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.

MOTIVATION: The widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil domains are key steps towards the determination of the protein structure and function. Different tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods. RESULTS: In this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches an accuracy of 80% at the residue level and a best per-segment and per-protein efficiency of 81% and 80%, respectively. The results indicate that CCHMM_PROF outperforms all the existing tools and can be adopted for large-scale genome annotation. AVAILABILITY: The dataset is available at http://www.biocomp.unibo.it/ approximately lisa/coiled-coils. The predictor is freely available at http://gpcr.biocomp.unibo.it/cgi/predictors/cchmmprof/pred_cchmmprof.cgi. CONTACT: [email protected].

BACKGROUND: Peptidases are proteolytic enzymes responsible for fundamental cellular activities in all organisms. Apparently about 2-5% of the genes encode for peptidases, irrespectively of the organism source. The basic peptidase function is "protein digestion" and this can be potentially dangerous in living organisms when it is not strictly controlled by specific inhibitors. In genome annotation a basic question is to predict gene function. Here we describe a computational approach that can filter peptidases and their inhibitors out of a given proteome. Furthermore and as an added value to MEROPS, a specific database for peptidases already available in the public domain, our method can predict whether a pair of peptidase/inhibitor can interact, eventually listing all possible predicted ligands (peptidases and/or inhibitors). RESULTS: We show that by adopting a decision-tree approach the accuracy of PROSITE and HMMER in detecting separately the four major peptidase types (Serine, Aspartic, Cysteine and Metallo- Peptidase) and their inhibitors among a non redundant set of globular proteins can be improved by some percentage points with respect to that obtained with each method separately. More importantly, our method can then predict pairs of peptidases and interacting inhibitors, scoring a joint global accuracy of 99% with coverage for the positive cases (peptidase/inhibitor) close to 100% and a correlation coefficient of 0.91%. In this task the decision-tree approach outperforms the single methods. CONCLUSION: The decision-tree can reliably classify protein sequences as peptidases or inhibitors, belonging to a certain class, and can provide a comprehensive list of possible interacting pairs of peptidase/inhibitor. This information can help the design of experiments to detect interacting peptidase/inhibitor complexes and can speed up the selection of possible interacting candidates, without searching for them separately and manually combining the obtained results. A web server specifically developed for annotating peptidases and their inhibitors (HIPPIE) is available at http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi.