Prot Comp Linkage Maps

402

From the analysis of protein complexes to proteome-wide linkage maps Pierre Legrain*†, Jean-Luc Jestin# and Vincent Schächter*‡ Recent advances in genomics have led to the accumulation of an unprecedented amount of data about genes. Proteins, not genes, however, sustain function. The traditional approach to protein function analysis has been the design of smart genetic assays and powerful purification protocols to address very specific questions concerning cellular mechanisms. Lately, a number of proteome-wide functional strategies have emerged, giving rise to a new field in biology, proteomics, that addresses the biology of a cell as a whole. Addresses *Hybrigenics, 180 Avenue Daumesnil, Paris 75012, France † e-mail: [email protected] ‡ e-mail: [email protected] # Unité de Chimie Organique, Institut Pasteur, 25 rue du Dr Roux, Paris 75724 Cedex 15, France; e-mail: [email protected] Current Opinion in Biotechnology 2000, 11:402–407 0958-1669/00/$ — see front matter © 2000 Elsevier Science Ltd. All rights reserved. Abbreviations 2DE two-dimensional gel electrophoresis MS mass spectrometry

Introduction Several technologies to study specific cellular functions or processes have been around for many years, such as enzymatic assays, complex purification or subcellular localization. In most cases, the studies have focused on a small set of genes or proteins. The increasing amount of genomic data, however, has led to the availability of more new biological objects than could reasonably be studied using classical genetic or biochemical means. An initial approach was to screen for new essential genes, assuming that these genes would be more interesting than others. It soon became clear, however, that most genes could be deleted without obvious changes in phenotype and that it was necessary to study combinations of proteins or subtle phenotypes to understand cellular functioning. Geneticists had been studying mutant phenotypes and grouping genes to build pathways. New technologies were developed, such as the yeast two-hybrid or phage-display assays that allow the detection of protein–protein interactions in order to build protein interaction maps, or synthetic lethality screens that identify genes whose products are functionally related. Biochemists have purified proteins associated with an activity in order to characterize the components of biochemical complexes. Two-dimensional gel electrophoresis (2DE) was coupled to protein mass spectrometry (MS) analysis to characterize the components of a complex or even to identify exhaustively most components of an expressed proteome, yielding protein expression maps. More recently, bioinformaticists developed algorithms to group proteins according to their

sequences assuming that such clustering of proteins would lead to functional grouping (see below for discussion). It is expected that the combination of several of these techniques applied at a proteome scale will lead to completely different approaches for functional analysis, integrating enzymatic complexes and cascades (e.g. metabolic pathways or maps) in a cell architecture and leading to an integrated view of cell functioning. We present here various approaches to link proteins together in order to build functional networks.

Protein interaction maps Two-hybrid in yeast

Since its original description in 1989 [1], the yeast twohybrid assay has been used extensively to identify protein–protein interactions. Initially designed as an assay for the detection of an interaction between two known proteins, it was rapidly developed as a screening assay to find partners for a protein of interest [2]. There are currently many variations around the same principle (for reviews see [3–5]). In most cases, the goal has been to find partners for one or several proteins and to build hypothesis-driven experiments from the two-hybrid data. Recently, several groups have developed strategies that allow construction of functional networks based on protein interaction maps [6–8]. These approaches have attempted to solve some of the technical drawbacks inherent to the two-hybrid approach, namely false positives (proteins that interact non-specifically) and false negatives (protein–protein interactions that are not detected in a classical yeast two-hybrid approach), in order to improve the quality of the data obtained as well as to provide as complete a description as possible of the protein–protein interactions occurring within a cell. Indeed, yeast two-hybrid strategies have been used successfully to decipher both stable biochemical complexes [9,10] and metabolic pathways, such as cell-cycling or splicing [6,7,11•]. With the recent tremendous increase in genome sequence data (with still more to come) it becomes attractive to consider these protein interaction maps as a first step toward the analysis of protein function. Indeed, genome-wide approaches have been reported on T7 phage [12], yeast [7,11•,13•,14•], the human hepatitis C virus [15•] and Caenorhabditis elegans [16•]. How these various experimental approaches will compare to each other is still an open question. As a whole, however, they will provide the scientific community with efficient and accurate tools to approach protein function on a large-scale and in a systematic way. In addition, they will help pinpoint many more proteins as new potential targets for drugs, while protein–protein interactions themselves could become promising targets for drug design in their own right [17].

