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  • ClusterM: a scalable algorithm for computational prediction of conserved protein complexes across multiple protein interaction networks
Title: ClusterM: a scalable algorithm for computational prediction of conserved protein complexes across multiple protein interaction networks
Abstract Background The current computational methods on identifying conserved protein complexes across multiple Protein-Protein Interaction (PPI) networks suffer from the lack of explicit modeling of the desired topological properties within conserved protein complexes as well as their scalability. Results To overcome those issues, we propose a scalable algorithm—ClusterM—for identifying conserved protein complexes across multiple PPI networks through the integration of network topology and protein sequence similarity information. ClusterM overcomes the computational barrier that existed in previous methods, where the complexity escalates exponentially when handling an increasing number of PPI networks; and it is able to detect conserved protein complexes with both topological separability and cohesive protein sequence conservation. On two independent compendiums of PPI networks from Saccharomyces cerevisiae ( Sce , yeast), Drosophila melanogaster ( Dme , fruit fly), Caenorhabditis elegans ( Cel , worm), and Homo sapiens ( Hsa , human), we demonstrate that ClusterM outperforms other state-of-the-art algorithms by a significant margin and is able to identify de novo conserved protein complexes across four species that are missed by existing algorithms. Conclusions ClusterM can better capture the desired topological property of a typical conserved protein complex, which is densely connected within the complex while being well-separated from the rest of the networks. Furthermore, our experiments have shown that ClusterM is highly scalable and efficient when analyzing multiple PPI networks.  more » « less
Award ID(s):
1812641
PAR ID:
10233000
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
BMC Genomics
Volume:
21
Issue:
S10
ISSN:
1471-2164
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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