3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources.
bioinformatics
experimentally determined structures computationally predicted structures
federated data network
structural biology
Journal
GigaScience
ISSN: 2047-217X
Titre abrégé: Gigascience
Pays: United States
ID NLM: 101596872
Informations de publication
Date de publication:
30 11 2022
30 11 2022
Historique:
received:
11
08
2022
revised:
20
09
2022
accepted:
11
11
2022
entrez:
30
11
2022
pubmed:
1
12
2022
medline:
3
12
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36448847
pii: 6854872
doi: 10.1093/gigascience/giac118
pmc: PMC9709962
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG006677
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM130151
Pays : United States
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/S020071/1
Pays : United Kingdom
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press GigaScience.
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