From the analysis of protein complexes to proteome-wide linkage maps Legrain, Jestin and Schächter

Other in vivo two-hybrid assays

During the past few years, several two-hybrid assays have been developed in organisms other than yeast. In order to build protein-linkage maps, a sufficient throughput at a cost-effective level must be achieved. In this respect, Escherichia coli appears potentially as an even more suitable host than yeast for protein–protein interactions screens. Indeed, a transcriptional activation assay in E. coli was published, very similar to the yeast assay, showing that a simple contact between a DNA-bound protein and the RNA polymerase mediated through two arbitrary polypeptides could elicit transcription [18]. Two other assays have been published based on the reconstitution of an enzyme, leading to a measurable enzymatic activity [19] or to the activation of a signal transduction pathway [20]. Although these technologies are not yet applicable to screening for protein–protein interactions, one of them has been used for screening variants in leucine-zipper libraries [21] by reconstituting an active DHFR enzyme made of two separate inactive polypeptides through a leucinezipper structure. Despite the absence of many post-translational modifications in bacteria, such systems might present many advantages for high-throughput screening compared to yeast: the protein complex is built in the cytoplasm, not on DNA, avoiding transcriptional autoactivation; also, screening assays might be more cost effective because of generaton time of bacteria as well as easier and inexpensive molecular biology on E. coli compared to yeast. Protein–protein interactions can also be screened in mammalian cells, allowing a more natural environment for many metazoan proteins [22]. This assay may or may not be amenable to large-scale screening. Phage display technologies

Phage display provides a physical link between a polypeptide and its coding gene. The polypeptide is expressed as a fusion with a phage coat protein on the surface of the phage particle, which contains the corresponding gene fusion. The phage-displayed polypeptide can be selected through interaction with a target using affinity chromatography and further characterized by amplification and sequencing of the corresponding gene. Although no protein–protein interaction map using phage display has been established so far, the technology might well have the necessary scaling potential: cDNA libraries have been successfully expressed on phage T7 [23], filamentous phage [24] and phage lambda [25], thereby providing a means to identify proteins interacting in vitro with given targets. An E. coli genomic library displayed on filamentous phage has also been constructed and tested in a model system [26].

Protein expression maps Proteome-wide protein identification

Protein expression maps aim to identify proteins localized in specific protein complexes, in organelles or in cells. A typical approach consists of the separation of the various

