26 Barash, I. et al. (1996) Nucleic Acids Res. 24, 602–61027 Ebert, K. M. et al. (1994) Biotechnology 12, 699–70228 VanCott, K. E. et al. (1997) Transgenic Res. 6, 203–21229 Whitelaw, C. B. A. et al. (1991) Transgenic Res. 1, 3–1330 Korhonen, V. et al. (1997) Eur. J. Biochem. 245, 482–48931 Wright, G. et al. (1991) Biotechnology 9, 830–83432 Clark, A. J. et al. (1994) Reprod. Fertil. Dev. 6, 589–59833 McKnight, R. A., Wall, R. J. and Hennighausen, L. (1995) Transgenic

Res. 4, 39–4134 Webster, J., Donofrio, G., Wallace, R., Clark, A. J. and

Whitelaw, C. B. A. (1997) Gene 193, 239–24335 Dobie, K. W. et al. (1996) Proc. Natl. Acad. Sci. U. S. A. 96, 6659–666436 Ellis, J. et al. (1996) EMBO J. 15, 562–56837 Jones, B., Monks, B. R., Liebhaber, S. A. and Cooke, N. E. (1995)

Mol. Cell. Biol. 15, 7010–702138 McKnight, R. A., Spencer, M., Wall, R. J. and Hennighausen, L.

(1996) Mol. Reprod. Dev. 44, 179–18439 Peterson, K. R., Clegg, C. H., Li, Q. and Stomatoyannopoulos, G.

(1997) Trends Genet. 13, 61–6640 Peterson, K. R., Navas, P. A. and Stomatoyannopoulos, G. (1998)

Hum. Mol. Genet. 7, 2079–208841 Fujiwara, Y. et al. (1997) Mol. Reprod. Dev. 47, 157–16342 Rouy, D. et al. (1998) J. Biol.Chem. 273, 1247–125143 Brem, G. et al. (1996) Mol. Reprod. Dev. 44, 56–6244 Langford, G. A. et al. (1996) Transplant. Proc. 28, 862–86345 Vos, J. M. H. (1998) Curr. Opin. Genet. Dev. 8, 351–35946 Telenius, H. et al. (1998) Chromosome Res. 6, 1–547 Cibelli, J. M. et al. (1998) Nat. Biotechnol. 16, 642–64648 Piedrahita, J. A. et al. (1998) Biol. Reprod. 58, 1321–132949 Xu, K., Shi, Z. M., Veeck, L. L., Hughes, M. R. and Rosenwaks, Z.

(1999) J. Am. Med. Assoc. 281, 1701–170650 Bowen, R. A. et al. (1994) Biol. Reprod. 50, 664–66851 Hyttinen, J. M. et al. (1994) Biotechnology 12, 606–60852 Duby, R., Damiani, P., Looney, C. R., Fissore, R. A. and Robl, J. M.

(1996) Theriogenology 45, 121–13053 Taneja, M. et al. (1998) Biol. Reprod. 58 (Suppl.), 475

54 Taneja, M. and Yang, X. (1998) IETS Newslett. 16, 10–1255 Willadsen, S. M. (1986) Nature 320, 63–6556 Wells, D. N., Misica, P. M., Day, A. M. and Tervit, H. R. (1997)

Biol. Reprod. 57, 385–39357 Prather, R. S. et al. (1987) Biol. Reprod. 37, 859–86658 Sims, M. and First, N. L. (1993) Proc. Natl. Acad. Sci. U. S. A. 90,

6143–614759 Yong, Z., Jianchen, W., Jufen, Q. and Zhiming, H. (1991)

Theriogenology 35, 29960 Baguisi, A. et al. (1999) Nat. Biotechnol. 17, 456–46261 Prather, R. S., Simms, M. M. and First, N. L. (1989) Biol. Reprod.

41, 414–41862 Loi, P. et al. (1997) Theriogenology 48, 1–1063 Bondioli, K., Westhusin, M. E. and Looney, C. R. (1990)

Theriogenology 33, 165–17264 Kato, Y. et al. (1998) Science 282, 2095–209865 Campbell, K. H., McWhir, J., Ritchie, W. A. and Wilmut, I. (1996)

Nature 380, 64–6666 Wilmut, I., Schnieke, A. E., McWhir, J., Kind, A. J. and

Campbell, K. H. S. (1997) Nature 385, 810–81367 Schnieke, A. E. et al. (1997) Science 278, 2130–213368 Cibelli, J. B. et al. (1998) Science 280, 1256–125869 Willadsen, S. M. et al. (1991) Theriogenology 35, 161–17070 Krimpenfort, P. et al. (1991) Biotechnology 9, 844–84771 Archer, J. S., Kennan, W. S., Gould, M. N. and Bremel, R. D.

(1994) Proc. Natl. Acad. Sci. U. S. A. 91, 6840–684472 Velander, W. H. et al. (1992) Proc. Natl. Acad. Sci. U. S. A. 89,

12003–1200773 Massoud, M. et al. (1996) Reprod. Nutr. Dev. 36, 555–56374 Limonta, J. M. et al. (1995) J. Biotechnol. 40, 49–5875 Brem, G. et al. (1994) Gene 149, 351–35576 Buhler, T. A., Bruyere, T., Went, D. F., Stranzinger, G. and Burki, K.

