PEASE: predicting B-cell epitopes utilizing antibody sequence.

UNLABELLED: Antibody epitope mapping is a key step in understanding antibody-antigen recognition and is of particular interest for drug development, diagnostics and vaccine design. Most computational methods for epitope prediction are based on properties of the antigen sequence and/or structure, not taking into account the antibody for which the epitope is predicted. Here, we introduce PEASE, a web server predicting antibody-specific epitopes, utilizing the sequence of the antibody. The predictions are provided both at the residue level and as patches on the antigen structure. The tradeoff between recall and precision can be tuned by the user, by changing the default parameters. The results are provided as text and HTML files as well as a graph, and can be viewed on the antigen 3D structure. AVAILABILITY AND IMPLEMENTATION: PEASE is freely available on the web at www.ofranlab.org/PEASE. CONTACT: yanay@ofranlab.org.

Co-expression and co-localization of hub proteins and their partners are encoded in protein sequence.

Spatiotemporal coordination is a critical factor in biological processes. Some hubs in protein-protein interaction networks tend to be co-expressed and co-localized with their partners more strongly than others, a difference which is arguably related to functional differences between the hubs. Based on numerous analyses of yeast hubs, it has been suggested that differences in co-expression and co-localization are reflected in the structural and molecular characteristics of the hubs. We hypothesized that if indeed differences in co-expression and co-localization are encoded in the molecular characteristics of the protein, it may be possible to predict the tendency for co-expression and co-localization of human hubs based on features learned from systematically characterized yeast hubs. Thus, we trained a prediction algorithm on hubs from yeast that were classified as either strongly or weakly co-expressed and co-localized with their partners, and applied the trained model to 800 human hub proteins. We found that the algorithm significantly distinguishes between human hubs that are co-expressed and co-localized with their partners and hubs that are not. The prediction is based on sequence derived features such as "stickiness", i.e. the existence of multiple putative binding sites that enable multiple simultaneous interactions, "plasticity", i.e. the existence of predicted structural disorder which conjecturally allows for multiple consecutive interactions with the same binding site and predicted subcellular localization. These results suggest that spatiotemporal dynamics is encoded, at least in part, in the amino acid sequence of the protein and that this encoding is similar in yeast and in human.

Assessing the relationship between conservation of function and conservation of sequence using photosynthetic proteins.

MOTIVATION: Assessing the false positive rate of function prediction methods is difficult, as it is hard to establish that a protein does not have a certain function. To determine to what extent proteins with similar sequences have a common function, we focused on photosynthesis-related proteins. A protein that comes from a non-photosynthetic organism is, undoubtedly, not involved in photosynthesis. RESULTS: We show that function diverges very rapidly: 70% of the close homologs of photosynthetic proteins come from non-photosynthetic organisms. Therefore, high sequence similarity, in most cases, is not tantamount to similar function. However, we found that many functionally similar proteins often share short sequence elements, which may correspond to a functional site and could reveal functional similarities more accurately than sequence similarity. CONCLUSIONS: These results shed light on the way biological function is conserved in evolution and may help improve large-scale analysis of protein function.

Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure.

Antibodies are capable of specifically recognizing and binding antigens. Identification of the antigen-binding site, commonly dubbed paratope, is of high importance both for medical and biological applications. To date, the identification of antigen-binding regions (ABRs) relies on tools for the identification of complementarity-determining regions (CDRs). However, we have shown that up to 22% of the residues that actually bind the antigen fall outside the traditionally defined CDRs. The Paratome web server predicts the ABRs of an antibody, given its amino acid sequence or 3D structure. It is based on a set of consensus regions derived from a structural alignment of a non-redundant set of all known antibody-antigen complexes. Given a query sequence or structure, the server identifies the regions in the query antibody that correspond to the consensus ABRs. An independent set of antibody-antigen complexes was used to test the server and it was shown to correctly identify at least 94% of the antigen-binding residues. The Paratome web server is freely available at http://www.ofranlab.org/paratome/.