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Mihály Váradi, Damian Bertoni, Paulyna Magaña, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim I. Tsenkov, Sreenath Sasidharan Nair, Milot Mirdita, Jingi Yeo and 13 more
The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.
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Váradi, M., Bertoni, D., Magaña, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M. I., Nair, S. S., Mirdita, M., Yeo, J., Kovalevskiy, O. V., Tunyasuvunakool, K., Laydon, A., Žídek, A., Tomlinson, H., Hariharan, D., Abrahamson, J., Green, T., Jumper, J., … Velankar, S. S. (2023). AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Research, 52(D1), D368–D375. https://doi.org/10.1093/nar/gkad1011
Váradi, Mihály, et al. "AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences." Nucleic Acids Research, vol. 52, no. D1, 2023, pp. D368–D375, https://doi.org/10.1093/nar/gkad1011.
Váradi, Mihály, Damian Bertoni, Paulyna Magaña, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim I. Tsenkov, et al. "AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences." Nucleic Acids Research 52, no. D1 (2023): D368–D375. https://doi.org/10.1093/nar/gkad1011.
Váradi M., Bertoni D., Magaña P. [и др.] AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences // Nucleic Acids Research. — 2023. — Vol. 52, № D1. — P. D368–D375. — URL: https://doi.org/10.1093/nar/gkad1011
@article{varadi2023alphafold,
title = {{AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences}},
author = {Mihály Váradi and Damian Bertoni and Paulyna Magaña and Urmila Paramval and Ivanna Pidruchna and Malarvizhi Radhakrishnan and Maxim I. Tsenkov and Sreenath Sasidharan Nair and Milot Mirdita and Jingi Yeo and Oleg V. Kovalevskiy and Kathryn Tunyasuvunakool and Agata Laydon and Augustin Žídek and Hamish Tomlinson and Dhavanthi Hariharan and Josh Abrahamson and Tim Green and John Jumper and Ewan Birney and Martin Steinegger and Demis Hassabis and Sameer S Velankar},
journal = {Nucleic Acids Research},
year = {2023},
volume = {52},
number = {D1},
pages = {D368--D375},
doi = {10.1093/nar/gkad1011},
url = {https://doi.org/10.1093/nar/gkad1011}
}TY - JOUR AU - Váradi, Mihály AU - Bertoni, Damian AU - Magaña, Paulyna AU - Paramval, Urmila AU - Pidruchna, Ivanna AU - Radhakrishnan, Malarvizhi AU - Tsenkov, Maxim I. AU - Nair, Sreenath Sasidharan AU - Mirdita, Milot AU - Yeo, Jingi AU - Kovalevskiy, Oleg V. AU - Tunyasuvunakool, Kathryn AU - Laydon, Agata AU - Žídek, Augustin AU - Tomlinson, Hamish AU - Hariharan, Dhavanthi AU - Abrahamson, Josh AU - Green, Tim AU - Jumper, John AU - Birney, Ewan AU - Steinegger, Martin AU - Hassabis, Demis AU - Velankar, Sameer S TI - AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences JO - Nucleic Acids Research VL - 52 IS - D1 SP - D368 EP - D375 PY - 2023 DA - 2023/10/18 DO - 10.1093/nar/gkad1011 UR - https://doi.org/10.1093/nar/gkad1011 ER -
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