Functional topology in a network of protein interactions
Natasa Przulj1, Dennis Wigle2, Igor Jurisica
1natasha@cs.toronto.edu, Department of Computer Science, University of Toronto, Canada; 2, Department of Surgery, University of Toronto, Canada
Protein-protein interactions (PPI) are building blocks of biological
networks. The largest currently available PPI dataset is the S. cerevisiae
cumulative PPI network consisting of over 78,000 interactions between
5,321 proteins. We represented the PPI network as a graph, where nodes
correspond to proteins and edges connecting them are interactions.
We analyzed the network using diverse graph theory approaches to obtain
insight into the inner workings of cells and determine the
structure-function relationships. We integrated the results of this
analysis with existing functional annotation databases to construct
computational models for describing and predicting the properties of
lethal and viable mutations and proteins participating in genetic
interactions, functional groups, protein complexes, and signaling
pathways.
Our analysis suggests a distinct property of lethal mutations: they are
not only highly connected within the network, but also their removal
causes a disruption in network structure. In addition, we provide evidence
for the existence of alternate paths that bypass viable proteins in PPI
networks, while such paths do not exist for lethal mutations. We also
examined different functional classes of proteins and showed that they
have differing network properties. We demonstrated a way to extract and
predict protein complexes and signaling pathways. We have validated our
predictions by comparing them to a random model, and the accuracy of
predictions is assessed by analyzing their overlap with MIPS database.
The models that we developed provide a means for understanding the complex
wiring underlying cellular function, and enable us to predict
essentiality, genetic interaction, function, protein complexes and
cellular pathways. Results of our systematic analysis suggest that there
is a relationship between network structure and protein function.