Protein-protein interactions (PPIs) are vital for the function of a cell and the
evolution of these interactions produce much of the evolution of phenotype of an
organism. However, as the evolutionary process cannot be observed, methods are
required to infer evolution from existing data. An understanding of the resulting
evolutionary relationships between species can then provide information for PPI
prediction and function assignment.
This thesis further develops and applies the interaction tree method for modelling
PPI evolution within and between protein families. In this approach, a
phylogeny of the protein family/ies of interest is used to explicitly construct a history
of duplication and specification events. Given a model relating sequence change
in this phylogeny to the probability of a rewiring event occurring, this method can
then infer probabilities of interaction between the ancestral proteins described in
the phylogeny.
It is shown that the method can be adapted to infer the evolution of PPIs
within obligate protein complexes, using a large set of such complexes to validate
this application. This approach is then applied to reconstruct the history of the
proteasome complex, using x-ray crystallography structures of the complex as
input, with validation to show its utility in predicting present day complexes for
which we have no structural data.
The methodology is then adapted for application to transient PPIs. It is shown
that the approach used in the previous chapter is inadequate here and a new scoring
system is described based on a likelihood score of interaction. The predictive ability
of this score is shown in predicting known two component systems in bacteria and
its use in an interaction tree setting is demonstrated through inference of the
interaction history between the histidine kinase and response regulator proteins
responsible for sporulation onset in a set of bacteria.
This thesis demonstrates that with suitable modifications the interaction tree
approach is widely applicable to modelling PPI evolution and also, importantly,
predicting existing PPIs. This demonstrates the need to incorporate phylogenetic
data in to methods of predicting PPIs and gives some measure of the benefit in
doing so.