An automated method for finding molecular complexes in large protein interaction networks

TLDR

Proteomics advances such as two‑hybrid, phage display, and mass spectrometry have produced detailed protein‑protein interaction maps, creating large datasets that require computational tools for storage, visualization, and analysis. This study introduces MCODE, a graph‑theoretic clustering algorithm that identifies densely connected protein‑protein interaction subgraphs potentially representing molecular complexes. MCODE assigns vertex weights based on local neighborhood density and expands from dense seed proteins outward, using directed traversal to isolate clusters and allowing fine‑tuning of cluster size and interconnectivity; it was evaluated on Saccharomyces cerevisiae interaction data and is available via FTP. The algorithm successfully identifies dense interaction regions that largely match known protein complexes and remains robust despite the high false‑positive rate of high‑throughput interaction data.

Abstract

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE .