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Abstract: The detection of local similarities between protein structures may give insights for the identification of a common function. Different tools exist which try to elucidate the mechanisms connecting the similarity of local subsets of residues with the biological activity of whole proteins: i) databases of functionally annotated structures and ii) structural comparison algorithms. Both types of tools suffer from two major problems. The first is the low degree of integration among databases containing functional information. The second is the low or absent integration between existing methods for structural comparison and functional annotation resources.

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Abstract: We are working at the development of a neural network strategy for the prediction of peptide recognition specificity by SH3 domains. As a training set we use the results of a large number of SH3-peptide binding experiments obtained by the SPOT synthesis technique (PepSPOT). As input for the neural network, we consider the sequence of both the domain and the hypothetical ligand peptide, in order to infer for each domain peptide combination the likelihood that they form a complex in a binding reaction. The method will be applied to predict the affinity of any peptide for domains of unknown specificity. We analyzed data from PepSPOT experiments for nine SH3 domains each tested against several hundred peptides: we decided to construct a proper dataset where each data point includes the domain and peptide sequence, and a figure in arbitrary BLU units that correlates with binding affinity. In order to translate this information in a format that can be easily captured from a neural network, we focused on three main problems: i) the information coding; ii) the dimension of the input space; iii) the correct identification of the two classes (binding and not binding). We decided to use the orthogonal representation of the sequences and, in order to reduce the huge dimensionality, of the domains residues we only considered those positions that make contact with the ligand peptide. The contact positions are identified from the analysis of the SH3-peptide complexes of known structure and extended to other SH3 domains of known sequence by multiple alignment. For the peptide sequences we restricted our representation to the most significant positions, excluding the two consensus prolines from the input. Finally we identified the binding class considering all the peptides that show spot intensity higher than 10000 BLU units. The resulting dataset was strongly unbalanced and this implies the pursuit of different methodological strategies: usual feed-forward neural networks requires the balancing of the training set, while kernel methods (support vector machine) perform classification even on unbalanced sets but with the correct choice of a non-linear kernel. We will verify the performance of the neural strategy with respect to regular expressions, position weight matrices, position specific scoring matrices (PSSMs) and the SPOT procedure.

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Abstract: SH3 domains bind polyproline II peptides characterized by the PxxP consensus (P is proline and x in any amino acid). Single domain specificities display a preference for peptides within a range of variability on the common structural theme and different domains may interact with common peptides. We defined a new neural strategy to extract information from interacting partner sequences to improve the identification of SH3 domains specificity.

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Abstract: We developed a systematic large-scale approach to identifying protein surface regions sharing shape and residue similarity. We used a new fast structural comparison algorithm (LSC: Local Structure Comparison) to exhaustively analyze a set of functionally annotated protein patches with a larger collection of protein cavities. From a dataset of about 10.000 protein surface patches extracted from a non redundant list of PDB proteins (p-value=10-7), we collected a grand total of 65910 matches among patch pairs that were stored in the SURFACE database. The functional meaning of most of the matches could be confirmed by other established methods: the presence of the same PROSITE and ELM motifs in the sequence, the presence of the same ligand in the PDB structure, similar GO terms, common SWISS-PROT keywords, sequence similarity, same SCOP superfamily and E.C. numbers. We noticed that the fraction of matches whose functional association can be confirmed by more methods sensibly decreases with the extension of the match.

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Abstract: Reflecting the modular nature of eukaryotic proteins, several WWW servers (e.g. PFAM, SMART, PROSITE) are dedicated to revealing domains in protein sequences. However, there is no resource, which specifically focuses on short functional motifs (targeting peptides, docking modules, glycosylation sites, phosphorylation sites, etc), yet these modules are just as important for function as the larger protein domains. Domains are identified by conventional methods, such as patterns (regular expressions) profiles or HMM models. But statistically robust methods cannot usually be applied to small motifs, while pattern-based methods over-predict enormously so that the few true motifs are lost amongst the many false positives. ELM (Eucariotic Linear Motifs - http://elm.eu.org) [1] is a new web based tool for the prediction of these small motifs on eukaryotic protein sequences. At the moment, the ELM database contains manually curated information about 114 known linear motifs in the form of regular expressions, profiles or hidden markov models that identify the motifs on the sequence. ELM addresses the over prediction deficiency of other methods by the use of context-based rules and logical filters that exclude false positives. The current version of the ELM server provides core functionality including filtering by cell compartment, phylogeny, globular domain clash (using the SMART/Pfam databases), secondary structure, and solvent accessibility. The current set of motifs is not at all exhaustive. Filters work by comparing the information on the motifs stored in the db (taxonomic, structural and cellular context) with the information submitted by the user together with his sequence. The structural filter works by automatically modeling the submitted protein sequences, whenever a good template is found in the SCOP database, and comparing predicted solvent accessibility values and secondary structure features with the corresponding values associated to ELM matches on true positive structures. The ELM server was launched on November 2002 and regularly enhanced since then. The server activity has been running for several months at > 45,000 hits from > 1700 unique internet sites.

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