  Understanding the molecular determinants of specificity in protein-protein
interaction is an outstanding challenge of postgenome biology. The availability
of large protein databases generated from sequences of hundreds of bacterial
genomes enables various statistical approaches to this problem. In this context
covariance-based methods have been used to identify correlation between amino
acid positions in interacting proteins. However, these methods have an
important shortcoming, in that they cannot distinguish between directly and
indirectly correlated residues. We developed a method that combines covariance
analysis with global inference analysis, adopted from use in statistical
physics. Applied to a set of >2,500 representatives of the bacterial
two-component signal transduction system, the combination of covariance with
global inference successfully and robustly identified residue pairs that are
proximal in space without resorting to ad hoc tuning parameters, both for
heterointeractions between sensor kinase (SK) and response regulator (RR)
proteins and for homointeractions between RR proteins. The spectacular success
of this approach illustrates the effectiveness of the global inference approach
in identifying direct interaction based on sequence information alone. We
expect this method to be applicable soon to interaction surfaces between
proteins present in only 1 copy per genome as the number of sequenced genomes
continues to expand. Use of this method could significantly increase the
potential targets for therapeutic intervention, shed light on the mechanism of
protein-protein interaction, and establish the foundation for the accurate
prediction of interacting protein partners.
