min on the x-axis,450–650 m/z on the y-axis and false color to represent peak intensity. (b) and (c) The mass spectra of the doubly and triply chargedisotope-coded affinity tag (ICAT) pairs respectively, circled in (a). In (b), the ICAT pair is at ~1:1 ratio and in (c) the d0:d8 ratio is ~1:2.5.The lower mass ions in each pair are alkylated with d0-ICAT and the heavy forms are alkylated with d8-ICAT . Charge state can bedetermined from both the isotope cluster of each peptide along with the mass difference between the two forms. Data from a comparison ofcytosolic extracts generated using an on-line LC–MS system. Following analysis of the differentials, only those ions that display alteredabundance are targeted for MS–MS.



roughly equivalent to the number of cells commonly

found in biological samples such as solid tumors. Because

of the unavoidable losses that are inherent in tissue, cell,

and protein fractionation, the proteomic mass spec-

trometrist commonly works at the micro-scale, reflecting

the limited amount of available material. Recent advances

in capillary LC coupled with electrospray MS (microspray

and nanospray methods) now enable high-yield MS

analyses from even smaller amounts of peptides.

Electrospray ionization (ESI) technology enables MS

analyses on chromatographically separated peptides online

(Fig. 3). This method is ideally suited to the detection of

low-abundance peptides, as well as for quantitation via

the ICAT method.

For peptide identification, mass accuracy (in the

50–100 ppm range) and isotopic resolution are essential

for accurate peptide-mass fingerprinting and important

for tandem MS methods [43,44]. Of the two methods,

tandem MS analysis gives the most definitive peptide

identification and is the preferred method for complex

mixture analysis. The high-throughput, high mass accu-

racy and resolution requirement tends to drive instru-

ment selection towards multiple electrospray-hybrid

quadrupole–time-of-flight (TOF) instruments, or instru-

ments using a source MALDI and TOF detector (although

instruments with lower mass resolution can also be

used). Recently, MALDI instruments with high-repetition

rate lasers have gained recent attention because of their

short duty-cycle, which results in the collection of thou-

sands of tandem MS spectra within several seconds [45].

This rapid acquisition rate enables the collection of

large numbers of spectra from individual peptides, im-

proving signal-to-noise ratios and subsequent peptide

identification reliability. Selection of mass spectrometry

instrumentation for the industrial proteomics laboratory

requires attention to parameters that are not optimized

in a single instrument: high-throughput capability, ion

sensitivity, and mass accuracy. For this reason, industrial

proteomic laboratories commonly use a mix of mass

spectrometers, each with their own specific monitoring

requirements, although these basically include rigorous

calibration and monitoring of column, buffer and data

output (Table 1).

Data analysis – the capillary becomes apipelineAs outlined here, a mixture of mass spectrometers and

liquid chromatographs will be the foundation of the

industrial laboratory. Data extraction from this diverse

instrument mixture must be accomplished in a way that

enables data archiving and analysis, ideally to a single

computational platform. Relevant clinical information

collected on biological specimens must be added to these

data, along with the results of other biological analyses

performed on the sample during its preparation in cell

biology and protein chemistry. All of these data (includ-

ing quality control data;Table 1) need to converge on the

computational desktop of the bioinfomatician, and be

interpretable by laboratory personnel responsible for the

laboratory process. High-speed access to a completely

assembled genome of the organism under study is also

necessary, as well as bridging software and algorithms to

compare mass spectra with a computer-generated range

of potential peptide sequences and to assign a probability

score to peptide and protein identifications.

Algorithms have been developed that can identify

from which protein a peptide MS–MS spectra was derived

(e.g. [46–51]). In an industrial proteomics laboratory,

terabyte amounts of information (chiefly, LC–MS and

MS–MS spectra) are developed that far exceed the data

storage and processing caability of desktop computers for

which some of these programs were written. For opera-

tion at this scale, algorithms and data analysis software

need to be created to run on a scaleable computing clus-

ter. A disadvantage of this requirement is that modern lab-

oratory instruments are controlled by desktop computer

systems; data created and stored on many desktop systems

will need to be extracted and transferred to the scaleable

computing cluster for subsequent analysis. At a point

downstream (preferably the desk of the bioinformatician

conducting the unified data analysis), clinical and pheno-

typic data collected for a biological specimen needs to

converge with the mass spectrometric identification of

proteins. These diverse elements must be effectively

coordinated if an industrial-scale proteomics facility is to

serve a useful role in systematic biological investigations

(Fig. 2,Table 1).

Concluding remarksIndustrial-scale proteomic analysis offers the opportunity

to conduct systematic studies into cellular and organismal

protein biochemistry for the purposes of drug target dis-

covery and academic pursuits. However, potent challenges

in sample preparation, mass spectrometric instrumenta-

tion, and data analysis need to be identified and overcome

for this process to be successful in a wider range of both

academic and industrial settings. The specific nature of

these challenges is determined by the biological study

undertaken, the availability and type of instrumentation

used, and the computational resources available to the

investigator. The successful management of these scaling

questions will advance this systematic approach to discov-

ery in the biological sciences, particularly in the discovery

of targets for therapeutics and diagnostics.



AcknowledgementsWe would like to thank Ian McCaffery for critical reading of

the manuscript, and Aiqun Li and Bruno Domon for Fig. 3.

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