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To facilitate studies of the yeast proteome, we cloned 5800 open reading frames and overexpressed and purified their corresponding proteins. The proteins were printed onto slides at high spatial density to form a yeast proteome microarray and screened for their ability to interact with proteins and phospholipids. We identified many new calmodulin- and phospholipid-interacting proteins; a common potential binding motif was identified for many of the calmodulin-binding proteins. Thus, microarrays of an entire eukaryotic proteome can be prepared and screened for diverse biochemical activities. The microarrays can also be used to screen protein-drug interactions and to detect posttranslational modifications.

Although approximately 10,000 antibodies are available from commercial sources, antibody reagents are still unavailable for most proteins 1 . Furthermore, new applications such as antibody arrays 2 , 3 , 4 , 5 and monoclonal antibody therapeutics 6 , 7 have increased the demand for more specific antibodies to reduce cross-reactivity and side effects. An array containing every protein for the relevant organism represents the ideal format for an assay to test antibody specificity, because it allows the simultaneous screening of thousands of proteins for possible cross-reactivity. As an initial test of this approach, we screened 11 polyclonal and monoclonal antibodies to ∼5,000 different yeast proteins deposited on a glass slide and found that, in addition to recognizing their cognate proteins, the antibodies cross-reacted with other yeast proteins to varying degrees. Some of the interactions of the antibodies with noncognate proteins could be deduced by alignment of the primary amino acid sequences of the antigens and cross-reactive proteins; however, these interactions could not be predicted a priori . Our findings show that proteome array technology has potential to improve antibody design and selection for applications in both medicine and research.

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.