This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.Despite the wealth of useful information coming from large scale protein interaction technologies, including yeast two-hybrid screens and MS-based protein complex determinations, it is becoming increasingly apparent that they have two major shortcomings. They all detect false positives and they all miss many biologically relevant interactions. The actual frequency of false positives and false negatives is the subject of some debate, and likely depends on the technique and how it is applied, but it is clear that all approaches have both problems. Comparisons of large scale yeast interaction data sets with each other and with published data have provided several important insights (von Mering et al., 2002). First, correlations with published data and with linked functional annotations indicate that the bulk of the high throughput data, at least from the yeast studies, contains true positives; i.e., it is not mostly junk. Second, the small fraction of overlap between datasets suggests that each method is prone to missing interactions (accepting that no dataset consists mostly of false positives). And finally, the overlapping data consisting of interactions found in two or more approaches, was found to contain the highest frequency of true positives. Combined, these findings reinforce the idea that multiple high throughput protein interactions technologies will be needed both for cross-validation, and to ensure a comprehensive collection of interaction data. Here we propose to use protein microarrays to validate protein interaction data derived from yeast two-hybrid and MS-based complex determinations, and to detect new protein interactions.
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