Pairs of Physical Protein-Protein Interactions
predicted for Entire Proteomes

Protein interactomes predicted from evolutionary profiles

We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from, Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. The top predictions available here.

Abstract excerpt from "ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes" (Tran et al.)

What features do we offer


We made the 200,000 PPIs with the highest prediction confidence available for query.


The code will available through our github repository soon.


Download all predictions as .CSV files.


Get the numbers on proteins and PPIs.


ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

Linh Tran, Tobias Hamp, Burkhard Rost


Evolutionary profiles improve protein–protein interaction prediction from sequence

Tobias Hamp, Burkhard Rost (Bioinformatics, June 2015)


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