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

Abstract

Interactomes embody one of the most effective representations of cellular behavior by revealing function through protein associations. In order to build these models at the organism scale, highthroughput techniques are required to identify interacting pairs of proteins. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate protein-protein interaction networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we describe a statistical framework, designated Y2HSCORES, for analyzing high-throughput Y2H screens that considers key aspects of experimental design, normalization, and controls. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS identified conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeastbased interaction screenings, accelerating the biological interpretation of experimental results. Proof-of-concept was demonstrated by discovery and validation of a novel interaction between the barley powdery mildew effector, AVRA13, with the vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1.

FINDER: An automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

“…Genome annotation is the process of identifying transcriptionally active regions of the genome and defining gene structures. Decoding the correct structures of genes is essential since several downstream applications rely on accurate annotations: detecting interactions between proteins [6][7][8][9][10][11][12][13][14], identifying post-translational modifications [15][16][17][18][19][20][21][22][23], mining effectors [24][25][26][27][28], and determining protein structure [29][30][31][32]. Although we have seen a significant improvement in genome sequencing technology, annotation methods continue to underperform [33,34].…”

Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information

“…In plants, the prediction of protein-protein interactions (PPIs) provides important information for understanding the molecular mechanisms underlying biological processes. Recently, a large number of high-throughput experimental approaches have been developed to identified PPIs, such as affinity-purification coupled to mass spectrometry (AP-MS) [1] and yeast two-hybrid (Y2H) [2][3][4][5] screens methods. Although we have accumulated a large amount of plant PPIs data [6][7][8], these experimental approaches also some inevitable drawbacks, which are not only costly, but also laborious and time-consuming.…”