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Screens for protein-protein interactions using assays like the yeast two-hybrid system have generated volumes of useful data. The protein interactions from these screens have been used to develop a better understanding of the functions of individual proteins, regulatory pathways, molecular machines, and entire biological systems. The value of this data, however, is limited by the inherent frequency of false positives that arise in most protein interaction screens. Appreciable numbers of false positives can crop up in both low-throughput and high-throughput screens, and even in screens that employ stringent criteria for defining a positive. A number of classification systems have been used to help distinguish false positives from biologically relevant true positives. This chapter describes a system for assigning a confidence score to each interaction based on the probability that it is a true positive. Such confidence scores can be used to prioritize interactions for validation. The scores are also useful for network analysis methods that take advantage of probabilistic edge weights. The scoring method does not rely on gold standard datasets of reliable true positives and true negatives, and thus circumvents the challenges associated with obtaining such datasets. Moreover, the scoring method uses data features that are largely assay-independent, making it useful for interactions obtained from a variety of different technologies and screening methods.

Charting the interactions among proteins is essential for understanding biological processes. While a number of complementary technologies for detecting protein interactions are available, the yeast two-hybrid system is one of the few that have been successfully scaled up. Two-hybrid screens have been used to construct extensive protein interaction maps for humans and several model organisms, and these maps have proven invaluable for studies on a variety of biological systems. These maps, however, have not come close to covering all proteins or interactions detectable by yeast two-hybrid. This is due in part to the difficulty of using library screening methods to sample all possible binary combinations of proteins. Ideally, every binary pair of proteins would be tested individually to ensure that every detectable interaction is identified. For organisms with large proteomes, however, this is not economically feasible and instead efficient pooling schemes must be implemented. The high-throughput two-hybrid screening methods presented here are designed to efficiently maximize coverage for selected sets of proteins or entire proteomes. We present two high-throughput screening protocols. Both methods are designed to identify interactors for any number of bait proteins expressed as DNA-binding domain (BD) fusions. The choice of which protocol to use depends largely on the nature of the available library of proteins fused to an activation domain (AD). The first protocol is appropriate for screening a library of AD clones, such as a cDNA library, a domain library, or a large pool of AD clones. By contrast, the second protocol is appropriate for screening a large array of individual sequence-verified AD clones. This protocol screens small pools of AD clones from the array in a two-phase scheme. Although the methods presented were developed using the LexA version of the yeast two-hybrid system, we include notes as appropriate to accommodate users of other versions.