| URL: | https://repeatsdb.org |
| Full name: | A Database of Tandem Repeat Protein Annotations |
| Description: | RepeatsDB is a repository for the annotation and classification of structural tandem repeat proteins (STRPs). Each entry has start and end positions of the repeat region, repeat units, classification into four levels (Class, Topology, Fold, Clan), and in-depth characterization of the repeat regions. |
| Year founded: | 2014 |
| Last update: | 2024-09-15 |
| Version: | v4.0 |
| Accessibility: |
Accessible
|
| Country/Region: | Italy |
| Data type: | |
| Data object: |
NA
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| Database category: | |
| Major species: |
NA
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| Keywords: |
| University/Institution: | University of Padua |
| Address: | Department of Biomedical Sciences,University of Padua,35131 Padova,Italy |
| City: | Padova |
| Province/State: | Padua |
| Country/Region: | Italy |
| Contact name (PI/Team): | Alexander Miguel Monzon |
| Contact email (PI/Helpdesk): | repeatsdb@ngp-net.bio.unipd.it |
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RepeatsDB in 2025: expanding annotations of structured tandem repeats proteins on AlphaFoldDB. [PMID: 39475178]
RepeatsDB (URL: https://repeatsdb.org) stands as a key resource for the classification and annotation of Structured Tandem Repeat Proteins (STRPs), incorporating data from both the Protein Data Bank (PDB) and AlphaFoldDB. This latest release features substantial advancements, including annotations for over 34 000 unique protein sequences from >2000 organisms, representing a fifteenfold increase in coverage. Leveraging state-of-the-art structural alignment tools, RepeatsDB now offers faster and more precise detection of STRPs across both experimental and predicted structures. Key improvements also include a redesigned user interface and enhanced web server, providing an intuitive browsing experience with improved data searchability and accessibility. A new statistics page allows users to explore database metrics based on repeat classifications, while API enhancements support scalability to manage the growing volume of data. These advancements not only refine the understanding of STRPs but also streamline annotation processes, further strengthening RepeatsDB's role in advancing our understanding of STRP functions. |
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RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures. [PMID: 33237313]
The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures. |
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RepeatsDB 2.0: improved annotation, classification, search and visualization of repeat protein structures. [PMID: 27899671]
RepeatsDB 2.0 (URL: http://repeatsdb.bio.unipd.it/) is an update of the database of annotated tandem repeat protein structures. Repeat proteins are a widespread class of non-globular proteins carrying heterogeneous functions involved in several diseases. Here we provide a new version of RepeatsDB with an improved classification schema including high quality annotations for ?5400 protein structures. RepeatsDB 2.0 features information on start and end positions for the repeat regions and units for all entries. The extensive growth of repeat unit characterization was possible by applying the novel ReUPred annotation method over the entire Protein Data Bank, with data quality is guaranteed by an extensive manual validation for >60% of the entries. The updated web interface includes a new search engine for complex queries and a fully re-designed entry page for a better overview of structural data. It is now possible to compare unit positions, together with secondary structure, fold information and Pfam domains. Moreover, a new classification level has been introduced on top of the existing scheme as an independent layer for sequence similarity relationships at 40%, 60% and 90% identity. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. |
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RepeatsDB: a database of tandem repeat protein structures. [PMID: 24311564]
RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10,745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services. |