Lisa Bartoli

Progress and challenges in predicting protein–protein interaction sites

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.

CCHMM_PROF: a HMM-based Coiled-Coil Predictor with Evolutionary Information

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 piero@biocomp.unibo.it.

The bologna annotation resource: a non hierarchical method for the functional and structural annotation of protein sequences relying on a comparative large-scale genome analysis.

Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availability of complete genomes and proteomes, protein annotation has recently taken invaluable advantage from cross-genome comparisons. In this work, we describe a new non hierarchical clustering procedure characterized by a stringent metric which ensures a reliable transfer of function between related proteins even in the case of multidomain and distantly related proteins. The method takes advantage of the comparative analysis of 599 completely sequenced genomes, both from prokaryotes and eukaryotes, and of a GO and PDB/SCOP mapping over the clusters. A statistical validation of our method demonstrates that our clustering technique captures the essential information shared between homologous and distantly related protein sequences. By this, uncharacterized proteins can be safely annotated by inheriting the annotation of the cluster. We validate our method by blindly annotating other 201 genomes and finally we develop BAR (the Bologna Annotation Resource), a prediction server for protein functional annotation based on a total of 800 genomes (publicly available at http://microserf.biocomp.unibo.it/bar/).

The Prediction of Protein-Protein Interacting Sites in Genome-Wide Protein Interaction Networks: The Test Case of the Human Cell Cycle

In this paper we aim at investigating possible correlations between the number of putative interaction patches of a given protein, as inferred by an algorithm that we have developed, and its degree (number of edges of the protein node in a protein interaction network). We focus on the human cell cycle that, as compared with other biological processes, comprises the largest number of proteins whose structure is known at atomic resolution both as monomers and as interacting complexes. For predicting interaction patches we specifically develop a HM-SVM based method reaching 71% overall accuracy with a correlation coefficient value equal to 0.43 on a non redundant set of protein complexes. To test the biological meaning of our predictions, we also explore whether interacting patches contain energetically important residues and/or disease related mutations and find that predicted patches are endowed with both features. Based on this, we propose that mapping the protein with all the predicted interaction patches bridges the molecule to the interactome at the cell level. To test our hypothesis we downloaded interaction data from interaction data bases and find that the number of predicted interaction patches significantly correlates (Pearson correlation value >0.3) with the number of the known interactions (edges) per protein in the human interactome, as contained in MINT and IntAct. We also show that the correlation increases (Pearson correlation value >0.5) when the subcellular co-localization and the co-expression levels of the interacting partners are taken into account.

Prediction of Protein-Protein Interacting Sites: How to Bridge Molecular Events to Large Scale Protein Interaction Networks

Most of the cellular functions are the result of the concerted action of protein complexes forming pathways and networks. For this reason, efforts were devoted to the study of protein-protein interactions. Large-scale experiments on whole genomes allowed the identification of interacting protein pairs. However residues involved in the interaction are generally not known and the majority of the interactions still lack a structural characterization. A crucial step towards the deciphering of the interaction mechanism of proteins is the recognition of their interacting surfaces, particularly in those structures for which also the most recent interaction network resources do not contain information. To this purpose, we developed a neural network-based method that is able to characterize protein complexes, by predicting amino acid residues that mediate the interactions. All the Protein Data Bank (PDB) chains, both in the unbound and in the complexed form, are predicted and the results are stored in a database of interaction surfaces (http://gpcr.biocomp.unibo.it/zenpatches). Finally, we performed a survey on the different computational methods for protein-protein interaction prediction and on their training/testing sets in order to highlight the most informative properties of protein interfaces.

Improving coiled-coil prediction with evolutionary information

The coiled-coil is a widespread protein structural motif known to have a stabilization function and to be involved in key interactions in cells and organisms. Here we show that it is possible to increase the prediction performance of an ab initio method by exploiting evolutionary information. We implement a new program (addressed here as PS-COILS) in order to take as input both single sequence and multiple sequence alignments. PS-COILS is introduced to define a baseline approach for benchmarking new coiled-coil predictors. We then design a new version of MARCOIL (a Hidden Markov Model based predictor) that can exploit evolutionary information in the form of sequence profiles. We show that the methods trained on sequence profiles perform better than the same methods only trained and tested on single sequence. Furthermore, we create a new structurally-annotated and freely-available dataset of coiled-coil structures (www.biocomp.unibo.it/ lisa/CC). The baseline method PS-COILS is available at www.plone4bio.org through subversion interface.

Topology prediction of membrane proteins: how distantly related homologs come into play

The first atomic-resolution structure of a membrane protein was solved in 1985. After 25 years and 213 more unique structures in the database, we learned some remarkable biophysical features that thanks to computational methods help us to model the topology of membrane proteins (White 2009). However, not all the features can be predicted with statistically relevant scores when few examples are available (Oberai et al. Protein Sci 15: 1723–1734, 2006). Too often the notion that similar functions are supported by similar structures is expanded far behind the limits of a safe sequence identity value (>50%) to select templates for modeling the membrane protein at hand. To select proper templates we introduce a strategy based on the notion that remote homologs can have a role in determining the structure of any given membrane protein provided that the two proteins are co-existing in a cluster. Sequences are clustered in a set provided that any two sequences share a sequence identity value ≥ 40% with a coverage ≥ 90% after cross-genome comparison. This procedure not only allows safe selection of a putative template but also filters out spurious assignments of templates even when they are generally considered as the structure reference to a given functional family. The strategy also can play a role in indicating which membrane protein sets still would be worthwhile a structural investigation effort. Possibly when more membrane proteins will be available, the clustering system will allow fold coverage of the membrane protein universe.