403

proteins of an extract by gel electrophoresis followed by mass spectrometric analysis of protein gel spots, providing precise identification of polypeptides by unique assignments with their corresponding DNA sequences through the use of sequence databases [27]. Recent optimizations of the various steps have provided one of the most powerful approaches in proteomics. First, accurate purification method(s) for proteins, protein complexes or organelles are required. These mainly include centrifugation in density gradients [28,29], exclusion chromatography [30], and affinity chromatography using, for example, peptide tags [31], antibodies (immunoprecipitation) [32,33•,34] or substrates [35]. A tandem affinity purification (TAP) involving a combination of two high-affinity tags linked to the protein of interest permits a very efficient purification in two steps from a crude extract [36•]. The technique has been suggested as a general method for protein complex purification under mild conditions after expression in a natural environment [36•]. It remains to be demonstrated that this technique is applicable to organisms other than yeast. Second, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) is typically combined with isoelectric focusing (IEF) to separate the various proteins according to their isoelectric point on a gel with an immobilized pH gradient to provide a 2DE technique, which is thus far the most commonly used multidimensional protein separation method used in proteomics [32,33•,35,37]. Recently, innovative sample preparations have made membrane-associated proteins amenable to 2DE analyses [38]. As this technique is becoming standardized and reproducible in different laboratories, annotated databases of 2DE images have been created for various proteomes [37,39]. Third, the separated protein spots on the gel can be excised and the proteins proteolytically digested in-gel. Peptides are subsequently eluted from the gel and analysed by MS. High throughput is achieved by automated matrix-assisted laser desorption/ionization (MALDI), providing a list of masses for the peptides. Matching this list against the list of calculated peptide masses from an appropriate protein sequence database characterizes the isolated protein. This method alone was found to be sufficient in several proteomic studies [28,33•]. Electrospray ionization (ESI) MS requires a preliminary peptide purification step and provides both the peptide mass list (ESI MS) and amino acid sequences of selected peptides on tandem mass spectra (ESI MS/MS): this allows unambiguous identification of a protein’s sequence by database searching [29–32,35]. Highthroughput methods have been designed to identify various post-translational modifications of proteins by MS [40•].

404

Protein technologies and commercial enzymes

Proteome-wide protein quantification: towards differential maps

Further proteome-wide characterization, which requires accurate large-scale quantification, allows the production of global maps of differentially expressed proteins. Radioactive labeling of metabolites yields protein extracts that can be analyzed quantitatively on 2D gels by scintillation counting [41]. Stable isotope metabolic labeling of distinct protein pools followed by MS analysis of proteins purified from 2D gels provides a further method for quantitative and differential analysis of protein expression [42]. An analogous strategy consists of direct MS analysis of purified proteins: cell extracts are treated with alkylating reagents which are tagged for affinity isolation and labeled by different stable isotopes for MS characterization of the distinct cell types or cell states studied [43••]. This strategy does not make use of 2DE technology and thereby avoids its main limitation, that is, its inability to detect and quantify rare proteins and the difficulty of comparing gels. Differential protein expression maps have been applied, for example, to the elucidatation of signal transduction pathways [42,44], the characterization of distinct cell types [45,46], the identification of virulence factors of pathogenic bacteria [47], parasite-encoded membrane-proteins [38], and proteins specifically associated with human diseases [48••], and the determination of the effects that environmental changes have on protein expression [43••,49].

Other protein linkage maps Transcriptome maps

The availability of completely sequenced genomes has permitted the production of microarrays of all the predicted genes of a particular organism. By performing hybridization experiments with probes prepared from RNAs of cells grown under varying conditions, it has been possible to produce genome-wide quantitative patterns of gene expression, and by inference maps of co-expressed proteins. Protein expression does not, however, always parallel mRNA expression [41]. In addition, different techniques used in gene expression profiling are far from being directly comparable to one another. Nevertheless, we believe that standardized and systematic studies on gene expression will provide rich foundations for further functional studies. Phenotypic mutant linkage maps

The availability of completely sequenced genomes also paves the way for systematic gene disruption experiments. A list of essential genes can be compiled [50•], and more importantly, systematic analysis of viable disrupted mutants allows the grouping of genes according to mutant phenotypes [51•,52••]. Such studies have been performed not only on small bacterial genomes and yeast, but also on higher eukaryotes with a collection of tagged mouse embryonic stem cells [53]. This should ultimately lead to databases of mutant phenotypes directly related to a given mutant genotype.

