AlphaFold (English Wikipedia)

McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21): 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.

arxiv.org

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21): 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.

blog.google

"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.

bloomberg.com

"DeepMind Breakthrough Helps to Solve How Diseases Invade Cells". Bloomberg.com. 2020-11-30. Archived from the original on 2022-04-05. Retrieved 2020-11-30.

chemh.com

"Beyond AlphaFold 3: Navigating Future Challenges in Protein Structure Prediction". 2024-05-10. Retrieved 2024-11-29.

chemistryworld.com

Ball, Phillip (9 December 2020). "Behind the screens of AlphaFold". Chemistry World. Archived from the original on 15 August 2021. Retrieved 10 December 2020.

cnbc.com

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.

cnn.com

edition.cnn.com

Hunt, Christian Edwards, Katie (9 October 2024). "Scientists who used AI to 'crack the code' of almost all proteins win Nobel Prize in chemistry". CNN. Archived from the original on 10 October 2024. Retrieved 9 October 2024.{{cite news}}: CS1 maint: multiple names: authors list (link)

deepmind.com

"AlphaFold". Deepmind. Archived from the original on 19 January 2021. Retrieved 30 November 2020.
"AlphaFold: Using AI for scientific discovery". Deepmind. 15 January 2020. Archived from the original on 2022-03-07. Retrieved 2020-11-30.
"AlphaFold: Using AI for scientific discovery". Deepmind. Archived from the original on 10 October 2024. Retrieved 30 November 2020.
"AlphaFold: a solution to a 50-year-old grand challenge in biology". Deepmind. 30 November 2020. Archived from the original on 30 November 2020. Retrieved 30 November 2020.
"Putting the power of AlphaFold into the world's hands". Deepmind. 22 July 2022. Archived from the original on 24 July 2021. Retrieved 24 July 2021.
"Computational predictions of protein structures associated with COVID-19". Deepmind. 4 August 2020. Archived from the original on 2022-03-25. Retrieved 2020-12-01.

doi.org

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.
Callaway, Ewen (2020-11-30). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. Bibcode:2020Natur.588..203C. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
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.
Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
Callaway, Ewen (13 April 2022). "What's next for AlphaFold and the AI protein-folding revolution". Nature. 604 (7905): 234–238. Bibcode:2022Natur.604..234C. doi:10.1038/d41586-022-00997-5. PMID 35418629. S2CID 248156195.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O’Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Singh, Arunima (2020). "Deep learning 3D structures". Nature Methods. 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733. S2CID 212403708.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 Archived 2022-01-22 at the Wayback Machine, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides Archived 2022-07-26 at the Wayback Machine
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13". Proteins: Structure, Function, and Bioinformatics. 87 (12). Wiley: 1165–1178. bioRxiv 10.1101/552422. doi:10.1002/prot.25697. ISSN 0887-3585. PMC 6800999. PMID 30985027.
Callaway, Ewen (2022-12-13). "After AlphaFold: protein-folding contest seeks next big breakthrough". Nature. 613 (7942): 13–14. doi:10.1038/d41586-022-04438-1. PMID 36513827. S2CID 254660427.
Callaway, Ewen (2022-07-28). "'The entire protein universe': AI predicts shape of nearly every known protein". Nature. 608 (7921): 15–16. Bibcode:2022Natur.608...15C. doi:10.1038/d41586-022-02083-2. PMID 35902752. S2CID 251159714.
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21): 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.
An, Hyun Joo; Froehlich, John W; Lebrilla, Carlito B (2009-10-01). "Determination of glycosylation sites and site-specific heterogeneity in glycoproteins". Current Opinion in Chemical Biology. Analytical Techniques/Mechanisms. 13 (4): 421–426. doi:10.1016/j.cbpa.2009.07.022. ISSN 1367-5931. PMC 2749913. PMID 19700364.
Hekkelman, Maarten L.; de Vries, Ida; Joosten, Robbie P.; Perrakis, Anastassis (February 2023). "AlphaFill: enriching AlphaFold models with ligands and cofactors". Nature Methods. 20 (2): 205–213. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
Dabrowski-Tumanski, Pawel; Stasiak, Andrzej (7 November 2023). "AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology". Molecules. 28 (22): 7462. doi:10.3390/molecules28227462. PMC 10672856. PMID 38005184.

ebi.ac.uk

alphafold.ebi.ac.uk

"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2021-07-24. Retrieved 2021-07-24.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 2022-07-29. Retrieved 2021-07-29.
"AlphaFold Protein Structure Database". alphafold.ebi.ac.uk. Archived from the original on 29 July 2022. Retrieved 27 July 2021.
"AlphaFold Protein Structure Database". www.alphafold.ebi.ac.uk. Archived from the original on 2022-08-02. Retrieved 2021-07-24.
"What use cases does AlphaFold not support?". AlphaFold Protein Structure Database. Archived from the original on 2022-07-29. Retrieved 2022-05-15.

