TY - JOUR
T1 - 3D-Beacons
T2 - Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources
AU - Varadi, Mihaly
AU - Nair, Sreenath
AU - Sillitoe, Ian
AU - Tauriello, Gerardo
AU - Anyango, Stephen
AU - Bienert, Stefan
AU - Borges, Clemente
AU - Deshpande, Mandar
AU - Green, Tim
AU - Hassabis, Demis
AU - Hatos, Andras
AU - Hegedus, Tamas
AU - Hekkelman, Maarten L.
AU - Joosten, Robbie
AU - Jumper, John
AU - Laydon, Agata
AU - Molodenskiy, Dmitry
AU - Piovesan, Damiano
AU - Salladini, Edoardo
AU - Salzberg, Steven L.
AU - Sommer, Markus J.
AU - Steinegger, Martin
AU - Suhajda, Erzsebet
AU - Svergun, Dmitri
AU - Tenorio-Ku, Luiggi
AU - Tosatto, Silvio
AU - Tunyasuvunakool, Kathryn
AU - Waterhouse, Andrew Mark
AU - Zídek, Augustin
AU - Schwede, Torsten
AU - Orengo, Christine
AU - Velankar, Sameer
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press GigaScience.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - bioinformatics
KW - experimentally determined structures computationally predicted structures
KW - federated data network
KW - structural biology
UR - http://www.scopus.com/inward/record.url?scp=85143181019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143181019&partnerID=8YFLogxK
U2 - 10.1093/gigascience/giac118
DO - 10.1093/gigascience/giac118
M3 - Article
C2 - 36448847
AN - SCOPUS:85143181019
SN - 2047-217X
VL - 11
JO - GigaScience
JF - GigaScience
M1 - giac118
ER -