HINT: High-quality protein interactomes and their applications in understanding human disease

Abstract

Background: A global map of protein-protein interactions in cellular systems provides key insights into the workings of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses.

Results: We develop HINT (http://hint.yulab.org) - a database of high-quality protein-protein interactomes for human, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Oryza sativa. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders.

Conclusions: HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.

Figure 1

Flow diagram depicting the series of steps used to build HINT.

Figure 2

Binary and co-complex interactomes and degree distribution plots for human and S. cerevisiae.

Figure 3

Average overlap percentage between all pairs of databases for binary and co-complex interactions in human, S. cerevisiae (S.c.), and S. pombe (S.p.) before and after filtering.

Figure 4

Screenshot of the user interface of HINT.

Figure 5

A. Percentage of disease genes within proteins that have 0, 1, 2, 3, and > =4 interactions respectively. B. Plot of average number of publications associated with a protein versus the cumulative degree of the protein in the HT and LC interaction networks in human. C. Plot of average number of publications associated with a protein versus the cumulative degree of the protein in the HT and LC interaction networks in S. cerevisiae. D. Percentage of disease hubs within disease genes that have 0,1, and > =2 interactions respectively.