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3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources.
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.
Afiliação
  • Varadi M; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SA, UK.
  • Nair S; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SA, UK.
  • Sillitoe I; Department of Structural and Molecular Biology, UCL, London WC1E 6BT, UK.
  • Tauriello G; Biozentrum, University of Basel, Basel 4056, Switzerland.
  • Anyango S; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland.
  • Bienert S; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SA, UK.
  • Borges C; Biozentrum, University of Basel, Basel 4056, Switzerland.
  • Deshpande M; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland.
  • Green T; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland.
  • Hassabis D; European Molecular Biology Laboratory, EMBL Hamburg, Hamburg 69117, Germany.
  • Hatos A; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SA, UK.
  • Hegedus T; DeepMind, London EC4A 3TW, UK.
  • Hekkelman ML; DeepMind, London EC4A 3TW, UK.
  • Joosten R; Department of Biomedical Sciences, University of Padova, Padova 35129, Italy.
  • Jumper J; Department of Oncology, Lausanne University Hospital, Lausanne 1015, Switzerland.
  • Laydon A; Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.
  • Molodenskiy D; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
  • Piovesan D; Swiss Cancer Center Leman, Lausanne 1005, Switzerland.
  • Salladini E; Department of Biophysics and Radiation Biology, Semmelweis University, Budapest 1094, Hungary.
  • Salzberg SL; Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands.
  • Sommer MJ; Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands.
  • Steinegger M; DeepMind, London EC4A 3TW, UK.
  • Suhajda E; DeepMind, London EC4A 3TW, UK.
  • Svergun D; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland.
  • Tenorio-Ku L; European Molecular Biology Laboratory, EMBL Hamburg, Hamburg 69117, Germany.
  • Tosatto S; Department of Biomedical Sciences, University of Padova, Padova 35129, Italy.
  • Tunyasuvunakool K; Department of Biomedical Sciences, University of Padova, Padova 35129, Italy.
  • Waterhouse AM; Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
  • Zídek A; Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
  • Schwede T; School of Biology, Seoul National University, Seoul 82-2-880-6971, 6977, South Korea.
  • Orengo C; Department of Biophysics and Radiation Biology, Semmelweis University, Budapest 1094, Hungary.
  • Velankar S; Computational Structural Biology, SIB Swiss Institute of Bioinformatics, Basel 4056, Switzerland.
Gigascience ; 112022 11 30.
Article em En | MEDLINE | ID: mdl-36448847
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 and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registros / Metadados Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registros / Metadados Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article