DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions

Abstract

Summary We adapted the yeast 2-hybrid assay to simultaneously uncover multiple transient protein interactions within a single screen by using a strategy termed DEEPN (dynamic enrichment for evaluation of protein networks). This approach incorporates high-throughput DNA sequencing and computation to follow competition among a plasmid population encoding interacting partners. To demonstrate the capacity of DEEPN, we identify a wide range of ubiq-uitin-binding proteins, including interactors that we verify biochemically. To demonstrate the specificity of DEEPN, we show that DEEPN allows simultaneous comparison of candidate interactors across multiple bait proteins, allowing differential interactions to be identified. This feature was used to identify interactors that distinguish between GTP- and GDP-bound conformations of Rab5.

Saccharomyces cerevisiae: A Unicellular Model Genetic Organism of Enduring Importance

“…The Gal4 protein also contains an activation domain (AD) that interacts with the mediator complex and helps promote transcription of genes controlled by UAS GAL sequences (Traven et al, 2006). Recently Y2H has been adapted for use in an exquisitely sensitive, high-throughput proteinprotein interaction assay called DEEPN (dynamic enrichment for evaluation of protein networks; Pashkova et al, 2016). As depicted in Figure 3, if the bait and prey proteins interact, the resulting hybrid transcription factor will drive expression of a reporter gene, which is under control of the GAL1 promoter.…”

Y2H-SCORES: A statistical framework to infer protein-protein interactions from next-generation yeast-two-hybrid sequence data

“…Thus, to optimize genomescale screens and obtain interactome data in a time-efficient manner, more recent approaches, collectively termed next-generation interaction screening (NGIS), use deep sequencing to score the result of Y2H screens. These innovations facilitate quantitative measures of bait-prey interactions, and importantly, do not require open-reading-frame sequence libraries from the organism(s) of interest (10)(11)(12)(13)(14)(15).…”

“…2) There is no consensus regarding what control(s) are more appropriate to signify the background interactivity of the preys and to help to distinguish true interactions (10)(11)(12)(13)(14)(16)(17)(18). 3) Despite its importance, most existing methods do not assess data normalization, or implement inappropriate normalization methods for Y2H-NGIS data (11)(12)(13)(14)16,18). High-throughput sequencing datasets, where read counts quantify signal strength, e.g., RNA-Seq, require normalization, as there are external factors, aside from experimental treatments, that influence read counts (19).…”

“…Most if not all prey will therefore be DE in the selected condition. Finally, 4) with no consensus on the appropriate data analysis, nor even how to report the results, whether ratios of counts, log fold-change from DE analysis, or a custom score function, it is nearly impossible to compare Y2H-NGIS studies, and there are reports that Y2H-NGIS-based interaction predictions suffer high false positive rates (10)(11)(12)(13)(14)(16)(17)(18). Hence, there is a need for robust and consistent statistical models that make use of all the available information in Y2H-NGIS data.…”

“…We optimized the protocol proposed by Pashkova and colleagues (11) to sub-culture diploid yeast populations that carry bait and prey plasmids under two batch conditions: 1) diploid growth obtained in what we call the non-selected condition (SC-Leu-Trp), used as background level of library prey abundance and 2) interaction test, or the selected condition (SC-Leu-Trp-His), which theoretically only allow the growth of diploid populations with positive bait-prey interactions through activation of the reporter gene. Diploids that grow under selection comprise a group of preys that can be split into true and false interactors.…”