The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

In the last years, small-world behavior has been extensively described for proteins, when they are represented by the undirected graph defined by the inter-residue protein contacts. By adopting this representation it was possible to compute the average clustering coefficient (C) and characteristic path length (L) of protein structures, and their values were found to be similar to those of graphs characterized by small-world topology. In this comment, we analyze a large set of non-redundant protein structures (1753) and show that by randomly mimicking the protein collapse, the covalent structure of the protein chain significantly contributes to the small-world behavior of the inter-residue contact graphs. When protein graphs are generated, imposing constraints similar to those induced by the backbone connectivity, their characteristic path lengths and clustering coefficients are indistinguishable from those computed using the real contact maps showing that L and C values cannot be used for 'protein fingerprinting'. Moreover we verified that these results are independent of the selected protein representations, residue composition and protein secondary structures.

piero@biocomp.unibo.it.

Is there any reason why we should predict contact maps (CMs)? The question is one of the several 'NP-hard' questions that arise when striving for feasible solutions of the protein folding problem. At some point, theoreticians started thinking that a possible alternative to an unsolvable problem was to predict a simplified version of the protein structure: a CM. In this chapter, we will clarify that whenever problems are difficult they remain at least as difficult in the process of finding approximate solutions or heuristic approaches. However, humans rarely give up, as it is stimulating to find solutions in the face of difficulties. CMs of proteins are an interesting and useful representation of protein structures. These two-dimensional representations capture all the important features of a protein fold. We will review the general characteristics of CMs and the methods developed to study and predict them, and we will highlight some new ideas on how to improve CM predictions.