economist.com

"DeepMind is answering one of biology's biggest challenges". The Economist. 2020-11-30. ISSN 0013-0613. Archived from the original on 2020-12-03. Retrieved 2020-11-30.

elpais.com

Domínguez, Nuño (2020-12-02). "La inteligencia artificial arrasa en uno de los problemas más importantes de la biología". El País (in Spanish). Archived from the original on 2022-07-26. Retrieved 2024-05-12.

fabianfuchsml.github.io

The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs

fastcompany.com

"DeepMind's latest AI breakthrough could turbocharge drug discovery". Fast Company. ISSN 1085-9241. Archived from the original on 2023-01-24. Retrieved 2023-01-24.

fold.it

AlphaFold: Machine learning for protein structure prediction Archived 2022-05-12 at the Wayback Machine, Foldit, 31 January 2020

foldingathome.org

e.g. Greg Bowman, Protein folding and related problems remain unsolved despite AlphaFold's advance Archived 2022-07-13 at the Wayback Machine, Folding@home blog, 8 December 2020

forbes.com

Knapp, Alex. "2023 Breakthrough Prizes Announced: Deepmind's Protein Folders Awarded $3 Million". Forbes. Archived from the original on 2024-05-09. Retrieved 2024-05-09.

fortune.com

Jeremy Kahn, Lessons from DeepMind's breakthrough in protein-folding A.I. Archived 2022-04-08 at the Wayback Machine, Fortune, 1 December 2020
"How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic". Fortune. Archived from the original on 2020-11-30. Retrieved 2020-12-01.

github.com

"GitHub - deepmind/alphafold: Open source code for AlphaFold". GitHub. Archived from the original on 2021-07-23. Retrieved 2021-07-24.
"deepmind/deepmind-research". GitHub. Archived from the original on 2022-02-01. Retrieved 2020-11-30.
google-deepmind/alphafold3, Google DeepMind, 2025-02-08, retrieved 2025-02-08

harvard.edu

ui.adsabs.harvard.edu

Callaway, Ewen (2020-11-30). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. Bibcode:2020Natur.588..203C. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
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.
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
Callaway, Ewen (13 April 2022). "What's next for AlphaFold and the AI protein-folding revolution". Nature. 604 (7905): 234–238. Bibcode:2022Natur.604..234C. doi:10.1038/d41586-022-00997-5. PMID 35418629. S2CID 248156195.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O’Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Callaway, Ewen (2022-07-28). "'The entire protein universe': AI predicts shape of nearly every known protein". Nature. 608 (7921): 15–16. Bibcode:2022Natur.608...15C. doi:10.1038/d41586-022-02083-2. PMID 35902752. S2CID 251159714.
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21): 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.

ccsp.hms.harvard.edu

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0

knowablemagazine.org

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.

maynoothuniversity.ie

mural.maynoothuniversity.ie

Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.

medium.com

timjph.medium.com

Tim Hubbard, The secret of life, part 2: the solution of the protein folding problem. Archived 2022-05-14 at the Wayback Machine, medium.com, 30 November 2020

moalquraishi.wordpress.com

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs

nature.com

Mohammed AlQuraishi (May 2019), AlphaFold at CASP13 Archived 2021-11-22 at the Wayback Machine, Bioinformatics, 35(22), 4862–4865 doi:10.1093/bioinformatics/btz422. See also Mohammed AlQuraishi (December 9, 2018), AlphaFold @ CASP13: "What just happened?" Archived 2022-07-29 at the Wayback Machine (blog post).
Mohammed AlQuraishi (15 January 2020), A watershed moment for protein structure prediction Archived 2022-06-23 at the Wayback Machine, Nature 577, 627–628 doi:10.1038/d41586-019-03951-0
Accurate structure prediction of biomolecular interactions with AlphaFold 3 Archived 2024-05-12 at the Wayback Machine, pdf of preprint of the article in Nature.
AlphaFold heralds a data-driven revolution in biology and medicine Archived 2024-10-10 at the Wayback Machine, by Janet M. Thornton, Roman A. Laskowski & Neera Borkakoti, Nature Medicine, volume 12, pages 1666–1669, 12 October 2021

newscientist.com

Michael Le Page, DeepMind's AI biologist can decipher secrets of the machinery of life Archived 2022-08-02 at the Wayback Machine, New Scientist, 30 November 2020
The predictions of DeepMind's latest AI could revolutionise medicine Archived 2021-11-07 at the Wayback Machine, New Scientist, 2 December 2020

