UniProt: the Universal Protein knowledgebase

To provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information, the Swiss‐Prot, TrEMBL and PIR protein database activities have united to form the Universal Protein Knowledgebase (UniProt) consortium. Our mission is to provide a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross‐references and query interfaces. The central database will have two sections, corresponding to the familiar Swiss‐Prot (fully manually curated entries) and TrEMBL (enriched with automated classification, annotation and extensive cross‐references). For convenient sequence searches, UniProt also provides several non‐redundant sequence databases. The UniProt NREF (UniRef) databases provide representative subsets of the knowledgebase suitable for efficient searching. The comprehensive UniProt Archive (UniParc) is updated daily from many public source databases. The UniProt databases can be accessed online (http://www.uniprot.org) or downloaded in several formats (ftp://ftp.uniprot.org/pub). The scientific community is encouraged to submit data for inclusion in UniPro

The Universal Protein Resource (UniProt)

The Universal Protein Resource (UniProt) provides the scientific community with a single, centralized, authoritative resource for protein sequences and functional information. Formed by uniting the Swiss-Prot, TrEMBL and PIR protein database activities, the UniProt consortium produces three layers of protein sequence databases: the UniProt Archive (UniParc), the UniProt Knowledgebase (UniProt) and the UniProt Reference (UniRef) databases. The UniProt Knowledgebase is a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase with extensive cross-references. This centrepiece consists of two sections: UniProt/Swiss-Prot, with fully, manually curated entries; and UniProt/TrEMBL, enriched with automated classification and annotation. During 2004, tens of thousands of Knowledgebase records got manually annotated or updated; we introduced a new comment line topic: TOXIC DOSE to store information on the acute toxicity of a toxin; the UniProt keyword list got augmented by additional keywords; we improved the documentation of the keywords and are continuously overhauling and standardizing the annotation of post-translational modifications. Furthermore, we introduced a new documentation file of the strains and their synonyms. Many new database cross-references were introduced and we started to make use of Digital Object Identifiers. We also achieved in collaboration with the Macromolecular Structure Database group at EBI an improved integration with structural databases by residue level mapping of sequences from the Protein Data Bank entries onto corresponding UniProt entries. For convenient sequence searches we provide the UniRef non-redundant sequence databases. The comprehensive UniParc database stores the complete body of publicly available protein sequence data. The UniProt databases can be accessed online (http://www.uniprot.org) or downloaded in several formats (ftp://ftp.uniprot.org/pub). New releases are published every two week

The Universal Protein Resource (UniProt)

The Universal Protein Resource (UniProt) provides the scientific community with a single, centralized, authoritative resource for protein sequences and functional information. Formed by uniting the Swiss-Prot, TrEMBL and PIR protein database activities, the UniProt consortium produces three layers of protein sequence databases: the UniProt Archive (UniParc), the UniProt Knowledgebase (UniProt) and the UniProt Reference (UniRef) databases. The UniProt Knowledgebase is a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase with extensive cross-references. This centrepiece consists of two sections: UniProt/Swiss-Prot, with fully, manually curated entries; and UniProt/TrEMBL, enriched with automated classification and annotation. During 2004, tens of thousands of Knowledgebase records got manually annotated or updated; we introduced a new comment line topic: TOXIC DOSE to store information on the acute toxicity of a toxin; the UniProt keyword list got augmented by additional keywords; we improved the documentation of the keywords and are continuously overhauling and standardizing the annotation of post-translational modifications. Furthermore, we introduced a new documentation file of the strains and their synonyms. Many new database cross-references were introduced and we started to make use of Digital Object Identifiers. We also achieved in collaboration with the Macromolecular Structure Database group at EBI an improved integration with structural databases by residue level mapping of sequences from the Protein Data Bank entries onto corresponding UniProt entries. For convenient sequence searches we provide the UniRef non-redundant sequence databases. The comprehensive UniParc database stores the complete body of publicly available protein sequence data. The UniProt databases can be accessed online (http://www.uniprot.org) or downloaded in several formats (ftp://ftp.uniprot.org/pub). New releases are published every two weeks