(1990) Biotechnology 8, 140–14377 Niemann, H. et al. (1996) J. Anim. Breed. Genet. 113, 437–44478 Clark, A. J. et al. (1989) Genome 31, 950–95579 Grootwine, E. et al. (1997) Theriogenology 48, 485–499

374 0167-7799/99/$ – see front matter © 1999 Elsevier Science Ltd. All rights reserved. PII: S0167-7799(99)01338-4 TIBTECH SEPTEMBER 1999 (VOL 17)

REVIEWS

Prospects for drug screening using thereverse two-hybrid systemMarc Vidal and Hideki Endoh

Rational drug-screening strategies have been limited by the number of available protein targets. The fields of genomics and

functional genomics are now merging into ‘chemical genomics’ approaches, in which large numbers of potential target proteins

can be used in standardized high-throughput drug-screening assays. Because protein–protein interactions are critical to most

biological processes and can be tested in standardized assays, they may represent optimal targets in the chemical-genomics

era. The reverse two-hybrid system appears to have several properties that would be critical for the success of this approach.

The recent evolution of the drug-screening fieldcan be described in three phases, each originatingfrom the development of a new technology

(Fig. 1). Originally, drug screening was performedusing natural products, with only a limited knowledgeof the molecular mechanisms involved in the develop-ment of diseases. Hence, limited numbers of samples

were screened in relatively crude assay systems, manyof which were phenomenological rather than target-based. For these reasons, drugs were often discoveredfortuitously and, more often, were not found at all.

In the 1980s, two technological developmentsallowed more-systematic approaches to be applied todrug screening. On the one hand, the development oforganic chemistry allowed complex libraries of artifi-cially synthesized drugs to be screened. On the otherhand, molecular biology allowed the use of defined

M. Vidal (Vidal@helix.mgh.harvard.edu) and H. Endoh are at theMGH Cancer Center, Bldg 149, 13th St, Charlestown, MA 02129,USA.

protein targets to be used in drug screening. Thedetailed characterization of the molecular changesbetween unaffected individuals and patients, and theelucidation of the function of the corresponding proteins in model organisms, could be used for more-rational drug-discovery approaches by selecting, char-acterizing and validating a few target proteins. Forexample, a new set of molecules that prevent the activ-ity of the Ras oncoprotein is currently being tested inclinical trials1. The molecules were originally identi-fied as inhibitors of an upstream regulator required forRas farnesylation (see below). It is the identification ofnumerous elements of the Ras pathway that made thechoice of this apparently optimal target possible.

However, such costly and time-consuming projectsoften lead to only a few hits, which turn out to be unviable as therapeutics. For example, particular com-pounds that scored very well in terms of selectivity andsensitivity in vitro can exhibit poor bioavailabilityand/or stability in animal models. In reality, many ofthe drugs sold today have been identified using themethods available before organic chemistry and mol-ecular biology. Thus, it is often argued that rationaldrug-discovery programs could benefit from the availability of many more target proteins.

The field of genomics provides a solution to this prob-lem, because potential protein targets are identified ata much higher rate than with conventional molecularbiology. For example, about half of the human geneshave already been identified using expressed sequence tags(ESTs)2 and the sequences of pathogen genomes arebeing published at a rapid rate3. This genomic infor-mation can help to identify large classes of genes poten-tially involved in a particular disease using functional-genomics strategies4,5. For example, it was recently shownthat the expression of 14 genes is induced to a high levelbefore the onset of apoptosis in p53-expressing human celllines, compared with normal cells6. Many of the corre-sponding gene products, but obviously not all, might rep-resent important proteins for new therapeutic strategies.

Conceptually, the conventional approach includesthe following sequence of events: (1) precise definitionof a few selected targets; (2) formal biological valida-tion of a few of these; and (3) drug screening performedin vitro (Fig. 1). By contrast, the genomics-basedapproach, often referred to as chemical genomics, canbe summarized as follows: (1) genome-wide definitionof many targets; (2) high-throughput drug screeningsfor most of them; (3) biological validation of a fewusing compounds found in step 2 (Fig. 1). One of theobvious advantages of the chemical-genomics approachis the fact that many more protein structures can beused to challenge libraries of compounds, furtherincreasing the probability of finding relevant molecules.However, the chemical-genomics approach introducesa new difficulty: the rapid definition of automateddrug-screening assays for such large numbers of proteins.

This article is an attempt to summarize several aspectsof that challenge, with a particular focus on protein–protein interactions. First, we will discuss the use ofprotein–protein interactions as potential targets for drugdiscovery. Then, we will describe a standard assay, thereverse two-hybrid system, that can be used to identifydissociators for large numbers of protein–protein inter-actions. Finally, we will briefly comment on the factthat small-molecule compounds can be valuable forfunctional studies of gene products and therefore couldbe used as tools for target validation.

