Protein-protein interface prediction is a booming field, with a substantial growth in the number of new methods being published the last two years. The increasing number of available three-dimensional structures of protein-protein complexes has enabled large-scale statistical analyses of protein interfaces, considering evolutionary, physicochemical and structural properties. Successful combinations of these properties have led to more accurate interface predictors in recent years.
In addition to parametric combination, machine learning algorithms have become popular. In the meantime, assessing the absolute and relative performance of interface predictors remains very difficult: This is due to differences in both the output of the various interface predictors, and in the evaluation criteria used by their respective authors.
This review provides an overview of the state of the art in the field, and discusses the performance of existing interface predictors. The focus is mainly on protein-protein interface prediction, although most issues are also valid for other kinds of interface prediction.