  A goal of computational studies of protein-protein interfaces (PPIs) is to
predict the binding site between two monomers that form a heterodimer. The
simplest version of this problem is to rigidly re-dock the bound forms of the
monomers, which involves generating computational models of the heterodimer and
then scoring them to determine the most native-like models. Scoring functions
have been assessed previously using rank- and classification-based metrics,
however, these methods are sensitive to the number and quality of models in the
scoring function training set. We assess the accuracy of seven PPI scoring
functions by comparing their scores to a measure of structural similarity to
the x-ray crystal structure (i.e. the DockQ score) for a non-redundant set of
heterodimers from the Protein Data Bank. For each heterodimer, we generate
re-docked models uniformly sampled over DockQ and calculate the Spearman
correlation between the PPI scores and DockQ. For some targets, the scores and
DockQ are highly correlated; however, for many targets, there are weak
correlations. Several physical features can explain the difference between
difficult- and easy-to-score targets. For example, strong correlations exist
between the score and DockQ for targets with highly intertwined monomers and
many interface contacts. We also develop a new score based on only three
physical features that matches or exceeds the performance of current PPI
scoring functions. These results emphasize that PPI prediction can be improved
by focusing on correlations between the PPI score and DockQ and incorporating
more discriminating physical features into PPI scoring functions.
