Varadi, Mihaly;
Nair, Sreenath;
Sillitoe, Ian;
Tauriello, Gerardo;
Anyango, Stephen;
Bienert, Stefan;
Borges, Clemente;
... Velankar, Sameer; + view all
(2022)
3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources.
Gigascience
, 11
, Article giac118. 10.1093/gigascience/giac118.
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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.
| Type: | Article |
|---|---|
| Title: | 3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/gigascience/giac118 |
| Publisher version: | https://doi.org/10.1093/gigascience/giac118 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2022. Published by Oxford University Press GigaScience. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | bioinformatics, experimentally determined structures computationally predicted structures, federated data network, structural biology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10161335 |
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