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 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.

Abstract: Abbiamo sviluppato (De Rinaldis et al., 1998) un programma per calcolatore in grado di generare un profilo tridimensionale a partire da una sovrapposizione di superfici proteiche che condividono la stessa funzione. L'analisi del profilo 3D ricavato da tale sovrapposizione consente: 1) lo studio dei determinanti strutturali di superficie associati ad una determinata funzione biologica; 2) la selezione di proteine con una determinata proprietà funzionale a partire da una banca dati di strutture; 3) l'identificazione di strutture proteiche da utilizzare come possibili "scaffold" di nuove funzioni in seguito a mutagenesi sito-specifica; 4) di indagare la possibilità di costruire due superfici proteiche con caratteristiche chimiche e funzionali simili su fold differenti e di analizzare il database di strutture note per vedere se la selezione naturale abbia mai esplorato questa opportunità attraverso una sorta di evoluzione convergente. Abbiamo applicato la procedura allo studio della tasca di legame dei domini SH2 e SH3 e della superficie associata alla struttura del p-loop. I profili 3D delle tasche di legame dei domini SH2 e SH3 riconoscono tutte le strutture degli SH2 e degli SH3 presenti nel dataset; il profilo associato al p-loop riconosce 17 delle 20 strutture proteiche contenenti un p-loop e presenti nel dataset. L'analisi del profilo 3D del p-loop ha consentito l'identificazione di una carica positiva e di una carica negativa conservata nello spazio, ma non nella sequenza. Stiamo applicando il metodo all'analisi dell'intero database delle proteine a struttura nota (PDB) per identificare funzioni definite eventualmente da pattern di sequenze diversi (motivi diversi nel database PROSITE (Bairoch, 1991; Bairoch et al., 1997), ma che possano invece essere riconosciute da un motivo di superficie unico, definito col metodo dei profili 3D.

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