Proteomics Approaches in Drug Discovery - ACS Publications

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Proteomics Approaches in Drug Discovery Understanding the human genome is important, but most pharmaceuticals target proteins.

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© MARLENA ZUBER

mong the many unprecedented and fundamental changes that occurred in our society during the past century, perhaps the most notable was the increase in the average life expectancy from 45 to 75 years of age in the United States (1). New drugs and vaccines directly improved life expectancy but presented new challenges. As the population ages, debilitating diseases such as Alzheimer’s and Parkinson’s disease and cancer can reduce the quality of life for many people. At the same time, new diseases with fatal consequences are emerging, including AIDS and variant Creutzfeldt– Jakob disease, a human transmissible spongiform encephalopathy. The foremost challenge for drug discovery in the 21st century is to find new efficacious drugs to cure diseases and improve the quality of life. Addressing these complex challenges requires an indepth understanding of human biology. In the early 1980s, researchers proposed that sequencing the human genome would be the first step toward gaining a deeper under-

standing of human biology and diseases and provide new avenues for drug discovery. After 13 years of labor, researchers reported a working draft of the human genome (2– 4). But the effort to sequence the human genome was only the “tip of the iceberg.” The true complexity of cellular biology exists at the level of proteins, not genes. Furthermore, the raw genetic sequence cannot predict a protein’s function, localization, posttranslational modification, or expression level in different cells. In reality, most drugs target proteins. Therefore, the study of proteins and their functions might bridge the gap between drug discovery and human genomic information. Fortunately, in the 1990s, technologies to rapidly analyze proteins and study their functions were developed. The term “proteome”—the ensemble of proteins related to a genome—was coined (5). Techniques for proteomics originated with the coupling of 2-dimensional electrophoresis (2-DE) in gels to MS. Within seven years, proteomics had provided significant insight into biology and

Daniel Figeys MDS-Proteomics (Canada) A U G U S T 1 , 2 0 0 2 / A N A LY T I C A L C H E M I S T R Y

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tandem mass spectrometers in the 1990s. These instruments measure peptide masses and permit the generation of fragmentation patterns related to an individual peptide amino acid sequence. Although the proteins prepared by 2-DE are stuck in a gel—and thus, are not directly compatible with these instruments—researchers realized that proteins isolated by 2-DE can be digested in-gel by a proteolytic enzyme, and the resulting peptides can be rapidly analyzed by MALDI-TOF MS or ESIMS/MS (9–11). Furthermore, mass spectra can be used to search Profiling proteomics Profiling proteomics is the original type of proteomics and still protein and DNA sequence databases to identify the proteins. This combination of 2-DE, MS, and database searching was its most popular application. Basically, it consists of identifying the proteins present in a biological sample or the proteins that originally used to list the proteins in a sample. However, if this are differentially expressed between samples, such as diseased was the only application for profiling proteomics, we would not versus normal tissues. The result is a refined list of proteins that be talking about it anymore because diseases are all about changes are present in a cell type or tissue or a list of the proteins that in cellular processes. To identify the critical proteins in diseases are up- or down-regulated (expressed at higher or lower levels and to be relevant for drug discovery, profiling proteomics had to follow the changes in proteomes during the progression of than usual) between different cell states or tissues. 2-DE/MS. At first, profiling proteomics depended on 2-DE, diseases by quantitatively mapping the proteins that differ bewhich is a powerful technique that separates proteins in the first tween samples. Over the years, better software was developed to analyze 2-DE dimension according to their isoelectric point and in the second dimension by denaturing gel electrophoresis according to protein patterns, and differential approaches were developed to molecular weight. This combination of orthogonal separation focus only on proteins for which the expression levels changed from sample to sample. Compared with whole-proteome methtechniques can separate up to 10,000 proteins (6, 7 ). 2-DE has been around for more than 25 years, so one might ods, these differential approaches provide smaller lists of prowonder why it has recently attracted such attention (8)! Be- teins, and when combined with other approaches, those lists can cause it is now feasible to systematically identify the proteins on guide the selection of diagnostic markers and potential targets a 2-D gel, thanks to the development of MALDI time-of-flight for drug discovery. Le Naour et al. recently illustrated the power of profiling (TOF) mass spectrometers and electrospray ionization (ESI) proteomics when they used it to discover breast carcinoma proteins that elicit a huState 1 moral response—that is, an immune response in the blood, mediated by antibodies (12). They assumed that proteins Extract Proteolytic specific to cancer cells are secreted into the proteins digestion bloodstream and act as antigens; therefore, antibodies against specific proteins in breast cancer might be present in the patients’ sera. State 2 Peptide mixture The researchers cultured a breast cancer cell line to generate sufficient proteins for 2-DE separation. A series of 2-DE gels were Protein mixture electroblotted to membranes and probed with individual serum extracted from cancer patients. An antihuman immunoglobulin G was then used as a secondary antiMass spectrometer Protein A body to highlight the protein spots that indicated that human antibodies were presProtein B ent in the sera. The proteins that were Liquid chromatograph/ Protein C markedly different in normal versus cancerautosampler ous patients were analyzed by MS. In par• ticular, the researchers discovered a protein • Nano HPLC/MS/MS called R S/DJ-1, which was detected at high Protein N levels in the sera of 37% newly diagnosed breast cancer patients. Thus, this elegant FIGURE 1. Profiling proteomics: “Gel-free” approach to protein identification. experiment used profiling proteomics to The proteins isolated from two different cell types (states 1 and 2) are extracted and digested. The resultrapidly analyze breast cancer markers. ing peptides are analyzed by LC/ESI-MS/MS, and the proteins contained in each cell type are identified. aided in drug discovery. Furthermore, interest in proteomics was evident in the literature: Publications about proteomics increased from merely a handful of articles in 1995 to over 1000 in 2001. The discipline is currently divided into three distinct classes: profiling, functional, and structural proteomics. Here, we introduce the different classes of proteomics and how they are becoming integral to drug discovery.

