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3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Cited 4 time in Web of Science Cited 10 time in Scopus
Authors

Varadi, Mihaly; Nair, Sreenath; Sillitoe, Ian; Tauriello, Gerardo; Anyango, Stephen; Bienert, Stefan; Borges, Clemente; Deshpande, Mandar; Green, Tim; Hassabis, Demis; Hatos, Andras; Hegedus, Tamas; Hekkelman, Maarten L; Joosten, Robbie; Jumper, John; Laydon, Agata; Molodenskiy, Dmitry; Piovesan, Damiano; Salladini, Edoardo; Salzberg, Steven L; Sommer, Markus J; Steinegger, Martin; Suhajda, Erzsebet; Svergun, Dmitri; Tenorio-Ku, Luiggi; Tosatto, Silvio; Tunyasuvunakool, Kathryn; Waterhouse, Andrew Mark; Zídek, Augustin; Schwede, Torsten; Orengo, Christine; Velankar, Sameer

Issue Date
2022-01
Publisher
BioMed Central
Citation
GigaScience, Vol.11, p. giac118
Abstract
While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files andmetadata for experimentally determined and theoretical proteinmodels fromstate-of-the-art and specialistmodel providers and also from the Protein Data Bank.
ISSN
2047-217X
URI
https://hdl.handle.net/10371/202540
DOI
https://doi.org/10.1093/gigascience/giac118
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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