nih.gov

pubmed.ncbi.nlm.nih.gov

Callaway, Ewen (2020-11-30). "'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures". Nature. 588 (7837): 203–204. Bibcode:2020Natur.588..203C. doi:10.1038/d41586-020-03348-4. PMID 33257889. S2CID 227243204.
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.
Jumper, John; et al. (August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. ISSN 1476-4687. PMC 8371605. PMID 34265844.
Callaway, Ewen (13 April 2022). "What's next for AlphaFold and the AI protein-folding revolution". Nature. 604 (7905): 234–238. Bibcode:2022Natur.604..234C. doi:10.1038/d41586-022-00997-5. PMID 35418629. S2CID 248156195.
Abramson, Josh; Adler, Jonas; Dunger, Jack; Evans, Richard; Green, Tim; Pritzel, Alexander; Ronneberger, Olaf; Willmore, Lindsay; Ballard, Andrew J.; Bambrick, Joshua; Bodenstein, Sebastian W.; Evans, David A.; Hung, Chia-Chun; O’Neill, Michael; Reiman, David (2024-05-08). "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature. 630 (8016): 493–500. Bibcode:2024Natur.630..493A. doi:10.1038/s41586-024-07487-w. ISSN 1476-4687. PMC 11168924. PMID 38718835.
Singh, Arunima (2020). "Deep learning 3D structures". Nature Methods. 17 (3): 249. doi:10.1038/s41592-020-0779-y. ISSN 1548-7105. PMID 32132733. S2CID 212403708.
Wei Zheng et al,Deep-learning contact-map guided protein structure prediction in CASP13 Archived 2022-01-22 at the Wayback Machine, Proteins: Structure, Function, and Bioinformatics, 87(12) 1149–1164 doi:10.1002/prot.25792; and slides Archived 2022-07-26 at the Wayback Machine
Hou, Jie; Wu, Tianqi; Cao, Renzhi; Cheng, Jianlin (2019-04-25). "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13". Proteins: Structure, Function, and Bioinformatics. 87 (12). Wiley: 1165–1178. bioRxiv 10.1101/552422. doi:10.1002/prot.25697. ISSN 0887-3585. PMC 6800999. PMID 30985027.
Callaway, Ewen (2022-12-13). "After AlphaFold: protein-folding contest seeks next big breakthrough". Nature. 613 (7942): 13–14. doi:10.1038/d41586-022-04438-1. PMID 36513827. S2CID 254660427.
Callaway, Ewen (2022-07-28). "'The entire protein universe': AI predicts shape of nearly every known protein". Nature. 608 (7921): 15–16. Bibcode:2022Natur.608...15C. doi:10.1038/d41586-022-02083-2. PMID 35902752. S2CID 251159714.
McBride, John M.; Polev, Konstantin; Abdirasulov, Amirbek; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi (2023-11-20). "AlphaFold2 Can Predict Single-Mutation Effects". Physical Review Letters. 131 (21): 218401. arXiv:2204.06860. Bibcode:2023PhRvL.131u8401M. doi:10.1103/PhysRevLett.131.218401. ISSN 0031-9007. PMID 38072605. Archived from the original on 2024-06-09. Retrieved 2023-11-26.
Bagdonas, Haroldas; Fogarty, Carl A.; Fadda, Elisa; Agirre, Jon (2021-10-29). "The case for post-predictional modifications in the AlphaFold Protein Structure Database" (PDF). Nature Structural & Molecular Biology. 28 (11): 869–870. doi:10.1038/s41594-021-00680-9. ISSN 1545-9985. PMID 34716446. S2CID 240228913. Archived (PDF) from the original on 2024-01-13. Retrieved 2024-01-13.
An, Hyun Joo; Froehlich, John W; Lebrilla, Carlito B (2009-10-01). "Determination of glycosylation sites and site-specific heterogeneity in glycoproteins". Current Opinion in Chemical Biology. Analytical Techniques/Mechanisms. 13 (4): 421–426. doi:10.1016/j.cbpa.2009.07.022. ISSN 1367-5931. PMC 2749913. PMID 19700364.
Hekkelman, Maarten L.; de Vries, Ida; Joosten, Robbie P.; Perrakis, Anastassis (February 2023). "AlphaFill: enriching AlphaFold models with ligands and cofactors". Nature Methods. 20 (2): 205–213. doi:10.1038/s41592-022-01685-y. PMC 9911346. PMID 36424442.
Dabrowski-Tumanski, Pawel; Stasiak, Andrzej (7 November 2023). "AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology". Molecules. 28 (22): 7462. doi:10.3390/molecules28227462. PMC 10672856. PMID 38005184.

ncbi.nlm.nih.gov

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See block diagram. Also John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 10
The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 12).
One design for a transformer network with SE(3)-equivariance was proposed in Fabian Fuchs et al SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Archived 2021-10-07 at the Wayback Machine, NeurIPS 2020; also website Archived 2022-07-03 at the Wayback Machine. It is not known how similar this may or may not be to what was used in AlphaFold.
See also the blog post Archived 2020-12-08 at the Wayback Machine by AlQuaraishi on this, or the more detailed post Archived 2022-07-03 at the Wayback Machine by Fabian Fuchs
John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slides 12 to 20
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