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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbergiac118
JournalGigaScience
Volume11
DOIs
StatePublished - 2022
Externally publishedYes

Keywords

  • bioinformatics
  • experimentally determined structures computationally predicted structures
  • federated data network
  • structural biology

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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