Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

Abstract

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein’s neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.

Structural Perspectives on the Evolutionary Expansion of Unique Protein-Protein Binding Sites

“…In addition, Bacteria have many specific transcription regulators that are involved in the xenobiotics-resistance response, as in the case of the HTH_XRE superfamily. Consistent with our findings, it was previously shown that the node degree in interaction networks was higher for more ancient taxonomic nodes (41) and ancient proteins had high connectivity and centrality in protein interactomes (42). Moreover, it was observed that protein interaction networks partitioned into two subnetworks: one corresponding to the most ancient interactions and one related to the recently emerged interactions in animal evolution that are involved in cell division and cell communication (41).…”

Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics

“…Additionally, these models should be cognizant of the fact that selection acts at the level of populations of organisms and not individual genes or proteins. This detail is crucial because numerous observations in the genome architecture (beyond the distribution of protein folds), could also be explained by a largely nonadaptive mode of evolution [63,64]; in this view, population size is a crucial parameter.…”

Hidden long evolutionary memory in a model biochemical network

“…It has been suggested that protein networks evolve primarily by two biological mechanisms: (i) gene duplication and (ii) random mutations in proteins leading to neofunctionalization, that is, the de novo creation of new relationships with other proteins [24]. Our studies illustrate the significance of neofunctionalization in the context of functional networks where protein-protein interactions are physically grounded, i.e., described via quantitative interaction strengths rather than Boolean variables.…”