Protein–protein interactions as potential drugtargets

The way that the collective participation of proteinsin biological processes is viewed has recently been dra-matically modified. For example, until about a decadeago, enzymes involved in regulatory signal-transductionpathways were described as soluble proteins function-ing as independent entities. It has since become clear,however, that they act in modules consisting of manysubunits that physically interact with each other. It isalso evident that such physical interactions are critical

TIBTECH SEPTEMBER 1999 (VOL 17) 375

REVIEWS

Organic chemistry Molecular biology Genomics

Rationaldrug

screening

Selectionof

target

Chemicalgenomics

Targets

Compounds screened

Target validation

Not defined

Few

—

Few

Many

Before screen

Many

Many

After screen

trends in Biotechnology

Figure 1Chemical genomics. The three circles represent three successive improvements in the field of drug screening, each improvement emerg-ing from the ‘wave’ created by the development of a new technology. Before molecular biology, the protein targets were rarely defined.Assay systems were usually phenomenological and few compounds could be tested. After the wave of molecular biology, convenient manipu-lation of genes and proteins allowed the validation of a few targets prior to screening large numbers of componds. In the recently proposedchemical-genomics approaches, many targets are identified and used in high-throughput drug-screening assays. The potential compoundsidentified are used to validate some of these targets using relevant biological assays.

to a wide range of other biological processes, from largecellular structures to enzymatic complexes.

Consistent with the functional importance of protein–protein interactions, several human diseases(including cancers) occur as a consequence of particu-lar protein–protein dissociation events. An excellentexample of this is the pRB pathway, which regulates theentry into the S phase of the cell cycle and appears tobe affected in nearly all human cancers (Fig. 2)7,8. ThepRB protein, together with the heterodimeric transcrip-tion factor E2F/DP, is thought to repress the transcrip-tion of genes required for the G1–S-phase transition.For cells to enter the cell cycle, a cyclin-D–cyclin-dependent-kinase (CDK) complex is required for thedissociation of the E2F/DP–pRB repressor complex.This kinase complex is itself negatively regulated byproteins such as p16.

In this pathway, like many others, it is striking howimportant protein–protein interactions can be. Theyare critical at essentially any step of the wild-type path-way function in normal cells7,8 (Fig. 2) – p16 acts bydissociating the cyclin-D–CDK complex, which itselfacts by dissociating the E2F/DP–pRB repressor com-plex and this interaction is, in its turn, crucially requiredfor targeting the repressor complex to the relevant genepromoters. In addition, most of the currently knownevents in this pathway that lead to tumor formation are related to a defect in protein–protein interaction;for example, a loss of p16, an amplification of cyclin D, a mutation in a CDK that renders the protein insen-sitive to p16 dissociation, pRB mutations that affect its interaction with E2F/DP and expression by tumor-forming DNA viruses of trans-acting proteins such asadenovirus E1A that dissociate the E2F/DP–pRB7,8.

For these reasons, protein–protein interactions mightbe considered to be important as potential drug targets.For example, in the pRB pathway, the dissociations thathappen downstream of the molecular defect could be

used as epistatic therapeutic strategies, independentlyof the type of molecular lesion upstream in the pathway.

The practicality of protein–protein interactions astargets has been a rather controversial subject. Manyargue against, with only a few arguing in favor. Themost feared issue relates to the relatively large molecularsize of most interacting domains relative to that ofenzyme catalytic domains. It is not widely accepted thatmolecules small enough to penetrate cells could exhibitthe required dissociation abilities by competing withthe binding of such large molecular surfaces. Anotherissue relates to the specificity of dissociation. The argu-ment here is usually that, when potent enough to pre-vent a particular interaction, a small molecule wouldoften prevent related interactions. We would like toargue that, although the answers to these questionsremain unclear, we might now have the appropriatetools and strategies to address them.

It has also been argued that the specificities of pro-tein–protein interactions make them particularly suit-able for drug screening9. An example is provided by thesize-homology domain 2 (SH2) phosphotyrosine pro-teins, which have been found to be dissociated fromtheir interactors by peptides as small as pentamers10;such a small size would allow the design of small organicpeptidomimetics. In addition, even if binding affinitiesare high, the rate of dissociation can also be high, whichis consistent with the notion that binding competitioncould be possible11. These aspects would be applicableto other kinds of interaction, such as leucine zippers,Tam/arh motifs, SH3 domains and zinc fingers9.

So why are protein–protein interactions not used astargets on a more regular basis? The types of problemthat have been encountered during such screens havebeen previously discussed12 but, in summary, in vitroscreening systems have been the most used so far, withthe following technological limitations: the proteins tobe tested must be used in a pure form and at high con-centrations; the read out for dissociator molecules issuch that only high level activities can be detected; andthe concentrations at which the compounds will beactive are largely unknown. Thus, it is unclear at thispoint whether protein–protein interactions are inher-ently incompatible with drug-screening programs or,alternatively, whether it is the technological limitationsof the assays used so far that have been the cause for theapparent lack of success.

Perhaps some of these limitations could be bypassedby the use of in vivo, cell-based assays, especially thosein which the assay is set up so that the compounds con-fer a selective growth advantage. We will summarizethe use of the yeast Saccharomyces cerevisiae as a cell-basedassay for drug screening, focusing particularly on meth-ods relying upon growth as a read out. In addition, wewill review the use of a genetic strategy in which protein-interaction-dissociator compounds can confer a selec-tive advantage, thereby facilitating their identification– the reverse two-hybrid system.

