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  • CGS 21680 Protein arrays provide a new approach for the iden

    2022-11-18

    Protein arrays provide a new approach for the identification of substrates for several protein-modifying enzymes. For example, protein kinase substrate identification, which constitutes an important aspect of pathway mapping because of the prevalence of these substrates in biological pathways. MacBeath and Schreiber [3] provided the initial demonstration that protein kinase A (PKA), casein kinase II (CK2) and p42 mitogen-activated protein (MAP) kinase (Erk2) specifically phosphorylate known substrates when immobilized on protein microarrays. We have applied a similar approach to identify kinase substrates by using both yeast and human proteome microarrays. The assay involves adding a kinase and 33P-γ-labeled ATP to a protein array printedon modified glass. After washing the slide, the image is acquired using either X-ray film or a phosphor screen (phoshorimager). Figure 3b shows an image of a yeast proteome array that has been treated with a kinase. The ability to quickly and comprehensively identify substrates for protein kinases will provide new insight into the roles of kinases and their substrates in biochemical pathways. Kinase-substrate profiling on peptide arrays (containing short unstructured peptide sequences) has also been useful for investigating CGS 21680 and will probably complement work on protein arrays [24].
    Assembling pathways from protein microarray assay data Similar to other genomic and proteomic technologies, protein micoarray experiments can generate significant amounts of data in relatively short periods of time. This introduces the challenge of presenting large amounts of data in an CGS 21680 intuitive and understandable form to facilitate interpretation. A further challenge results from the need to integrate this information within the context of known pathways (for example, see Saccharomyces cerevisiae Genome Database at http://pathway.yeastgenome.org:8555/YEAST/class-subs-instances?object=Pathways), known interactions (for example, see Human Protein Reference Database at http://www.hprd.org) and published literature. As datasets grow, the use of appropriate software for such tasks becomes increasingly important. As an example of data integration, a protein array containing the majority of proteins from the yeast S. cerevisae was probed with a yeast protein phosphatase (Pph3). Two notable interactions were observed on the array. One interactor was identified as Tip41, a protein known to associate with Pph3; a second interactor was identified as Rrd1 [25]. The function of Rrd1 is unknown but has been predicted to be a regulator of Pph3 because Pph3 overexpression can suppress the synthetic lethal phenotype of a rrd1, rrd2 double mutant [26]. Thus, the protein array data confirm the two-hybrid interactions of Tip41 with Pph3 and provides biochemical evidence for a direct interaction of Rrd1 with Pph3. Using Pathblazer 2.0 software (http://www.invitrogen.com/bioinformatics), a pathway diagram (Figure 4) was built that depicts Rrd1 as an activator of Pph3, which in turn regulates cell-cycle progression through the protein kinases Cla4, Swe1 and Cdc28 [27]. Although this example is relatively straightforward, the same basic approach can be applied to much larger datasets. Data from public or commercially available databases, for instance, can be integrated with the experimental data to create much more complex pathway diagrams with relatively little effort (for example, see Kyoto Encyclopedia of Genes and Genomes at http://www.genome.ad.jp/kegg/kegg2.html). Multicomponent pathways can be postulated by integrating protein microarray data with other data types (Figure 4). In theory, it should also be possible to reconstitute pathways on protein arrays, an achievement recently accomplished with seven enzymes that synthesise the carbohydrate trehalose [28]. Because the contribution of each protein can be investigated by determining which proteins or enzymes are necessary for downstream signaling, protein microarrays can be used to generate and test models of intracellular pathway signaling.