Prediction-based linkage maps

Whereas the accumulated knowledge on protein linkage maps has been derived so far mainly from direct exploitation of biochemical and genetic experiments, recent approaches attempt to bypass the collection of specific experimental data altogether by predicting functional links between proteins through computational means. These approaches are founded on various biological hypotheses (phylogeny, structure, etc.) and tested on different sets of experimental data (sequences, gene expression, etc.), this data being available in databases of various kinds, bibliographical included. Promising attempts to automatically extract protein–protein interaction information from scientific text, for example, by applying parsing techniques based on a restricted vocabulary to Medline abstracts [54•], need to be refined in accuracy and scaled-up to produce sizeable maps. Other works endeavor to identify functional links based on the ordering of related genes on genome sequences, following the notion that gene proximity is a result of selective pressure to associate genes that are coregulated and thus potential interacting partners [55,56]. The so-called ‘domain-fusion’ method [57•,58] also relies on sequence data to predict interactions; it is based on the idea that a configuration where a protein (the ‘composite protein’) contains two domains orthologous to two fulllength proteins of a given species (the ‘component proteins’) hints at the existence of an interaction between the component proteins. Although comparisons with biochemical data obtained independently validate each of these prediction schemes to a certain degree, all have theoretical limitations, and false negatives as well as false positives abound. Furthermore, the predicted functional links may correspond to indirect functional associations, such as involvement in the same pathway or complex, as well as to direct molecular interactions. Given the present state of the art, additional information or biological validation is thus required to reach conclusive evidence on both the existence and the nature of a given functional link. One way to reduce this uncertainty is to combine independent prediction methods, or better yet, methods based on different types of experimental data. Links between proteins showing similar phylogenetic profiles, correlated mRNA expression patterns, or which participate in the same pathways have been compounded with domainfusion predicted links, resulting in a higher confidence composite linkage map [59••].

Conclusions Proteomics aims to determine the nature and the quantity of proteins present in biological samples, to identify linkage between proteins and ultimately to understand the function of these proteins. The approaches described in this review — ranging from yeast twohybrid or phage-display assays for interaction map construction to the combined use of 2DE and MS for protein expression map building, to the faster but less

From the analysis of protein complexes to proteome-wide linkage maps Legrain, Jestin and Schächter

conclusive computational prediction techniques — evolve from different technological backgrounds, but all stem from an effort to scale up from ad-hoc, ‘local’ protein analysis to global, proteome-wide pictures.

405

Acknowledgements We thank D Strosberg, S Whiteside and J Wojcik for critical review of the manuscript. We are also grateful to A Danchin for many stimulating discussions.

References and recommended reading Such a change in scale — a prerequisite to the development of industrial applications in the fields of drug discovery and diagnostic tests — impacts both the nature and the organization of proteomics work. The production of proteome-wide linkage maps relies not only on high-throughput experimental technologies, but also on extensive bioinformatics support. For example, protein identification from 2DE experiments involves searching sequence databases, storing annotated gel images, identifying spots using sophisticated image analysis techniques, as well as computing and tabulating molecular weights from mass spectra. Efficient storage, manipulation and retrieval of experimental data, as well as the integration of technologyspecific algorithms in a seamless chain of data transformation running from raw experimental data to exploitable results, are thus essential enablers of the production process. As the larger proteomics projects demand resources seldom available to isolated academic or industrial laboratories, distribution of work and pooling of resources within mixed academic/industrial networks will become increasingly necessary. Such collaborative efforts will revolve around the sharing of experimental data and postanalysis results, as yet nonexistent common databases will integrate data produced by several groups using different or even totally unrelated techniques.

Papers of particular interest, published within the annual period of review, have been highlighted as:

• of special interest •• of outstanding interest 1.

Fields S, Song O: A novel genetic system to detect protein–protein interactions. Nature 1989, 340:245-246.

2.

Chien CT, Bartel PL, Sternglanz R, Fields S: The two-hybrid system: a method to identify and clone genes for proteins that interact with a protein of interest. Proc Natl Acad Sci USA 1991, 88:9578-9582.

3.

Frederickson RM: Macromolecular matchmaking: advances in two-hybrid and related technologies. Curr Opin Biotechnol 1998, 9:90-96.

4.

Drees BL: Progress and variations in two-hybrid and three-hybrid technologies. Curr Opin Chem Biol 1999, 3:64-70.

5.

