and the ones studied most extensively (10,55). Their import- ance was emphasized by screens for random activating frag- ments of the E.coli genome or activating peptides, both of which mainly identiﬁed acidic activators (56). The ﬁnding was conﬁrmed in this study by the lower isoelectric point of activators (Figure 3A), more acidic stretches (Figure 4F) and by the increased clustering of aspartate (Figure 4E). Other previously deﬁned activator classes were also mir- rored by this study: glutamine-rich activators (Figure 4B, Supplementary Figure 2) (11), proline-rich activators (Figure 4D) (12) and serine-rich activators (Figure 4C) (57). Elevated asparagine levels and cluster values (Figure 4A and Supplementary Figure 2) of activators may indicate a role of an additional amino acid in transcription activation, possibly similar to the closely related amino-acid glutamine in glutamine-rich activators. It is generally accepted that ADs mediate their function by speciﬁc interactions with the basal transcription machinery (5) or additional factors involved in transcription, like chromo- some remodeling complexes (58). Transcription activators analyzed in this study form a highly interconnected protein interaction network involving transcription related proteins (Figure 5A). Protein interactions with transcription-related proteins are highly overrepresented, even in the set of activ- ators which have not been described as TRs (Figure 5B and C). A signiﬁcant fraction of nuclear Y2H activators as well as known TRs interact with the basal transcription machinery (like RNA polymerase II) and chromosome remodeling com- plexes (like the SAGA complex) (Supplementary Table 4). These interactions possibly explain their activation properties. Strikingly, the top ranking interaction partner of nuclear activators not known to regulate transcription was also RNA polymerase II. This indicates that genuine transcription regulators are contained in this set and form the basis for annotating them as TRs (see Results). However, for a large fraction of the Y2H activators no interactions are known and thus their mode of action remains unclear. A surprising ﬁnding is the frequent interaction of activators with alcohol dehydrogenase. This enzyme is found with 10% of nuclear proteins (data not shown) but with 20% in both the nuclear activator and known transcription regulator dataset. Thus, alcohol dehydrogenase might be more a speciﬁc com- ponent of activation complexes than an unspeciﬁc contamina- tion. Such moonlighting functions (59) have been found in other components of various transcription factor complexes but also among other proteins such as actin which acts both as a cytoskeletal protein and a transcription factor (60). Screening of protein fragments fused to the DBD of Gal4 has been used before to identify transcriptional activat- ors. Ruden et al. (56) screened fragments of the E.coli genome and mainly identiﬁed acidic stretches that activate transcrip- tion in yeast. Wiesner et al. (24) established a screening sys- tem based on a GFP reporter gene in a murine cell line and identiﬁed human transcription factors using a cDNA library. In contrast to this study we could make use of extensive large-scale studies which not only identiﬁed a set of trans- cription activators (26,27), but also localized proteins (33), measured protein concentrations (34) and protein interactions (26,27,46,47). The measurement of the activation strength let us focus on sets of proteins highly enriched in known TRs. Combining several data sources, for six uncharacterized Nucleic Acids Research, 2006, Vol. 34, No. 3 965 yeast proteins we could identify additional evidence for their genuine role in transcriptional regulation. We are aware that we may have missed a number of activ- ators: of 138 proteins annotated with the GO-term ‘transcrip- tional activator activity’ in the YPD database 55 (40%) were detected as activators in this study (Supplementary Table 1), suggesting a false negative rate of 60%. This discrepancy may be explained by the fact that some TRs are annotated as activators although they do not act as such in our assay. Other proteins may require speciﬁc promoters, cofactors or conditions to exhibit activating activity. For example, several activators are only active when yeast is grown in glucose- free media. Interestingly, Gal4 itself becomes a very weak activator when fused to another Gal4–DBD, i.e. the DBD appears to inhibit the activating properties of some proteins. Thus, our studies need to be extended in order to get a more complete picture of all transcriptional activators in yeast, e.g. by using different conditions or fusing the DBD C-terminally. Outlook Although we have identiﬁed a large number of transcriptional activators in yeast and semi-quantitatively measured their activation strength, we have not measured activation in a truly quantitative way. New reporter strains with luciferase or some other enzyme need to be used in the future for more precise measurements. In addition, we have not determined the actual ADs. Given the poor deﬁnition of ADs, it remains an important challenge to map these domains by fragmenting the proteins described here. As ADs do not appear to be well-deﬁned struc- tural domains it appears to be likely that they do not require a deﬁned 3D structure. Instead, short linear peptides may con- tain the activation activity proper. Once the ADs have been mapped, their interactions with the transcriptional machinery have to be identiﬁed. Unfortunately, the classical two-hybrid system cannot be used for this purpose. Thus either classical biochemistry or alternative sys- tems have to be used such as the split-Ubiquitin system (61). Finally, activators can differ in their ability to activate tran- scription dependent on the promoters to which they are bound (62,63). Hence all activators need to be tested in different promoter contexts and their transcriptional activity quantiﬁed. Once their interactions have been mapped on a proteome- wide scale and their promoter context evaluated, it may be possible to generalize these ﬁndings and predict the transcrip- tional activity of every protein, including their activation strength. This would be a major step towards quantitative and thus systems biology. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. ACKNOWLEDGEMENTS We thank Olivier Kassel and Ivan Sadowski for helpful com- ments on the manuscript. Ed Hurt and Jochen Baßler kindly provided help with the YPD database. This work was supported by DFG grant Ue 50/2-1. B.T. is a fellow of the Studienstiftung des deutschen Volkes. Funding to pay the Open Access