Alphafold Database Fetch And Analyze

references/citation.bib

@ARTICLE{Varadi2022,
  ABSTRACT = {The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.},
  AUTHOR = {Varadi, Mihaly and Anyango, Stephen and Deshpande, Mandar and Nair, Sreenath and Natassia, Cindy and Yordanova, Galabina and Yuan, David and Stroe, Oana and Wood, Gemma and Laydon, Agata and Žídek, Augustin and Green, Tim and Tunyasuvunakool, Kathryn and Petersen, Stig and Jumper, John and Clancy, Ellen and Green, Richard and Vora, Ankur and Lutfi, Mira and Figurnov, Michael and Cowie, Andrew and Hobbs, Nicole and Kohli, Pushmeet and Kleywegt, Gerard and Birney, Ewan and Hassabis, Demis and Velankar, Sameer},
  URL = {https://doi.org/10.1093/nar/gkab1061},
  DATE = {2022-01},
  DOI = {10.1093/nar/gkab1061},
  EPRINT = {https://academic.oup.com/nar/article-pdf/50/D1/D439/43502749/gkab1061.pdf},
  ISSN = {0305-1048},
  JOURNALTITLE = {Nucleic Acids Research},
  NUMBER = {D1},
  PAGES = {D439--D444},
  TITLE = {AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models},
  VOLUME = {50},
}

@ARTICLE{Varadi2024,
  ABSTRACT = {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.The AlphaFold Protein Structure Database (AlphaFold DB) is a massive digital library of predicted protein structures, with over 214 million entries, marking a 500-times expansion in size since its initial release in 2021. The structures are predicted using Google DeepMind's AlphaFold 2 artificial intelligence (AI) system. Our new report highlights the latest updates we have made to this database. We have added more data on specific organisms and proteins related to global health and expanded to cover almost the complete UniProt database, a primary data resource of protein sequences. We also made it easier for our users to access the data by directly downloading files or using advanced cloud-based tools. Finally, we have also improved how users view and search through these protein structures, making the user experience smoother and more informative. In short, AlphaFold DB has been growing rapidly and has become more user-friendly and robust to support the broader scientific community.},
  AUTHOR = {Varadi, Mihaly and Bertoni, Damian and Magana, Paulyna and Paramval, Urmila and Pidruchna, Ivanna and Radhakrishnan, Malarvizhi and Tsenkov, Maxim and Nair, Sreenath and Mirdita, Milot and Yeo, Jingi and Kovalevskiy, Oleg and Tunyasuvunakool, Kathryn and Laydon, Agata and Žídek, Augustin and Tomlinson, Hamish and Hariharan, Dhavanthi and Abrahamson, Josh and Green, Tim and Jumper, John and Birney, Ewan and Steinegger, Martin and Hassabis, Demis and Velankar, Sameer},
  URL = {https://doi.org/10.1093/nar/gkad1011},
  DATE = {2024-01},
  DOI = {10.1093/nar/gkad1011},
  EPRINT = {https://academic.oup.com/nar/article-pdf/52/D1/D368/55039845/gkad1011.pdf},
  ISSN = {0305-1048},
  JOURNALTITLE = {Nucleic Acids Research},
  NUMBER = {D1},
  PAGES = {D368--D375},
  TITLE = {AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences},
  VOLUME = {52},
}

@ARTICLE{Jumper2021,
  ABSTRACT = {Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1--4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence---the structure prediction component of the `protein folding problem'8---has been an important open research problem for more than 50 years9. Despite recent progress10--14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.},
  AUTHOR = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and Žídek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A. A. and Ballard, Andrew J. and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W. and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},
  URL = {https://doi.org/10.1038/s41586-021-03819-2},
  DATE = {2021-08-01},
  DOI = {10.1038/s41586-021-03819-2},
  ISSN = {1476-4687},
  JOURNALTITLE = {Nature},
  NUMBER = {7873},
  PAGES = {583--589},
  TITLE = {Highly accurate protein structure prediction with AlphaFold},
  VOLUME = {596},
}

@ARTICLE{Tunyasuvunakool2021,
  ABSTRACT = {Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17{\%} of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5{\%} of human proteins). The resulting dataset covers 58{\%} of residues with a confident prediction, of which a subset (36{\%} of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.},
  AUTHOR = {Tunyasuvunakool, Kathryn and Adler, Jonas and Wu, Zachary and Green, Tim and Zielinski, Michal and Žídek, Augustin and Bridgland, Alex and Cowie, Andrew and Meyer, Clemens and Laydon, Agata and Velankar, Sameer and Kleywegt, Gerard J. and Bateman, Alex and Evans, Richard and Pritzel, Alexander and Figurnov, Michael and Ronneberger, Olaf and Bates, Russ and Kohl, Simon A. A. and Potapenko, Anna and Ballard, Andrew J. and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Clancy, Ellen and Reiman, David and Petersen, Stig and Senior, Andrew W. and Kavukcuoglu, Koray and Birney, Ewan and Kohli, Pushmeet and Jumper, John and Hassabis, Demis},
  URL = {https://doi.org/10.1038/s41586-021-03828-1},
  DATE = {2021-08-01},
  DOI = {10.1038/s41586-021-03828-1},
  ISSN = {1476-4687},
  JOURNALTITLE = {Nature},
  NUMBER = {7873},
  PAGES = {590--596},
  TITLE = {Highly accurate protein structure prediction for the human proteome},
  VOLUME = {596},
}