Computational Methods for Identifying Conserved Protein Complexes between Species from Protein Interaction Data

Abstract

Protein complexes conserved across species indicate processes that are core to cellular machinery (e.g. cell-cycle or DNA damage-repair complexes conserved across human and yeast). While numerous computational methods have been devised to identify complexes from the protein interaction (PPI) networks of individual species, these are severely limited by noise and errors (false positives) in currently available datasets. We identify conserved complexes by constructing an interolog network (IN) leveraging on the functional conservation of proteins between species through domain conservation (from Ensembl) in addition to sequence similarity. We employ `state-of-the-art? methods to cluster the interolog network, and map these clusters back to the original PPI networks to identify complexes conserved between the species. Our analysis revealed that the IN-construction removes several non-conserved interactions many of which are false positives, thereby improving complex prediction. Moreover, our method based on integrating domain conservation and sequence similarity to construct interolog networks helps to identify considerably more conserved complexes between the PPI networks from two species compared to direct complex prediction from the PPI networks.