3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources
Open accessPreprint — published as 10.1093/gigascience/giac118 · Paper page
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 modelling 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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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
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- Authors
- Varadi, M., Nair, S., Sillitoe, I., Tauriello, G., Anyango, S., Bienert, S., Borges, C., Deshpande, M., Green, T., Hassabis, D., Hatos, A., Hegedus, T., Hekkelman, M. L., Joosten, R., Jumper, J., Laydon, A., Molodenskiy, D., Piovesan, D., Salladini, E., Salzberg, S. L., Sommer, M. J., Steinegger, M., Suhajda, E., Svergun, D., Tenorio-Ku, L., Tosatto, S., Tunyasuvunakool, K., Waterhouse, A. M., Žídek, A., Schwede, T., Orengo, C., & Velankar, S.
- Affiliations
- European Bioinformatics Institute (GB)
- University College London (GB)
- University of Basel (CH)
- European Molecular Biology Laboratory (DE)
- Google DeepMind (United Kingdom) (GB)
- The Netherlands Cancer Institute (NL)
- University of Padua (IT)
- Johns Hopkins University (US)
- Seoul National University (KR)
- Corresponding author
- Schwede, Torsten
- Orengo, Christine
- Velankar, Sameer
- ORCID
- 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
- Žídek, Augustin
- Schwede, Torsten
- Orengo, Christine
- Velankar, Sameer
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-08-03
- OpenAlex
- W4289637442
- Cited by
- 2
- References
- 3
- Citations by year
- 2023: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Research Foundation (2021-R1C1-C102065)
- National Research Foundation of Korea (2021-R1C1-C102065)
- Swiss Institute of Bioinformatics
- Biotechnology and Biological Sciences Research Council (BB/S020071/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein structure prediction, federated data network, comparative model evaluation, Protein Data Bank
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