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(b) Generic approach (a) ICAT for cysteine-containing peptides Gel-free approaches. Although 2-DE is a powbased on 18O/16O water erful separation technique, its limitations must be O understood. In particular, 2-DE/MS does not HN NH X X X O O X O offer a sufficient dynamic range for analyzing lowO N O N X H S H X X X abundance proteins. This was demonstrated by Garrels et al. and Gygi et al., who performed a sysCell state 1 Cell state 2 Cell state 1 Cell state 2 tematic MS analysis of yeast proteins separated by 2-DE and observed that only mid- to high-abundance proteins could be analyzed (13, 14). (Fey and Larsen published arguments that counter Gygi and Garrels [15].) In addition, 2-DE is tedious to Label with D0 Label with D8 Digest with Digest with perform and has difficulty dealing with hydrophotrypsin in the trypsin in the 16 bic and basic proteins. presence of O presence of 18O Combine and digest water water Substitutes that address such limitations are bewith trypsin coming increasingly attractive for profiling proteomics, especially methods that totally bypass 2-DE Combine and analyze Purify label peptide on and analyze complex protein mixtures without gels by MS avidin column (Figure 1). This is possible because of improvements in on-line peptide separation, the dynamic range of mass spectrometers, and software for proAnalysis by MS tein identification. In the “gel-free approach”, a complex mixture of proteins is extracted from the FIGURE 2. Approaches to determine the relative changes in protein expression. biological material of interest and digested in so(a) ICAT methodology, which is based on the differential labeling of cysteine-containing lution to produce a complex mixture of peptides. peptides and analysis by MS. X stands for either hydrogen (D0 label) or deuterium (D8 label). (b) 18O/16O water methodology based on the differential labeling of all the peptides and The mixture of peptides is then separated on-line analysis by MS. either by 1-D or 2-D HPLC and ESI-MS. The mass spectrometer is continuously triggered by the eluting peptides to generate MS/MS spectra for the individual tle (i.e., less than 2-fold), yet they must be reproducibly meapeptides. These spectra can then be searched against genomic sured across multiple samples. Another challenge in gel-free analysis is the relative quantitaand proteomic databases to identify the various proteins extracttion of peptides. The traditional approach of staining a gel does ed from the cell. Is this work worth the effort? Yes. This approach has already not apply; instead, quantitation relies on the MS signal. In predemonstrated that low-abundance proteins can be discovered vious MS studies, researchers have quantified analytes by estab(16). Furthermore, characteristic hydrophilic peptides can be lishing response curves for particular analytes, but such curves found in proteins that have a preponderance of hydrophobic are difficult to obtain for a constantly changing set of analytes, amino acids. Finally, gel-free profiling proteomics can be per- such as peptides obtained from a proteolytic digestion of comformed on smaller amounts of material—a smaller mass of can- plex proteins. Even two experiments run consecutively are difcerous cells, for example—which could open the door to pro- ficult to compare in terms of peptide signal intensity. Fortunately, quantitative methods based on isotope tagging teomic discovery in earlier stages of disease. Even so, the gel-free approach has its own limitations. First, of peptides have recently been developed. These methods can the protein extract can be extremely complex in terms of the be used on gel-free samples, and they allow direct comparison number and concentration of proteins. Moreover, the enzymat- of the changes in expression levels between two proteomes in a ic digestion of the gel-free sample before MS analysis increases single experiment. Various approaches have been recently prothe complexity of the protein extract. For example, upon diges- posed—in particular, the isotope-coded affinity tag (ICAT) by tion, a mixture of 1000 proteins can easily yield a mixture with Aebersold et al. (17 ), the N-terminal labeling of peptides by tens of thousands of peptides. Currently, the combined resolving James et al. (18), and using 18O/16O water by Stewart et al. and power of 1-D or 2-D HPLC and MS does not surpass the sep- Mirgorodskaya et al. (19, 20). aration power of 2-DE. Furthermore, unambiguous identificaThe ICAT method has been by far the most popular, maintion of proteins is not always feasible because fragmentation ly because kits that provide all the chemicals for protein labelpatterns in MS/MS spectra can only be obtained for a fraction ing are commercially available. Briefly, the approach involves laof the peptides present in the gel-free sample. beling cysteine-containing peptides from different samples with Another key challenge in proteomics is the quantitation of light versus heavy forms of a reactive chemical, which differ by changes in protein expression between normal and diseased tis- 8 amu (17, 21, 22) (Figure 2a). The ICAT reagent consists of a sues. It is not unusual to see 10-fold changes in protein expres- biotin group followed by a linker and is terminated with a cysteinesion in such cases. More often, though, the changes can be sub- reactive group. Because the only difference between the light A U G U S T 1 , 2 0 0 2 / A N A LY T I C A L C H E M I S T R Y

