Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein-protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as several novel complex components. In addition to validating our predictions using orthogonal data, we were able to assign specific functional roles to the predicted complexes. In several cases, the incorporation of interaction data through network alignment allowed to distinguish real complex components from other orthologous proteins. Our analyses indicate that current knowledge of yeast protein complexes exceeds that in other organisms and that predicting complexes in fly based on human and yeast data is complementary rather than redundant. Lastly, assessing the conservation of protein complexes of the human pathogen Mycoplasma pneumoniae, we discovered that its complexes repertoire is different fromthat of eukaryotes, suggesting new points of therapeutic intervention, whereas targeting the pathogen's Restriction enzyme complex might lead to adverse effects due to its similarity to ATP-dependent metalloproteases in the human host. © 2014 Pache and Aloy.
Pache, R. A., & Aloy, P. (2014). Increasing the precision of orthology-based complex prediction through network alignment. PeerJ, 2014(1). https://doi.org/10.7717/peerj.413