Vidal M, Legrain P: Yeast forward and reverse ‘n’-hybrid systems. Nucleic Acids Res 1999, 27:919-929.

6.

Finley RL Jr, Brent R: Interaction mating reveals binary and ternary connections between Drosophila cell cycle regulators. Proc Natl Acad Sci USA 1994, 91:12980-12984.

7.

Fromont-Racine M, Rain JC, Legrain P: Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens. Nat Genet 1997, 16:277-282.

8.

Serebriiskii I, Khazak V, Golemis EA: A two-hybrid dual bait system to discriminate specificity of protein interactions. J Biol Chem 1999, 274:17080-17087.

9.

Flores A, Briand JF, Gadal O, Andrau JC, Rubbi L, Van Mullem V, Boschiero C, Goussot M, Marck C, Carles C et al.: A protein–protein interaction map of yeast RNA polymerase III. Proc Natl Acad Sci USA 1999, 96:7815-7820.

10. Mayes AE, Verdone L, Legrain P, Beggs JD: Characterization of Sm-like proteins in yeast and their association with U6 snRNA. EMBO J 1999, 18:4321-4331.

If these data are to be related meaningfully, the establishment of experimental standards for quality, of common computer representations for both experimental protocols and results, and of a set of corresponding benchmarks, are likely to become major issues within the proteomics community.

11. Fromont-Racine M, Mayes AE, Brunet-Simon A, Rain J-C, Colley A, • Dix I, Joly N, Beggs JD, Legrain P: Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins. Comp Funct Genomics 2000, 2:in press. Construction of a protein interaction map by screening the yeast proteome for all possible interacting domains with mRNA splicing factors. This led to the discovery of an unexpected functional link between nuclear mRNA splicing and cytoplasmic mRNA degradation, the existence of which was later confirmed.

Another essential enabler for proteomics research should be the development of Internet-based graphical interfaces for the visualization, exploration and analysis of linkage maps resulting from each type of technology [60]. As front-ends for databases following adequate standards of quality and presentation, these interfaces may provide not only a popular novel medium for the dissemination of scientific results, but also a mandatory complement to more traditional publishing.

12. Bartel PL, Roecklein JA, SenGupta D, Fields S: A protein linkage map of Escherichia coli bacteriophage T7. Nat Genet 1996, 12:72-77.

Finally, meaningful integration and useful presentation of linkage maps of the same biological nature obtained through diverse means is only the first step towards the fulfillment of the promise of proteomics. Beyond ‘single data-type’ representation, analysis and visualization tools, one can envision more ambitious frameworks aimed at the comprehensive study of ‘biological links’ resulting from several distinct data types. Such compound, generalized linkage maps should be fertile grounds for the discovery of more global insights into the molecular mechanisms of life.

13. Ito T, Tashiro K, Muta S, Ozawa R, Chiba T, Nishizawa M, Yamamoto K, • Kuhara S, Sakaki Y: Toward a protein–protein interaction map of the budding yeast: a comprehensive system to examine twohybrid interactions in all possible combinations between the yeast proteins. Proc Natl Acad Sci USA 2000, 97:1143-1147. Analysis of the yeast proteome for protein interactions in a two by two combination approach between annotated proteins (a matricial approach). This is a key reference on the technical advantages and limitations of this approach (see also [14•]). 14. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, • Lockshon D, Narayan V, Srinivasan M, Pochart P et al.: A-comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 2000, 403:623-627. Analysis of the yeast proteome for protein interactions in a two by two combination approach between annotated proteins (a matricial approach). This is a key reference on the technical advantages and limitations of this approach (see also [13•]). 15. Flajolet M, Rotondo G, Daviet L, Bergametti F, Inchauspé G, Tiollais P, • Transy C, Legrain P: A genomic approach of the hepatitis C virus generates a protein interaction map. Gene 2000, 242:369-379. Analysis of the Hepatitis C proteome for protein interactions by screening all potential interacting domains (a screening approach).

