Protein interaction networks are a principal component of a systems level description of the cell. Network topology has been clearly linked to protein function, expression dynamics and other genomic features. In particular, recent studies emphasized the importance of a proteins degree (number of interaction partners) and the role degree hubs play in the network. However, most network studies thus far operate on a relatively high level of abstraction and treat all proteins as simple nodes and all interactions as simple edges, neglecting the structural and chemical aspects of each interaction.
Here, we utilize atomic-resolution information from 3D protein structures to further characterize proteins and interactions in the network, thereby giving a chemical reality to nodes and edges. Having focused on direct, physical interactions only, we find that the resulting network topology shows marked differences to previously characterized network topologies and is much less dominated by hubs. Moreover, by differentiating between single-interface proteins and multi-interface proteins, we find that the relation of protein essentiality, expression correlation and a number of other genomic features with the degree is actually due to the number of interfaces, rather than the degree.
This differentiation also helps to resolve the current debate on the correlation between a proteins degree and its evolutionary rate, as only multi-interface hubs appear to evolve slower than the average protein. In fact, we can extend this result to show that a proteins evolutionary rate correlates best with the fraction of its surface involved in interactions. Finally, we show that current models of network evolution can only explain the topology of interactions sharing the same interface, while they fail to explain the growth of multi-interface proteins, and they likely need to be revisited. Since we only used a small fraction of the information contained in 3D structures, we can envisage a variety of immediate extensions, bringing a more structural and chemical understanding to protein networks.