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Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ∼130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal. [ABSTRACT FROM AUTHOR]

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards. We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of ∼10,000 × ∼10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at ∼116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins. [ABSTRACT FROM AUTHOR]

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks. [ABSTRACT FROM AUTHOR]

The article offers information on the binary protein map of the yeast interactome network. Recent yeast maps contain hundreds of molecular complexes with limited representation of direct binary interactions. A comparative quality assessment of current yeast interactome data sets manifests that high-throughput yeast two hybrid (Y2H) screening provides high-quality binary interaction information. A mapping framework is developed to produce a high-quality Y2H data set, for a large fraction of the yeast binary interactome remains to be mapped. Both Y2H and affinity purification data are of equally high quality but of a different and complementary networks. The binary map is improved for transient signaling interactions and intercomplex connections with a clustering between essential proteins.

Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping. [ABSTRACT FROM AUTHOR]