Protein-protein interactions are at the basis of most cellular processes and crucial
for many bio-technological applications. During the last few years the development of
high-throughput technologies has produced several large-scale protein-interaction data
sets for various organisms and many interaction databases have been created by
data-mining techniques. It is well known that interactions can be mediated by the
presence of specific features, such as motifs, patches and domains. Even if many
efforts are underway to elucidate the role of these features in the regulation of
interaction networks, very little is known about them on a genome scale.
Data-integration and computational methods can be used to assign a confidence level
to specific interactions or datasets and to obtain information about the molecular
basis that regulate such interactions.
The MoVIN web server is a new bioinformatics resource for the analysis and
validation of protein interaction networks. It combines yeast protein interaction
data with other biological resources - such as sequences, process and component
ontologies, domains and structures - to construct a high-confidence interaction set
by identifying similar features in protein groups sharing a common interaction
partner. Such results are presented to the user with an integrated graphical
interface that offers the possibility of exploring the interaction network and to
access many biological-relevant data computed by the server or present in other
To assess the usefulness of our server, we analysed the presence of similar linear
motifs, functions, localization and domains in many different interaction datasets.
We observed a statistically significant presence of such features with respect to
random datasets and found that these information are consistent but non redundant.
Our study shows that the analysis of shared motifs in protein interaction networks
can be a valuable method to investigate the properties of interacting proteins and to
provide information that can be effectively integrated with other sources. As more
experimental interaction data become available, this method will be a useful tool to
gain a wider and more precise picture of the interactome.