Yeast drug-screening assays based on growthselection

Yeast geneticists have long exploited the power ofgrowth selections to identify important genes andmutations. In these experiments, the genotype and/orthe growth media are manipulated to obtain a set of

376 TIBTECH SEPTEMBER 1999 (VOL 17)

REVIEWS

pRB pathway Physical interactions

Cyclin Dp16

CDK4/6

pRB

E2F

Cell-cycleprogression

CDK4/6

CDK4/6Cyclin D

Cyclin D

p16

pRB

DP

E2FpRB

E2F

DP

trends in Biotechnology

Figure 2The pRB pathway. Each functional step of the pRB pathway is medi-ated by a protein–protein interaction. Interactions represented byshaded circles are detectable in the two-hybrid system.

conditions under which the initial strain is not able togrow. Such genetic selections allow very rare events tobe detected: a few growing colonies can be detectedamong millions of non-growing colonies. Althoughgenetic selections are usually used to identify genes andmutations that can exhibit particular functions (posi-tive selections), they can also be used to identify eventsthat affect particular functions (negative selections). Forexample, yeast auxotrophic mutants unable to synthe-size pyrimidine can be selected on the basis of theirresistance to ureodosuccinic acid, because wild-typestrains are sensitive to this drug13.

Drug-screening programs, so far unrelated to physi-cal protein–protein interactions, have already benefitedtremendously from such negative selections. For example,in a screen for compounds that affect the farnesylationof small G proteins such as Ras, a genetic situation was created in which the wild-type function of anendogenous yeast G protein (Ste18p) is toxic14. Thismade it possible to identify manumycin as a potentinhibitor of farnesylation because this compound res-cues the toxicity. Manumycin was found to inhibit thegrowth of Ki-Ras-transformed fibrosarcomas, which isconsistent with its possible use as an antitumor drug. Inanother example, the influenza-A-virus M2 proteinwas shown to impair the growth of wild-type yeastcells, apparently by altering the electrochemical proteingradient established by the H1 ATPase. This negativeselection was used to screen a compound library and identified BI1743, a compound that later was confirmed to be an antiviral agent15.

Such negative selections present two major advan-tages. First, they can be extremely sensitive. For example,differences in the transcriptional level of a particulargene of less than a factor of two can be identified whenusing the appropriate selections16,17. Second, comparedwith more-conventional detection methods based onthe loss of particular activities (enzymatic or viabilityassays), they automatically prevent the identification oftoxic molecules.

Because yeast genetics is extremely versatile, one canimagine that a number of functional assays18 could beeasily turned into negative selections. For example, thefunction of p53 can be recreated in yeast cells if theproper p53-binding site is present in the promoter of areporter gene19. By using a toxic marker, it has provedto be possible to design p53-specific negative selection20.

Two-hybrid systems and drug screening

Forward two-hybrid systemTraditionally, the tools available to identify, charac-

terize and manipulate protein–protein interactions havebeen restricted to biochemical approaches. Despitetheir inherent advantages, biochemical approaches canbe time consuming and genetic selection systems areoften needed. The yeast two-hybrid system is one ofthe most powerful genetic methods to study protein–protein interactions21.

The two-hybrid system22 is based on the fact thatmany sequence-specific transcription factors increasethe rate of transcription of their target genes by bind-ing to upstream activating DNA sequences (UAS) andactivating RNA-polymerase II at the correspondingpromoters. The DNA-binding and activating functions

are located in physically separable domains, which arereferred to as the DNA-binding domain (DB) and theactivation domain (AD), respectively23. It has been shownthat protein–protein interactions unrelated to transcrip-tion factors can reconstitute a functional transcriptionfactor by bringing the DB and AD into close physicalproximity22. Thus, the reconstitution of a functionaltranscription factor can be summarized as DB–X:AD–Y,where X and Y could be essentially any proteins fromany organism.

When yeast-growth selection markers such as LEU2or HIS3 (genes involved in leucine and histidine syn-thesis, respectively) are expressed from a promoter con-taining DB-binding sites, the DB–X:AD–Y interactionconfers a selective advantage. Thus, a few growing yeastcolonies can be identified on plates lacking the corre-sponding amino acid24–26. Such positive selections havebeen used to identify a great number of important protein–protein interactions.

Reverse two-hybrid systemPotential protein–protein interactions identified with

the two-hybrid system represent hypotheses that needto be tested in the context of relevant biological systems.Conventional approaches include coimmunoprecipi-tation of endogenous proteins, coimmunolocalization,gradient sedimentation and a number of biophysicaltechniques. However, the most direct approach genet-ically correlates the potential physical interaction witha biological parameter: the physical interaction is dis-sociated and the consequences analysed in a functionalassay. One would logically expect that, if the newlydetected interaction is critical for a function of interest,preventing the interaction would impair that function21.

Conceptually, protein–protein interactions can beinhibited by the use of cis-acting mutations in one part-ner (referred to as interaction-defective alleles) or trans-acting molecules such as dissociating peptides or smallmolecules. For example, interaction-defective allelescan be compared with their wild-type counterparts fortheir ability to functionally complement a knock-outin the corresponding gene or for their ability to func-tion in an expression assay in the relevant cells27.Alternatively, dissociating peptides can be expressed orsmall molecules coincubated in similar biological sys-tems, and the functional consequences of dissociatinga particular interaction analysed.

Until recently, these genetic strategies to validatepotential interactions have not been widely used, owingto the technical difficulties of identifying informativeinteraction-defective alleles or specific dissociating molecules. The main challenge for interaction-defectivealleles is to create subtle mutations that disrupt theinteraction without grossly affecting the protein. Sim-ilarly, one might expect that complex libraries need tobe screened to find a few specific dissociating peptidesor small molecules.

