Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis

• Published in: BMC bioinformatics 2006-01, Vol.7 (1), p.2-2, Article 2
• Main Authors: Jonsson, Pall F, Cavanna, Tamara, Zicha, Daniel, Bates, Paul A
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Artificial Intelligence; Biomarkers, Tumor - metabolism; Cell Line, Tumor; Chromosome Mapping - methods; Cluster Analysis; Diagnosis, Computer-Assisted - methods; Gene Expression Profiling - methods; Methodology; Neoplasm Proteins - metabolism; Pattern Recognition, Automated - methods; Protein Interaction Mapping - methods; Rats; Reproducibility of Results; Sarcoma - diagnosis; Sarcoma - metabolism; Sarcoma - secondary; Sensitivity and Specificity; Sequence Analysis, Protein - methods; Sequence Homology, Amino Acid; Signal Transduction
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-7-2

Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable. We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome--the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis. The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest.