Abstract
BackgroundProtein-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.ResultsWe 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.ConclusionThe 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.
Highlights
Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest
Validation of the scoring function The protein networks are composed of predicted individual interactions, each of which is assigned a score which indicates the strength of the prediction
Expression data has previously been put into a network context, for example by incorporating gene ontology data [15] and protein interactions [50], but here we generated the networks first, mapped the expression on top, and performed a clustering
Summary
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. Microarray experiments provide information about gene expression within the cells under study. Expression patterns can be uncovered from large-scale microarray data by systematically grouping genes with the help of clustering methods. Microarray experiments typically yield hundreds of significantly differentially-expressed genes, making it difficult to draw biological conclusions. Microarray experiments can show correlations between the expression of genes, they do not reveal the exact protein interaction mechanism. Protein-protein interactions are commonly studied using biochemical methods, and several different experimental methods are currently in use. Advances in other techniques, such as tandem-affinity purification and mass spectroscopy, have made large-scale studies increasingly feasible [7,8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