The problem of screening very large libraries, of allelesor of molecules, could be overcome by using a geneticselection in which preventing the interaction providesa selective advantage. This situation is provided in thecontext of the reverse two-hybrid system28. In thisupside-down version of the two-hybrid system, thewild-type DB–X:AD–Y interaction can be toxic or lethalfor the yeast cells because a toxic marker (e.g. URA329)

TIBTECH SEPTEMBER 1999 (VOL 17) 377

REVIEWS

is used as a reporter gene (negative selection). In thissetting, DB–X:AD–Y dissociation confers a selectivegrowth advantage that can conveniently identify bothinteraction-defective alleles and dissociating peptides orsmall molecules (Fig. 3). Yeast cells that express URA3,a gene normally involved in the synthesis of uracil, failto grow on media containing 5-fluoro-orotic acid (5-FOA) because the URA3 enzyme transforms 5-FOA into a toxic compound. Thus, DB–X:AD–Yexpressing cells are 5-FOA sensitive (5-FOAS).

The conceptual and technical aspects of selecting forinteraction-defective alleles using the reverse two-hybrid system have been discussed before28,30. Here, wewill concentrate on the use of the reverse two-hybridsystem to identify trans-acting dissociators.

The idea that trans-acting dissociation of protein–protein interactions could be genetically selected usingthe reverse two-hybrid system has recently beendemonstrated. A short adenovirus-E1A peptide wasshown to rescue the 5-FOAS phenotype in yeast cellsexpressing the DB–pRB:AD–E2F1 interaction29. It iswell known that, upon adenovirus infection, E1Amediates the dissociation of pRB from E2F1 in the hostcell31. The specificity of dissociation was demonstratedby the fact that mutant versions of E1A that are unableto dissociate pRB from E2F1 in mammalian cells arealso unable to rescue the 5-FOAS phenotype.

It was shown subsequently that the small organicmolecule FK506 can rescue the 5-FOAS phenotypeconferred by the interaction between the cytoplasmicdomain of the TGF-b-type-I receptor (RIC) fused toDB and FKBP12 fused to AD32 (Figs 4,5). Althoughthe interaction between FKBP12 and RIC has notbeen completely validated in vivo, it had been previouslyshown that FK506 affects its two-hybrid read out33. Insuch reverse two-hybrid assays, the FK506-dissociating

effect can be detected at submicromolar concentrationsand, importantly, specificity controls are also possibleusing unrelated protein–protein interactions (Fig. 4).

Last but not least, it was recently demonstrated thatsmall molecules can be selected de novo from complexlibraries using a yeast reverse two-hybrid system. In thiscase, the interaction between two subunits of an N-type calcium channel was reconstituted in a two-hybridconfiguration: the b3 subunit was fused to Gal4 DBand the a1B-I–II intracellular loop was fused to theGal4 AD34. The negative selection was provided by yetanother negative selection marker, CYH2, containingGal4-binding sites in its promoter35. Expression of thisreporter gene confers toxicity in a particular yeast back-ground when the cells are incubated on cycloheximideplates36. Using a plate agar-diffusion assay28, ten mol-ecules could be recovered from 156 000 small syntheticcompounds on the basis of their ability to rescue thecycloheximide sensitivity of DB–b3:AD–a1B-expressingcells. The most potent of these molecules is able toblock N-type calcium channel activity at concentrationsclose to 100 mM in superior cervical ganglion neurons.

Practical issuesIt remains to be shown how general the above strat-

egy can be and, for this reason, it is important to focuson solving several practical issues. Chemical genomicsis based on using large numbers of potential protein tar-gets for drug screening and so it requires the develop-ment of high-throughput standardized assays. The yeastreverse two-hybrid drug-screening assay seems ideal for this purpose, for several reasons. First, as the yeastcells used in the assay reproduce, expensive and peculiarbiochemical purification of the target molecules is notneeded and so many proteins can be tested in a rela-tively short time. In addition, potential compounds are

378 TIBTECH SEPTEMBER 1999 (VOL 17)

REVIEWS

Yeast growth selections

Control Forward Reverse

DB

DB

X

X

Y

Y

*

AD

Drug

AD

a

bInteraction disrupted

trends in Biotechnology

Figure 3An illustration of the way two-hybrid systems work. The grey and white patches on the right-hand side represent growing and non-growingyeast cells, respectively; under normal conditions (control), the yeast cells grow whether or not a two-hybrid interaction takes place. In theforward two-hybrid selections, potential interactions (a) are identified by the transcriptional activation of a reporter gene required for growth,which confers a selective advantage. In the reverse two-hybrid selections, the interaction activates the expression of a ‘toxic gene’ and soprevention of the interaction (b) provides a selective advantage.

examined in a biological context that resembles physio-logical conditions. Thus, cell permeability and cyto-toxicity (because of the nature of a negative selection)are included as parameters in the screen. This pre-selection is not available to in vitro assays and shouldtherefore represent a major advantage of cell-basedassays.

More importantly, the assay is completely standard-ized. Interactions between unrelated proteins are testedin almost exactly the same way. This means that manyprotein–protein interactions involved in a particularpathway can be tested simultaneously in a multiplexformat. When using the assay shown in Fig. 4, yeastcells expressing different DB–X:AD–Y interactions andexhibiting a similar 5-FOAS phenotype (Ref. 28) canbe pooled and seeded on the surface of a solid agarmedium containing the relevant concentration of 5-FOA, producing a lawn of non-growing cells.