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Known protein A GAL4 DNA-binding domain DNA GAL1-lacZ gene

DNA-binding site

Off β-galactosidase activity

Protein B X

Y

GAL4 activation domain GAL1-lacZ gene

On β-galactosidase activity

formed with a concentration of water up to 55 M, greatly favoring the reaction kinetics. Regardless of the technique, profiling proteomics only provides a list of proteins that are differentially expressed. This approach cannot indicate whether these proteins are involved in cellular changes or are merely side effects of those changes. For example, protein expression is often upregulated in cancerous cells, yet some of these increases simply indicate that the cells are rapidly growing and dividing and are not part of the cause.

Functional proteomics To understand different cellular processes, one must understand how proteins function Schematic of the classical two-hybrid approach using -galactosidase (GAL). Protein A is fused to the individually and in pathways. The function GAL4 DNA-binding domain. Protein B is attached to the GAL4 activation domain. Expression of -galactosidase is activated only when both hybrids are expressed in the same cell and proteins A and B interof a protein can be defined on the basis of act. The presence of -galactosidase is assayed either by colony color using 5-bromo-4-chloro-3-indolyl its interactions, whereas pathways are cas-D-galactoside or by enzyme activity using chlorophenol red -D-galactopyranoside. cades of specific protein interactions that are necessary to activate distinct cellular funcand heavy tags is the presence of hydrogen or deuterium, the tions. Functional proteomics attempts to define a protein’s role mass spectrometer responds similarly to both tags, yet there is on the basis of the presence of specific functional groups or ina slight spacing of the m/z peaks on the spectrum. Because the volvement in protein–ligand interactions, protein complexes, and peptides are identical in sequence yet do not completely quasi- novel pathways. Mapping the complete set of protein interactions in humans co-elute, their relative abundances can be determined from the could be a challenge. The human genome is estimated to conratio of their peak intensities. In a typical ICAT experiment, one lysate sample is tagged tain 30,000–60,000 genes, producing perhaps millions of prowith the light form of the reagent, and a second sample is tagged teins if posttranslational modifications and mutations are inwith the heavy form. Then, the two lysates are combined and cluded. Experience in mapping protein interactions suggests enzymatically cleaved to generate peptide fragments. The cys- that each protein participates in an average of 5–10 interacteine-containing peptides are purified using a monomeric avidin tions. In humans, that implies millions of possible interactions. Fortunately, advances in molecular biology have created gecolumn, which binds to the biotin in the tag. The purified peptides are separated on a nanoflow HPLC system, which is on- netically engineered systems capable of signaling the occurrence line with an ESI mass spectrometer. Finally, the automated gen- of interactions either directly or through an affinity-based proeration of MS/MS fragmentation patterns of the peptides and tein purification approach. In particular, the two-hybrid system database searching identify the peptide sequence and its protein and protein affinity/immunoprecipitation with MS form the basic approaches to large-scale functional proteomics (23, 24). provenance, and the relative quantitation is determined. Two-hybrid approach. The two-hybrid approach to functionMore pairs of chemicals will soon become available for the relative quantitation of proteomes. This is important because al proteomics is an invaluable technology for studying binary the current methods may not accurately measure the expression protein–protein interactions (23, 25). It answers the question, levels of all proteins. First, the expression profile of a proteome “Is protein A binding to protein B?”, by using a reporter gene is spread over a few orders of magnitude, which may not coin- to indicate when an interaction occurs between two chimeric, cide with the dynamic range of a given chemical tagging ap- or hybrid, proteins (Figure 3). The method, which is performed proach. In addition, if only minute amounts of some proteins in yeast, originated when researchers noticed that transcription are present, a method might fail to label the peptides because factors include a DNA-binding domain and a transcription acof unfavorable kinetics. This is not an issue when large amounts tivation domain, both of which are fully functional on their of starting materials, such as yeast, E. coli, or blood, can be ob- own. These domains are normally close together, but even if tained; but performing ICAT, for example, on scarce samples they are encoded in separate genetic constructs, a gene can still such as cancerous cells from biopsies can be a problem. Fortu- be activated if the domains are brought into close proximity. In these experiments, one chimeric protein is created by exnately, techniques such as 18O/16O water labeling can be performed with a large excess of reagent, which allows minute pressing the first protein of interest—called the “bait”, or proamounts of proteins to be quantified in the gel-free approach tein A—and fusing it to the DNA-binding domain of a tran(19, 20) (Figure 2b). Labeling with18O/16O water can be per- scription factor. This transcription factor lacks the transcription FIGURE 3. Functional proteomics: Yeast two-hybrid approach.