Novel CaLB-like Lipase Found Using ProspectBIO, a Software for Genome-Based Bioprospection

Enzymes have been highly demanded in diverse applications such as in the food, pharmaceutical, and industrial fuel sectors. Thus, in silico bioprospecting emerges as an efficient strategy for discovering new enzyme candidates. A new program called ProspectBIO was developed for this purpose as it can find non-annotated sequences by searching for homologs of a model enzyme directly in genomes. Here we describe the ProspectBIO software methodology and the experimental validation by prospecting for novel lipases by sequence homology to Candida antarctica lipase B (CaLB) and conserved motifs. As expected, we observed that the new bioprospecting software could find more sequences (1672) than a conventional similarity-based search in a protein database (733). Additionally, the absence of patent protection was introduced as a criterion resulting in the final selection of a putative lipase-encoding gene from Ustilago hordei (UhL). Expression of UhL in Pichia pastoris resulted in the production of an enzyme with activity towards a tributyrin substrate. The recombinant enzyme activity levels were 4-fold improved when lowering the temperature and increasing methanol concentrations during the induction phase in shake-flask cultures. Protein sequence alignment and structural modeling showed that the recombinant enzyme has high similarity and capability of adjustment to the structure of CaLB. However, amino acid substitutions identified in the active pocket entrance may be responsible for the differences in the substrate specificities of the two enzymes. Thus, the ProspectBIO software allowed the finding of a new promising lipase for biotechnological application without the need for laborious and expensive conventional bioprospecting experimental steps

How Large Is the Metabolome? A Critical Analysis of Data Exchange Practices in Chemistry

Calculating the metabolome size of species by genome-guided reconstruction of metabolic pathways misses all products from orphan genes and from enzymes lacking annotated genes. Hence, metabolomes need to be determined experimentally. Annotations by mass spectrometry would greatly benefit if peer-reviewed public databases could be queried to compile target lists of structures that already have been reported for a given species. We detail current obstacles to compile such a knowledge base of metabolites.As an example, results are presented for rice. Two rice (oryza sativa) subspecies have been fully sequenced, oryza japonica and oryza indica. Several major small molecule databases were compared for listing known rice metabolites comprising PubChem, Chemical Abstracts, Beilstein, Patent databases, Dictionary of Natural Products, SetupX/BinBase, KNApSAcK DB, and finally those databases which were obtained by computational approaches, i.e. RiceCyc, KEGG, and Reactome. More than 5,000 small molecules were retrieved when searching these databases. Unfortunately, most often, genuine rice metabolites were retrieved together with non-metabolite database entries such as pesticides. Overlaps from database compound lists were very difficult to compare because structures were either not encoded in machine-readable format or because compound identifiers were not cross-referenced between databases.We conclude that present databases are not capable of comprehensively retrieving all known metabolites. Metabolome lists are yet mostly restricted to genome-reconstructed pathways. We suggest that providers of (bio)chemical databases enrich their database identifiers to PubChem IDs and InChIKeys to enable cross-database queries. In addition, peer-reviewed journal repositories need to mandate submission of structures and spectra in machine readable format to allow automated semantic annotation of articles containing chemical structures. Such changes in publication standards and database architectures will enable researchers to compile current knowledge about the metabolome of species, which may extend to derived information such as spectral libraries, organ-specific metabolites, and cross-study comparisons

Enhancing navigation in biomedical databases by community voting and database-driven text classification

<p>Abstract</p> <p>Background</p> <p>The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them.</p> <p>Results</p> <p>Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly.</p> <p>Conclusion</p> <p>Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases.</p> <p>The system can be accessed at <url>http://pepbank.mgh.harvard.edu</url>.</p