Compounds are spotted onto the plate and diffuse toproduce a gradient of concentrations. A ring of growthis expected around non-toxic compounds that are ableto penetrate the cells and dissociate one of the interac-tions expressed by the pool of cells. The particularinteraction prevented in the assay can then be identifiedby a convenient PCR analysis (Fig. 5). The recovery ofa single type of cell expressing one particular interac-tion represents an internal control for specificity in such

experiments. This multiplex method would not bepossible in the forward two-hybrid configuration because,in this setting, the growth of cells expressing interac-tions unrelated to the drug mask the effect (Fig. 4).

This assay is also designed to be compatible with theuse of current high-throughput methods to handlelarge numbers of compounds. It allows the use of suit-ably arrayed compound libraries and natural products,usually available in a 96-well or 384-well format. Mul-tiplex screening is imaginable with up to a few dozensof compounds in a mixture corresponding to each spoton a Petri plate, provided that convenient deconvolu-tion methods are available to identify which compoundin the mixture is responsible for the activity. (Mixing afew dozen compounds requires that any toxic mol-ecules in the library are not too toxic, because the assayis based on a growth phenotype.) In addition, it is idealfor automated tools using pipette-handling work-stations, and computerized systems could be producedto handle the data.

Despite these potential advantages, yeast systems areusually not considered to be ideal because of potentialdrug permeability problems. However, particular geneticmodifications can be used to make yeast two-hybridstrains more permeable to a wide range of compounds,including hydrophilic or charged molecules. One ofthe most widely used mutations is erg6, which affects

TIBTECH SEPTEMBER 1999 (VOL 17) 379

REVIEWS

DB–DP:AD–E2F1

DB–RIC:AD–FKBP12

Multiplex

a b

0 0.2 2.0 0 0.2 2.0 0 0.2 2.0

c

FK506 concentration (µM)

trends in Biotechnology

Figure 4Drug screening with the reverse two-hybrid system. All experiments were performed using synthetic complete medium lacking leucine andtryptophan. The controls (a) show that, under normal conditions (i.e. in the absence of any drug), FK506 does not alter growth. The reversetwo-hybrid experiments (c) also used 5-fluoro-orotic acid (5-FOA), a drug that prevents the growth of yeast cells expressing functional inter-actions. FK506 prevents the interaction of the TGF-b receptor (RIC) and FKBP12, and rescues the 5-FOA sensitivity of cells expressing RICand FKBP12 in fusion with the Gal4 DNA-binding domain (DB) and activation domain (AD), respectively (middle row). As expected, this growth advantage was not observed with cells expressing irrelevant interactions such as DP:E2F1 (top row). In the multiplex setting (bottomrow), yeast strains expressing a variety of interactions (wild-type Gal4, DB–pRB:AD–E2F1, DB–DP:AD–E2F1, DB–Fos:AD–Jun andDB–RIC:AD–FKBP12) were pooled before plating. The forward two-hybrid experiment (b) used 3-aminotriazole (3AT), a drug that inhibits theHIS3 enzyme28. In this system, FK506 is expected to inhibit the growth of DB–RIC:AD–FKBP12-expressing cells (middle row) but not of cellsexpressing irrelevant interactions (top row). In the multiplex setting (bottom row), the compound effect is not detectable because the growthof cells expressing non-dissociatable interactions is not altered by FK506.

ergosterol synthesis and membrane functions37, andincreases the cell’s sensitivity to several compounds (e.g.cycloheximide37, brefeldin A38–40, nalixidic acid41,anthracyclines and dactinomycin42, and sodium lithium43).

However, in some strain backgrounds, the manipu-lation of erg6 mutants is complicated by severalpleiotropic phenotypes such as a low efficiency of plas-mid transformation and a reduced rate of tryptophantransport37. Thus, alternative mutations might sometimesbe more appropriate. For example, strains containing adeletion of PDR5, a gene that encodes a drug-extrusionpump, also exhibit multidrug-sensitivity phenotypes44.The ability of FK506 to rescue the 5-FOAS ofDB–RIC:AD–FKBP12-expressing cells is greatlyincreased in a pdr5D background (M. Vidal andH. Endoh, unpublished). Other mutations that increasedrug sensitivity include snq2 and yor145,46.

Until recently, cell-based assays such as the reversetwo-hybrid system were not considered to be suitablefor screening molecules generated by combinatorialchemistry, which produces more-complex libraries ofcompounds. This is because cell-based assays usuallyrequire relatively high concentrations, and these are notnecessarily provided by combinatorial-chemistrystrategies. However, a system was recently developedin which cells are confined in minuscule lipid vesicles,referred to as nanodroplets47. Such small volumes arecompatible with higher active concentrations. It hasbeen shown that the nanodroplet system is compatiblewith reverse two-hybrid selections32.

Geneticists in need of drugs, or it takes two totango

Geneticists have long perceived the power of com-bining the use of compounds and genetic tools. For

example, the use of benomyl in combination withmutations affecting yeast actin function was extremelyhelpful in understanding the yeast cytoskeleton48.Another example is the use of antitumor moleculescombined with yeast checkpoint mutants in order todesign alternative therapeutic strategies49.

However, until recently, these approaches remainedunder-developed because of the limited number ofavailable molecules. Most of the molecules used to datewere discovered fortuitously. In addition to helping findnovel therapeutic strategies, genomics could also gen-erate new sets of tools to answer fundamental questions.In this regard, new tools have recently been developedthat address the problem of systematically finding pep-tides that affect protein function. For example, peptidesthat bind and affect a target protein can be identifiedusing modified versions of the two-hybrid system50,51.Another example is the systematic use of libraries ofpeptides to isolate compounds that perturb most stepsof the yeast a-factor-signal-transduction pathway52. Itis possible that, in the near future, such screens mightbe performed to identify small organic compoundswith similar activities.

A number of laboratories have recently initiated pro-tein-interaction-mapping projects for a few modelorganisms such as phage T753, S. cerevisiae54,55 and thenematode Caenorhabditis elegans56. These projects usehigh-throughput versions of the forward two-hybrid system and provide many potential targets for the methods described here. At least some of the com-pounds identified by reverse two-hybrid selectionscould be valuable to validate potential interactionsrapidly in vivo.

It has been demonstrated, although not for protein–protein-interaction inhibitors, that small molecules

380 TIBTECH SEPTEMBER 1999 (VOL 17)

REVIEWS

1.5 kb AD–FKBP12DB–RIC

0.5 kb

AD

DB

AD

DB

AD

DB

AD

DB

AD

DB

AD

DB

DB

–pR

B:A

D–E

2F1

DB

–Fos

:AD

–Jun

GA

L4:A

D

DB

–DP

:AD

–E2F

1

DB

–RIC

:AD

–FK

BP

12

Rin

g of

gro

wth

M M M

trends in Biotechnology

Figure 5The interaction dissociated in the multiplex reverse two-hybrid experiment (Fig. 4) was identified by a PCR reaction using yeast cells as tem-plate and specific primers to amplify sequences fused to the DNA-binding domain or the activation-domain28. The ethidium-bromide-stainedgel compares the expected PCR products for each of the interactions tested here with the products obtained from the 5-FOA-resistant ringof growth from Fig. 4 and shows that DB–RIC:AD–FKBP12 interaction is specifically dissociated by FK506.

can be used to characterize regulatory pathways inC. elegans. For example, manumycin is capable of sup-pressing the multivulva phenotype caused by expressionof let-60(gf), an activated allele of ras that is analogousto those found in human cancers57. As demonstrated inthis experiment, the nematode system provides anexcellent opportunity to control the specificity of theexpected biological effects. For example the manumycineffect is not observed for other gain-of-functionmutations acting downstream of Ras.

ConclusionDespite their biological importance in human dis-

eases, protein–protein interactions are not widely used as drug targets. However, recent technologicaldevelopments might facilitate their use in screeningprograms. Functional-genomics projects can rapidly identify large numbers of potential protein–proteininteractions for a particular pathway using large-scaleforward two-hybrid selections. On the other hand, thereverse two-hybrid system provides a genetic growthselection that can be applied to the identification ofcompounds that prevent these interactions. Whenstudying protein interactions in model organisms inwhich genetic tools are applicable, the potential com-pounds can be tested in well-controlled biological sys-tems. Such combined approaches might provide usefultest cases for the development of novel therapeuticstrategies based on protein–protein interactions.

AcknowledgmentsWe thank our colleagues and friends M. Boisclair,

G. Cottarel, A. Fattaey, A. Kuliopulos and M. Walhout,and an anonymous reviewer, for their helpful sugges-tions on the manuscript. We are grateful to T. Wangfor providing the cDNAs encoding FKBP12 and RIC. Part of the work described here is supported by NHGRI grant 1 RO1 HG01715-01 to M.V. Wealso thank Yamanouchi Pharmaceuticals for their support.

References1 Gibbs, J. B. and Oliff, A. (1994) Cell 79, 193–1982 Hillier, L. D. et al. (1996) Genome Res. 6, 807–8283 Bult, C. J. et al. (1996) Science 273, 1058–10734 Lander, E. S. (1996) Science 274, 536–5395 Fields, S. (1997) Nat. Genet. 15, 325–3276 Polyak, K., Xia, Y., Zweier, J. L., Kinzler, K. W. and Vogelstein, B.

(1997) Nature 389, 300–3057 Dyson, N. (1998) Genes Dev. 12, 2245–22628 Sardet, C., LeCam, L., Fabbrizio, E. and Vidal, M. (1997) in Onco-

genes as Transcriptional Regulators (Vol. 2) (Ghysdael, J. and Yaniv, M.,eds), pp. 1–63, Birkhauser Verlag

9 Brugge, J. S. (1993) Science 260, 918–91910 Fantl, W. J. et al. (1992) Cell 69, 413–42311 Felder, S. et al. (1993) Mol. Cell. Biol. 13, 1449–145512 Huber, H. E., Koblan, K. S. and Heimbrook, D. C. (1994) Curr.

Med. Chem. 1, 13–3413 Bach, M. L. and Lacroute, F. (1972) Mol. Gen. Genet. 115, 126–13014 Hara, M. et al. (1993) Proc. Natl. Acad. Sci. U. S. A. 90, 2281–228515 Hahnenberger, K. M. and Kurtz, S. E. (1997) Trends Biotechnol. 15,

1–416 Vidal, M., Buckley, A. M., Hilger, F. and Gaber, R. F. (1990) Genetics

125, 313–32017 Vidal, M. and Gaber, R. F. (1991) Mol. Cell. Biol. 11, 6317–632718 Kirsch, D. R. (1993) Curr. Opin. Biotechnol. 4, 543–552

19 Ishioka, C. et al. (1993) Nat. Genet. 5, 124–12920 Brachmann, R., Vidal, M. and Boeke, J. (1996) Proc. Natl. Acad. Sci.

U. S. A. 93, 4091–409521 Vidal, M. and Legrain, P. (1999) Nucleic Acids Res. 27, 919–92922 Fields, S. and Song, O. (1989) Nature 340, 245–24623 Keegan, L., Gill, G. and Ptashne, M. (1986) Science 231, 699–70424 Gyuris, J., Golemis, E., Chertkov, H. and Brent, R. (1993) Cell 75,

791–80325 Vojtek, A. B., Hollenberg, S. M. and Cooper, J. A. (1993) Cell 74,

205–21426 Durfee, T. et al. (1993) Genes Dev. 7, 555–56927 Yasugi, T., Vidal, M., Sakai, H., Howley, P. M. and Benson, J. D.

(1997) J. Virol. 71, 5942–595128 Vidal, M. (1997) in The Yeast Two-Hybrid System (Bartels, P. and

Fields, S., eds), pp. 109–147, Oxford University Press29 Vidal, M., Brachmann, R., Fattaey, A., Harlow, E. and Boeke, J. D.

(1996) Proc. Natl. Acad. Sci. U. S. A. 93, 10315–1032030 Vidal, M., Braun, P., Chen, E., Boeke, J. D. and Harlow, E. (1996)

Proc. Natl. Acad. Sci. U. S. A. 93, 10321–1032631 Fattaey, A. R., Harlow, E. and Helin, K. (1993) Mol. Cell. Biol. 13,

7267–727732 Huang, J. and Schreiber, S. L. (1997) Proc. Natl. Acad. Sci. U. S. A.

94, 13396–1340133 Wang, T., Donahoe, P. K. and Zervos, A. S. (1994) Science 265,

674–67634 Young, K. et al. (1998) Nat. Biotechnol. 16, 946–95035 Leanna, C. A. and Hannink, M. (1996) Nucleic Acids Res. 24,

3341–334736 Sikorski, R. S. and Boeke, J. D. (1991) Methods Enzymol. 194,

302–31837 Gaber, R. F., Copple, D. M., Kennedy, B., Vidal, M. and Bard, M.

(1989) Mol. Cell. Biol. 9, 3447–345638 Vogel, J. P., Lee, J. N., Kirsch, D. R., Rose, M. D. and Sztul, E. S.

(1993) J. Biol. Chem. 268, 3040–304339 Graham, T. R., Scott, P. A. and Emr, S. D. (1993) EMBO J. 12,

869–87740 Shah, N. and Klausner, R. D. (1993) J. Biol. Chem. 268,

5345–534841 Prendergast, J. A. et al. (1995) Yeast 11, 357–54742 Hemenway, C. S. and Heitman, J. (1996) J. Biol. Chem. 271,

18527–1853443 Welihinda, A. A., Beavis, A. D. and Trumbly, R. J. (1994) Biochim.

Biophys. Acta 1193, 107–11744 Balzi, E. and Goffeau, A. (1995) J. Bioenerg. Biomembr. 27, 71–7645 Katzmann, D. J., Hallstrom, T. C., Mahe, Y. and Moye-Rowley, W. S.

(1996) J. Biol. Chem. 271, 23049–2305446 Nourani, A., Wesolowski-Louvel, M., Delaveau, T., Jacq, C. and

Delahodde, A. (1997) Mol. Cell. Biol. 17, 5453–546047 Borchardt, A., Liberles, S. D., Biggar, S. R., Crabtree, G. R. and

Schreiber, S. L. (1997) Chem. Biol. 4, 961–96848 Huffaker, T. C., Hoyt, M. A. and Botstein, D. (1987) Annu. Rev.

Genet. 21, 259–28449 Hartwell, L. H., Szankasi, P., Roberts, C. J., Murray, A. W. and

Friend, S. H. (1997) Science 278, 1064–106850 Yang, M., Wu, Z. and Fields, S. (1995) Nucleic Acids Res. 23,

1152–115651 Colas, P., Cohen, B., Jessen, T., Grishina, I., McCoy, J. and Brent, R.

(1996) Nature 380, 548–55052 Caponigro, G., Abedi, M. R., Hurlburt, A. P., Maxfield, A.,

Judd, W. and Kamb, A. (1998) Proc. Natl. Acad. Sci. U. S. A. 95,7508–7513

53 Bartel, P. L., Roecklein, J. A., SenGupta, D. and Fields, S. (1996)Nat. Genet. 12, 72–77

54 Fromont-Racine, M., Rain, J. C. and Legrain, P. (1997) Nat. Genet.16, 277–282

55 Evangelista, C., Lockshon, D. and Fields, S. (1997) Trends Cell Biol.6, 196–199

56 Walhout, A. J. M., Endoh, H., Thierry-Mieg, N., Wong, W. andVidal, M. (1998) Am. J. Hum. Genet. 63, 955–961

57 Hara, M. and Han, M. (1995) Proc. Natl. Acad. Sci. U. S. A. 92,3333–3337

TIBTECH SEPTEMBER 1999 (VOL 17) 381

REVIEWS