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activation domain, which is expressed separately as a fusion with cannot be mimicked by the two-hybrid approach. Ultimately, the the second protein of interest—often called the “prey”, or pro- best place to study the interactions between human proteins is tein B. Only when both fusion proteins are expressed and have in human cells. Protein tagging and immunopurification. Studying prointeracted are all the elements in place to turn on the reporter gene. Thus, the reporter identifies cells in which proteins A and tein–protein interactions directly in human cells has tremendous advantages: The proteins are properly folded and regulated, their B interact. The DNA coding of the two fusion proteins can be done in localizations are appropriate, and the correct posttranslational different ways. In the classical high-throughput approach, plas- modifications have been made. More importantly, deciphering mids encode the two fusion proteins (26). The first hybrid is con- protein complexes, pathways, and posttranslational modifications provides invaluable information for understructed using the DNA-binding domain standing cellular functions and for target– of the yeast’s transcriptional activator prodrug validation. Fortunately, the combinatein (GAL4) coupled to a known protein. tion of molecular biology, protein tagging, The second hybrid consists of the GAL4 immunopurification, and MS has made activation domain coupled to a library of possible the high-throughput analyses of yeast genomic fragments. The yeast is then human protein complexes formed in vivo simultaneously transformed with the (24) (Figure 4). known protein hybrid and an element Briefly, in this approach, the correfrom the hybrid library. The yeast that insponding full-length complementary DNA corporates both hybrids will grow on a (cDNA) of a gene is cloned into a transhistine- and leucine-depleted medium. fection vector. The vector is pre-encoded Because the medium includes 5-bromoto add an epitope tag to either the N- or 4-chloro-3-indolyl -D-galactoside, the C-terminus of the protein. This vector is yeast colonies in which the proteins interthen added to a culture of human cells in act will turn blue, indicating the activaThe two-hybrid approach the presence of an agent to facilitate the tion of -galactosidase. In this way, a yeast transfection. The DNA is rapidly transgenomic library can be rapidly screened ferred into the cells, and the cells express for interactions with a known protein. the tagged protein of interest, which One variation is to express the protein performs its normal interactions and is A fusion and the protein B fusion in two to functional proteomics properly localized. The cells are then coldifferent yeast strains, which must be lected and lysed, and the recovered sumated to each other to determine if the pernatant is clarified and subjected to improteins interact. This approach can remunopurification using an immobilized duce the number of transformations needis invaluable for studying antibody to capture the epitope tag. This ed when multiple bait proteins are screened step purifies and concentrates the tagged against multiple prey proteins. For examprotein and all the proteins that interact ple, if 10 bait and 5 prey proteins are used, with it in vivo. The purified protein frac50 transformations are needed to cover binary protein–protein tion is then separated by 1-DE or 2-DE all possible combinations in the traditionand analyzed by MS to identify the interal two-hybrid assay, but only 15 transforacting proteins. mations—10 for the bait plasmids and 5 A key issue with applying this approach for the prey plasmids—are needed for the interactions. to humans is obtaining the target genes’ mating assay. coding material. Encoded genetic mateLarge-scale two-hybrid studies are posrial can be carefully accessed at the messible because the approach can be automated. In particular, a library of proteins attached to the acti- senger RNA (mRNA) level and reverse-transcribed into cDNA. vation domain can be screened in high throughput against a Fortunately, collections of human cDNA, which represent a large library of proteins attached to the DNA-binding domain. The portion of the expressed genetic material, have been amassed. two-hybrid method is not just for yeast proteins; it can be ap- De novo cloning either directly from mRNA or by using cDNA plied to discover the interactions between human proteins, al- pools is also standard practice. The systematic application of this approach to high-throughput beit in the yeast environment. On the negative side, the two-hybrid method can only detect studies provides a collection of interactions, complexes, and binary interactions. Protein interactions in cells are more intri- pathways that are useful for discovering novel protein targets. cate because proteins are involved in multiprotein complexes. Recently, Ho et al. demonstrated the power of this approach in Furthermore, human proteins are regulated by a series of post- yeast (27 ). Their technique is readily applicable to humans. translational modifications and have specific localizations that Gavin et al. also presented the high-throughput mapping of inA U G U S T 1 , 2 0 0 2 / A N A LY T I C A L C H E M I S T R Y

