AlphaFold

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AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure.[1] The program is designed as a deep learning system.[2]

AlphaFold software has had three major versions. A team of researchers that used AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. The program was particularly successful at predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing template structures were available from proteins with a partially similar sequence. A team that used AlphaFold 2 (2020) repeated the placement in the CASP14 competition in November 2020.[3] The team achieved a level of accuracy much higher than any other group.[2][4] It scored above 90 for around two-thirds of the proteins in CASP's global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to the lab experiment determined structure, with 100 being a complete match, within the distance cutoff used for calculating GDT.[2][5]

AlphaFold 2's results at CASP14 were described as "astounding"[6] and "transformational".[7] Some researchers noted that the accuracy is not high enough for a third of its predictions, and that it does not reveal the mechanism or rules of protein folding for the protein folding problem to be considered solved.[8][9] Nevertheless, there has been widespread respect for the technical achievement. On 15 July 2021 the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes.[10][11][12] The paper has since been cited more than 27 thousand times.

AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA, RNA, various ligands, and ions.[13]

Demis Hassabis and John Jumper from the team that developed AlphaFold won the Nobel Prize in Chemistry in 2024 for their work on “protein structure prediction”. The two had won the Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research earlier in 2023.[14][15]

  1. ^ "AlphaFold". Deepmind. Archived from the original on 19 January 2021. Retrieved 30 November 2020.
  2. ^ a b c "DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology". MIT Technology Review. Archived from the original on 2021-08-28. Retrieved 2020-11-30.
  3. ^ Shead, Sam (2020-11-30). "DeepMind solves 50-year-old 'grand challenge' with protein folding A.I." CNBC. Archived from the original on 2021-01-28. Retrieved 2020-11-30.
  4. ^ Stoddart, Charlotte (1 March 2022). "Structural biology: How proteins got their close-up". Knowable Magazine. doi:10.1146/knowable-022822-1. S2CID 247206999. Archived from the original on 7 April 2022. Retrieved 25 March 2022.
  5. ^ Robert F. Service, 'The game has changed.' AI triumphs at solving protein structures Archived 2023-06-24 at the Wayback Machine, Science, 30 November 2020
  6. ^ Cite error: The named reference AlQuraishiTweet was invoked but never defined (see the help page).
  7. ^ Cite error: The named reference :5 was invoked but never defined (see the help page).
  8. ^ Stephen Curry, No, DeepMind has not solved protein folding Archived 2022-07-29 at the Wayback Machine, Reciprocal Space (blog), 2 December 2020
  9. ^ Ball, Phillip (9 December 2020). "Behind the screens of AlphaFold". Chemistry World. Archived from the original on 15 August 2021. Retrieved 10 December 2020.
  10. ^ Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool, Kathryn; Bates, Russ; Žídek, Augustin; Potapenko, Anna; Bridgland, Alex; Meyer, Clemens; Kohl, Simon A A; Ballard, Andrew J; Cowie, Andrew; Romera-Paredes, Bernardino; Nikolov, Stanislav; Jain, Rishub; Adler, Jonas; Back, Trevor; Petersen, Stig; Reiman, David; Clancy, Ellen; Zielinski, Michal; Steinegger, Martin; Pacholska, Michalina; Berghammer, Tamas; Bodenstein, Sebastian; Silver, David; Vinyals, Oriol; Senior, Andrew W; Kavukcuoglu, Koray; Kohli, Pushmeet; Hassabis, Demis (2021-07-15). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
  11. ^ "GitHub - deepmind/alphafold: Open source code for AlphaFold". GitHub. Archived from the original on 2021-07-23. Retrieved 2021-07-24.
  12. ^ "AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2021-07-24. Retrieved 2021-07-24.
  13. ^ "AlphaFold 3 predicts the structure and interactions of all of life's molecules". Google. 2024-05-08. Archived from the original on 2024-05-09. Retrieved 2024-05-09.
  14. ^ Cite error: The named reference :4 was invoked but never defined (see the help page).
  15. ^ Cite error: The named reference :7 was invoked but never defined (see the help page).