406

Protein technologies and commercial enzymes

16. Walhout AJ, Sordella R, Lu X, Hartley JL, Temple GF, Brasch MA, • Thierry-Mieg N, Vidal M: Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 2000, 287:116-122. A combination of matricial and screening approaches for protein interactions applied to a well-defined developmental pathway in C. elegans. 17.

Young K, Lin S, Sun L, Lee E, Modi M, Hellings S, Husbands M, Ozenberger B, Franco R: Identification of a calcium channel modulator using a high throughput yeast two-hybrid screen. Nat Biotechnol 1998, 16:946-950.

18. Dove SL, Joung JK, Hochschild A: Activation of prokaryotic transcription through arbitrary protein–protein contacts. Nature 1997, 386:627-630. 19. Pelletier JN, Campbell-Valois FX, Michnick SW: Oligomerization domain-directed reassembly of active dihydrofolate reductase from rationally designed fragments. Proc Natl Acad Sci USA 1998, 95:12141-12146. 20. Karimova G, Pidoux J, Ullmann A, Ladant D: A bacterial two-hybrid system based on a reconstituted signal transduction pathway. Proc Natl Acad Sci USA 1998, 95:5752-5756. 21. Pelletier JN, Arndt KM, Pluckthun A, Michnick SW: An in vivo libraryversus-library selection of optimized protein–protein interactions. Nat Biotechnol 1999, 17:683-690. 22. Rossi F, Charlton CA, Blau HM: Monitoring protein–protein interactions in intact eukaryotic cells by beta-galactosidase complementation. Proc Natl Acad Sci USA 1997, 94:8405-8410. 23. Zozulya S, Lioubin M, Hill RJ, Abram C, Gishizky ML: Mapping signal transduction pathways by phage display. Nat Biotechnol 1999, 17:1193-1198. 24. Jespers LS, Messens JH, De Keyser A, Eeckhout D, Van den Brande I, Gansemans YG, Lauwereys MJ, Vlasuk GP, Stanssens PE: Surface expression and ligand-based selection of cDNAs fused to filamentous phage gene VI. Bio/Technology 1995, 13:378-382. 25. Santini C, Brennan D, Mennuni C, Hoess RH, Nicosia A, Cortese R, Luzzago A: Efficient display of an HCV cDNA expression library as C-terminal fusion to the capsid protein D of bacteriophage lambda. J Mol Biol 1998, 282:125-135. 26. Palzkill T, Huang W, Weinstock GM: Mapping protein–ligand interactions using whole genome phage display libraries. Gene 1998, 221:79-83. 27.

Shevchenko A, Jensen ON, Podtelejnikov AV, Sagliocco F, Wilm M, Vorm O, Mortensen P, Boucherie H, Mann M: Linking genome and proteome by mass spectrometry: large-scale identification of yeast proteins from two dimensional gels. Proc Natl Acad Sci USA 1996, 93:14440-14445.

28. Wigge PA, Jensen ON, Holmes S, Soues S, Mann M, Kilmartin JV: Analysis of the Saccharomyces spindle pole by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. J Cell Biol 1998, 141:967-977. 29. Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR, Garvik BM, Yates JR III: Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol 1999, 17:676-682. 30. Grant PA, Schieltz D, Pray-Grant MG, Steger DJ, Reese JC, Yates JR III, Workman JL: A subset of TAF(II)s are integral components of the SAGA complex required for nucleosome acetylation and transcriptional stimulation. Cell 1998, 94:45-53. 31. Neubauer G, Gottschalk A, Fabrizio P, Seraphin B, Luhrmann R, Mann M: Identification of the proteins of the yeast U1 small nuclear ribonucleoprotein complex by mass spectrometry. Proc Natl Acad Sci USA 1997, 94:385-390. 32. Gygi SP, Han DK, Gingras AC, Sonenberg N, Aebersold R: Protein analysis by mass spectrometry and sequence database searching: tools for cancer research in the post-genomic era. Electrophoresis 1999, 20:310-319. 33. Houry WA, Frishman D, Eckerskorn C, Lottspeich F, Hartl FU: • Identification of in vivo substrates of the chaperonin GroEL. Nature 1999, 402:147-154. About 300 proteins of E. coli were defined as GroEL-binding proteins and observed on 2D gels. 52 of them were further identified by MALDI-TOF mass spectrometry, thereby suggesting a definition for the typical GroEL substrate. 34. Ogryzko VV, Kotani T, Zhang X, Schlitz RL, Howard T, Yang XJ, Howard BH, Qin J, Nakatani Y: Histone-like TAFs within the PCAF histone acetylase complex. Cell 1998, 94:35-44.