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has been engineered to include a tag, which is not supposed to interfere with the drug’s normal interactions or localization. The cells are then lysed, and the drug and the molecules with which it interacts are affinity-purified using the tag. After proper rinsing, the interacting proteins can be digested and identified by MS. The drug–protein interactions truly occur inside the cell; therefore, the proper localization, normal protein concentrations, and appropriate cellular processes are in place. The disadvantage of this approach is that the tag might, in reality, disturb the drug’s localization or interactions. Although chemiproteomics is a promising new technology for discovering drug targets, technological improvements in drug immobilization and tagging are still needed. Nevertheless, chemiproteomics could become a high-throughput approach for rapidly screening drug libraries against different cells to discover protein–drug interactions. For example, high throughput could be achieved for the in vitro approach by immobilizing drug libraries or for the in vivo approach by tagging a drug library. Phosphoproteomics. Protein–protein interactions in cells do not occur randomly. Regulatory mechanisms activate proteins and allow interactions to occur at appropriate times. Such regulation might include the posttranslational modification of a protein, the increased expression of a protein involved in a pathway, or the degradation of a protein. Posttranslational modifications are important regulatory elements of protein–protein interactions. In particular, the phosphorylation of serine, threonine, and tyrosine are known to be involved in protein regulations. Simplistically, phosphorylation of proteins is comparable with switching the function of a protein on and off or modulating Extracellular Extracellular protein activity through increasExtracellular ing phosphorylation. The study Cytoplasm Cytoplasm Extracellular of protein phosphorylation is imCell Cell Cytoplasm Extracellular culture transfection Vector portant in pharmaceutical research Cytoplasm because diseases are often due to Nucleus Nucleus Cytoplasm modifications that affect the phosNucleus phorylation pattern of proteins Nucleus and, therefore, their interactions. Nucleus Protein MS-based techniques have been identified developed and reviewed for the low-throughput analysis of proHarvesting/lysis Bait tein phosphorylation (31–33). Until recently, the large-scale application of these approaches Protein I was limited by the sensitivity and Immunopurification, Gel electrophoresis denaturation throughput that could be achieved and protein identification with these methods. However, by MS phosphoproteomics—a new subfield for the large-scale mapping Protein II of protein phosphorylation— sprouted from recent developments in the field of phosphoryFIGURE 4. Functional proteomics: Immunopurification of protein complexes. lation mapping by MS. Schematic of the over-expression of a tag protein for the in vivo capture of protein complexes. The transfected cells Although the selective purifiare harvested and lysed, and the protein complex of interest is extracted using the tag on the bait protein and an immobilized antitag antibody. cation of phosphoproteins and tag

