Largest database of protein-to-protein interaction networks


Led by investigators at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard, an international research team has developed the largest database of protein-to-protein interaction networks, a resource that can illuminate how numerous disease-associated genes contribute to disease development and progression. The team reported the development of the network called InWeb_InBioMap (InWeb_IM) on Nov. 28th in Nature Methods.
It has been widely recognized that mapping large-scale interaction networks is important in understanding genomic data and the underlying mechanisms of gene groups and pathways that contribute to diseases. Recent experimental efforts have identified approximately 30,000 direct protein-protein interactions, albeit representing well under a quarter of the most conservative estimates of the total number of interactions. Lage's team, in collaboration with researchers in Denmark and the U.K., developed a computational framework to integrate data from more than 43,000 published articles, including data from eight established protein-protein interaction databases. They applied stringent quality control in creating InWeb_IM, which consisted of almost 586,000 interactions when the paper was submitted in February 2015 and now includes more than 625,500 interactions.
"Modern genetic technologies allow us to routinely sequence the genomes of people with, for example, cancer or psychiatric diseases, but understanding the cellular systems that are affected by disease-causing genetic variations remains a major challenge," says Kasper Lage, MGH Department of Surgery, project leader and co-corresponding author of the Nature Methods report. "Having more complete maps of the physical interactions of human proteins will enable us to start exploring cellular processes affected in disease at a higher resolution than is currently possible."
Lage adds, "The rapidly declining cost of genome sequencing has far outpaced our ability to interpret the gene variants we identify in patients with undiagnosed diseases. By exploring interaction networks at the level of proteins and of the genes that may be causing a disease, clinicians may begin to see patterns of genetic data that would otherwise be difficult to discern, which we illustrate in the article for cancers and autism. For example, around 30 genes appear to be involved in cardiomyopathies, but many individuals with the condition do not have mutations in any of those genes. By looking at interaction partners at the protein level of the 30 cardiomyopathy genes, we can start to identify new candidate genes based on the 'cardiomyopathy network,' potentially leading to new molecular insights into the disease. It is our hope that InWeb_IM can be a resource that contributes to interpreting clinical exome sequencing data and play a part in enabling clinical action in patients with an unknown cause of disease."
The team is continuing to develop ways of using InWeb_IM to explain large-scale genomic datasets and improve understanding of complex biological systems in a tissue-specific manner by integrating proteomic, transcriptomic and genomic data. In collaboration with several groups at MGH, the information will be applied to the understanding of cardiovascular diseases, birth defects, cancers, reproductive disorders and psychiatric disease. InWeb_IM will be maintained and updated quarterly and is fully accessible to academic users at or

Read more: Taibo Li, et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nature Methods, 2016; DOI:10.1038/nmeth.4083

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