EEPPIBM: Exploring the evolution of protein-protein interactions and their networks using biophysical models


The evolution of protein interactions has produced interaction networks and much biological complexity. Although molecular phylogenetics reveals the end points of evolutionary searches, little is known about the trajectories of interacting proteins through sequence space over evolutionary time. A major bottleneck is the inability to extensively map how binding affinity changes with sequence. Prior to the developement of this project a  previously developed affinity prediction model was modified to predict affinity changes upon mutation. Preliminary results showed that this method has unprecedented speed and accuracy.

During the project, the preliminary models were improved and thoroughly validated. Subsequently, they were used to assess millions of combinations of interface mutations in structurally annotated interaction networks. The mutational robustness of interactions was determined, compared with theoretical models, and its relationships with observed evolutionary rates investigated at the amino acid, interface, protein and network levels. Then, paths connecting orthologs to each other and to ancestral protein reconstructions were determined and characterised. The positions on the phylogenetic trees where the affinity imbuing contacts are gained were determined, as well as the positions where interactions are lost, revealing network rewiring. This illuminated the changes in interactions over time, and showed how interface biophysics constrains the functionally viable paths available for interaction evolution. The relationships between extent of constraint, evolutionary rates and network properties were investigated.

We hypothesised that the inherent ability to form interactions and modulate specificity could be explain a number of observations garnered from network evolution studies. Finally, the human-virus exogenous interaction network were investigated, to potentially reveal strategies and counter strategies employed as viruses vie to hijack regulatory mechanisms.