tag

teractions in yeast, although their method is not directly applicable to humans (28). Chemiproteomics. Proteins also interact with a range of other molecules, such as drugs, lipids, and other small molecules. Chemiproteomics studies the interaction between small molecules and proteins, particularly drugs and proteins. Instead of using proteins as bait, chemiproteomics uses small molecules to “fish” for interacting proteins (29, 30). Drug companies are particularly eager to discover drug– protein interactions because the majority of drugs act directly on proteins. So, finding the proteins that bind to a drug might aid in the discovery of new protein targets that can prolong the intellectual protection of current drugs or might help elucidate the side effects of a drug before it is released on the market. Chemiproteomics can be performed in vitro and in vivo. In the in vitro approach, the compound of interest is immobilized on beads or flat surfaces and used to probe a cell lysate for interacting proteins. After appropriate rinsing, the extracted proteins are digested and identified by MS. The advantage of this approach is that it can be used with cell cultures, tissues, and bodily fluids. One disadvantage is that the proteins from the cell are combined in vitro, increasing the risk of degradation and false interactions. Another drawback is that the concentration of proteins decreases when cells are lysed. Thus, the drug– protein interactions that were observed in vivo may not occur in vitro, depending on the on-rate (rate of ligand binding to the protein) and the incubation time. In the in vivo approach, cells are incubated with a drug that

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References

phosphopeptides has been the limiting factor of high-throughput phosphoproteomic mapping, new approaches have been developed for the selective purification of phosphorylated peptides on the basis of their chemical derivatization (34–36). Zhou et al. used a six-step chemical derivatization approach to purify and analyze phosphorylated peptides by MS (34). Oda et al. proposed a method that involves the chemical replacement of phosphorylated serine and threonine by a biotin moiety, which can then be used for the selective enrichment of the derivatized peptides (35). Goshe et al. reported a phosphoprotein isotopecoded affinity tag based on the -elimination applied to serine and threonine followed by biotinylation to introduce an affinity purification tag (36). These approaches still achieve limited sensitivity but clearly demonstrate that phosphoproteomic studies might be feasible. Ficarro et al. recently reported a breakthrough: They developed a novel MS approach that rapidly and globally analyzes the phosphorylation “switchboard” on a proteomic scale (37 ). They combined chemical derivatization with affinity purification of phosphorylated peptides, followed by automated MS identification and mapping of the peptides. Using yeast, they demonstrated that 383 phosphorylation sites could be rapidly mapped. Furthermore, they achieved a sufficient level of detection to identify rare protein phosphorylations, such as tyrosine phosphorylation in yeast and the phosphorylation of low-abundance proteins.

(19)

The new frontier

(20)

Structural proteomics is one of the less-developed areas of proteomics. Typically, it attempts to rapidly determine the tertiary structures of proteins, mainly using X-ray crystallography and the prediction of protein structures by computational biology. Various projects are currently under way for large-scale X-ray crystallography of proteins and domains, either by a large-scale crystallization effort (38, 39) or by systematic elucidation of domains. Researchers have come a long way since the early days of proteomics by 2-DE. Looking ahead, we can expect profiling proteomics to play an important role in the discovery of disease markers and provide lists of potential drug targets. Similarly, functional proteomics has a broad applicability across the drugdiscovery pipeline, from target prioritization to extending or recycling drugs. Finally, structural proteomics is uncovering the 3-D structures of proteins, which will have an impact on drug designs and the modeling of drug docking (ligand binding) and provide a framework for the structural modeling of novel proteins. I would like to acknowledge Rachel Figeys for reviewing this manuscript. Daniel Figeys is the vice president of analytical sciences at MDSProteomics. His research interests include industrialization of proteomics, new technologies, and applications of proteomics to the drug discovery process. Address correspondence to Figeys at 251 Attwell Dr., Toronto, Ontario M9W 7H4, Canada or [email protected].