35. Neubauer G, King A, Rappsilber J, Calvio C, Watson M, Ajuh P, Sleeman J, Lamond A, Mann M: Mass spectrometry and ESTdatabase searching allows characterization of the multi-protein spliceosome complex. Nat Genet 1998, 20:46-50. 36. Rigaut G, Shevchenko A, Rutz B, Wilm M, Mann M, Seraphin B: • A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotechnol 1999, 17:1030-1032. The protein complex purification method tested in yeast consists of two steps of affinity chromatography and does not require protein overexpression, which is not possible for complexes of unknown composition and which might lead to non-physiological complexes. 37.

Celis JE, Gromov P: 2D protein electrophoresis: can it be perfected? Curr Opin Biotechnol 1999, 10:16-21.

38. Rabilloud T, Blisnick T, Heller M, Luche S, Aebersold R, Lunardi J, Braun-Breton C: Analysis of membrane proteins by twodimensional electrophoresis: comparison of the proteins extracted from normal or Plasmodium falciparum-infected erythrocyte ghosts. Electrophoresis 1999, 20:3603-3610. 39. Cordwell SJ, Nouwens AS, Verrills NM, McPherson JC, Hains PG, Van Dyk DD, Walsh BJ: The microbial proteome database — an automated laboratory catalogue for monitoring protein expression in bacteria. Electrophoresis 1999, 20:3580-3588. 40. Wilkins MR, Gasteiger E, Gooley AA, Herbert BR, Molloy MP, • Binz PA, Ou K, Sanchez JC, Bairoch A, Williams KL et al.: Highthroughput mass spectrometric discovery of protein posttranslational modifications. J Mol Biol 1999, 289:645-657. MALDI-TOF post source peptide fragmentation used with an appropriate software tool is shown to be sufficient for the identification of post-translational modifications and might be automated for proteome-wide studies. 41. Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 1999, 19:1720-1730. 42. Oda Y, Huang K, Cross FR, Cowburn D, Chait BT: Accurate quantitation of protein expression and site-specific phosphorylation. Proc Natl Acad Sci USA 1999, 96:6591-6596. 43. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R: •• Quantitative analysis of complex protein mixtures using isotopecoded affinity tags. Nat Biotechnol 1999, 17:994-999. A method for quantitative differential analysis of protein expression on a proteome-wide scale is described. It makes use of differentially labelled protein extracts analysed by liquid chromatography and ESI mass spectrometry and does not require 2D gel electrophoresis. 44. Godovac-Zimmermann J, Soskic V, Poznanovic S, Brianza F: Functional proteomics of signal transduction by membrane receptors. Electrophoresis 1999, 20:952-961. 45. Witzmann FA, Fultz CD, Grant RA, Wright LS, Kornguth SE, Siegel FL: Differential expression of cytosolic proteins in the rat kidney cortex and medulla: preliminary proteomics. Electrophoresis 1998, 19:2491-2497. 46. Page MJ, Amess B, Townsend RR, Parekh R, Herath A, Brusten L, Zvelebil MJ, Stein RC, Waterfield MD, Davies SC et al.: Proteomic definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties. Proc Natl Acad Sci USA 1999, 96:12589-12594. 47.