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

(21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39)

U. S. Government. National Vital Statistics Report 1999, 47. Human Genome News 2000, 11 (1–2), 4. International Human Genome Sequencing Consortium. Nature 2001, 409, 860–921. Venter, J. C.; et al. Science 2001, 291, 1304–1351. Wilkins, M. R.; et al. Biotechnology 1996, 14, 61–65. Gorg, A.; Postel, W.; Gunther, S. Electrophoresis 1988, 9, 531–546. Klose, J.; Kobalz, U. Electrophoresis 1995, 16, 1034–1059. O’Farrell, P. H. J. Biol. Chem. 1975, 250, 4007–4021. Wilm, M.; et al. Nature 1996, 379, 466–469. Henzel, W. J.; Billeci, T. M.; Stults, J. T.; Wong, S.C.; Grimley, C.; Watanabe, C. Proc. Natl. Acad. Sci. U.S.A. 1993, 90, 5011–5015. Rosenfeld, J.; Capdevielle, J.; Guillemot, J. C.; Ferrara, P. Anal. Biochem. 1992, 203, 173–179. Le Naour, F.; et al. Clinical Cancer Research 2001, 7, 3328–3335. Gygi, S. P.; Corthals, G. L.; Zhang, Y.; Rochon, Y.; Aebersold, R. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 9390–9395. Garrels, J. I.; et al. Electrophoresis 1997, 18, 1347–60. Fey, S. J.; Larsen, P. M. Curr. Opin. Chem. Biol. 2001, 5, 26–33. Washburn, M. P.; Wolters, D.; Yates, J. R. Nat. Biotechnol. 2001, 19, 242–247. Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Nat. Biotechnol. 1999, 17, 994–999. Munchbach, M.; Quadroni, M.; Miotto, G.; James, P. Anal. Chem. 2000, 72, 4047–4057. Stewart, I. I.; Thomson, T.; Figeys, D. Rapid Commun. Mass Spectrom. 2001, 15, 2456–2465. Mirgorodskaya, O. A.; Kozmgin, Y. P.; Titov, M. I.; Korner, R.; Sonksen, C. P.; Roepstorff, P. Rapid Commun. Mass Spectrom. 2000, 14, 1226–1232. Aebersold, R.; Rist, B.; Gygi, S. P. Ann. N. Y. Acad. Sci. 2000, 919, 33–47. Smolka, M. B.; Zhou, H.; Purkayastha, S.; Aebersold, R. Anal. Biochem. 2001, 297, 25–31. Fields, S.; Song, O. Nature 1989, 340, 245–246. Figeys, D.; McBroom, L. D.; Moran, M. F. Methods 2001, 24, 230–239. Fromont, R.-M.; Rain, J. C.; Legrain, P. Nat. Genet. 1997, 16, 277–282. Chien, C.-T.; Bartel, P. L.; Sternglanz, R.; Fields, S. Proc. Natl. Acad. Sci. U.S.A. 1991, 88, 9578–9582. Ho, Y.; et al. Nature 2002, 415, 180–183. Gavin, A.-C.; et al. Nature 2002, 415, 141–147. Henion, J.; Li, Y. T.; Hsieh, Y. L.; Ganem, B. Ther. Drug Monit. 1993, 15, 563–569. Sin, N.; Meng, L.; Auth, H.; Crews, C. M. Bioorg. Med. Chem. 1998, 6, 1209–1217. Conrads, T. P.; Issaq, H. J.; Veenstra, T. D. Biochem. Biophys. Res. Commun. 2002, 290, 885–890. McLachlin, D. T.; Chait, B. T. Curr. Opin. Chem. Biol. 2001, 5, 591–602. MacCoss, M. J.; Yates, J. R. Curr. Opin. Clin. Nutr. Metab. Care 2001, 4, 369–375. Zhou, H.; Watts, J. D.; Aebersold, R. Nat. Biotechnol. 2001, 19, 375–378. Oda, Y.; Nagasu, T.; Chait, B. T. Nat. Biotechnol. 2001, 19, 379–382. Goshe, M. B.; Veenstra, T. D.; Panisko, E. A.; Conrads, T. P.; Angell, N. H.; Smith , R. D. Anal. Chem. 2002, 74, 607–616. Ficarro, S. B.; et al. Nat. Biotechnol. 2002, 20, 301–305. Stewart, L.; Clark, R.; Behnke, C. Drug Discov. Today 2002, 7, 187–196. Jhoti, H. Trends Biotechnol. 2001, 19, S67–S71.

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