Jungblut PR, Schaible UE, Mollenkopf HJ, Zimny-Arndt U, Raupach B, Mattow J, Halada P, Lamer S, Hagens K, Kaufmann SH: Comparative proteome analysis of Mycobacterium tuberculosis and Mycobacterium bovis BCG strains: towards functional genomics of microbial pathogens. Mol Microbiol 1999, 33:1103-1117.

48. Jungblut PR, Zimny-Arndt U, Zeindl-Eberhart E, Stulik J, Koupilova K, •• Pleissner KP, Otto A, Muller EC, Sokolowska-Kohler W, Grabher G et al.: Proteomics in human disease: cancer, heart and infectious diseases. Electrophoresis 1999, 20:2100-2110. In this review, the substractive analysis of 2D gels is shown to be a powerful method to identify proteins specifically associated to various diseases. Applications to diagnostic tests are described. 49. Witzmann FA, Bauer MD, Fieno AM, Grant RA, Keough TW, Kornguth SE, Lacey MP, Siegel FL, Sun Y, Wright LS et al.: Proteomic analysis of simulated occupational jet fuel exposure in the lung. Electrophoresis 1999, 20:3659-3669.

From the analysis of protein complexes to proteome-wide linkage maps Legrain, Jestin and Schächter

50. Hutchison CA, Peterson SN, Gill SR, Cline RT, White O, Fraser CM, • Smith HO, Venter JC: Global transposon mutagenesis and a minimal Mycoplasma genome. Science 1999, 286:2165-2169. Global transposon mutagenesis was used to identify nonessential genes in Mycoplasma genitalium. 51. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, • Andre B, Bangham R, Benito R, Boeke JD, Bussey H et al.: Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 1999, 285:901-906. An extensive analysis of yeast deletion mutant strains. 52. Ross-Macdonald P, Coelho PS, Roemer T, Agarwal S, Kumar A, •• Jansen R, Cheung KH, Sheehan A, Symoniatis D, Umansky L et al.: Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 1999, 402:413-418. A transposon-tagging strategy for the genome-wide analysis in yeast of disruption phenotypes, gene expression and protein localization. 53. Zambrowicz BP, Friedrich GA, Buxton EC, Lilleberg SL, Person C, Sands AT: Disruption and sequence identification of 2,000 genes in mouse embryonic stem cells. Nature 1998, 392:608-611. 54. Blaschke C, Andrade M, Ouzounis C, Valencia A: Automatic • extraction of biological information from scientific text: proteinprotein interactions. Seventh International Conference on Intelligent Systems for Molecular Biology: 1999; Heidelberg. La Jolla, CA: ISMB; 1999:60-67. A fairly simple yet seemingly efficient approach to information extraction from Medline abstracts.

407

55. Dandekar T, Snel B, Huynen M, Bork P: Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci 1998, 23:324-328. 56. Overbeek R, Fonstein M, D’Souza M, Pusch GD, Maltsev N: The use of gene clusters to infer functional coupling. Proc Natl Acad Sci USA 1999, 96:2896-2901. 57. •

Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA: Protein interaction maps for complete genomes based on gene fusion events [see comments]. Nature 1999, 402:86-90. A careful study of the results of the ‘gene-fusion based’ prediction method for protein–protein interactions applied to three bacterial genomes. An interesting discussion on method scope and results interpretation. 58. Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D: Detecting protein function and protein–protein interactions from genome sequences. Science 1999, 285:751-753. 59. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D: •• A combined algorithm for genome-wide prediction of protein function. Nature 1999, 402:83-86. A first study on the combination of different computational techniques to predict functional links on a genome-wide basis. 60. Eilbeck K, Brass A, Paton N, Hodgman C: INTERACT: an objectoriented protein–protein interaction database. Seventh International Conference of Intelligent Systems for Molecular Biology: 1999; Heidelberg. La Jolla, CA: ISMB; 1999:87-94.