AlphaFold
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In 2020, the '''AlphaFold2'''<ref name="senior202001">PMID: 31942072</ref><ref name="alphafoldwikipedia">[https://en.wikipedia.org/wiki/AlphaFold AlphaFold] at Wikipedia.</ref> system of [http://deepmind.com DeepMind]<ref name="deepmindblog">[https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology AlphaFold: a solution to a 50-year-old grand challenge in biology], DeepMind Blog, November 30, 2020.</ref><ref name="deepmindwikipedia">[https://en.wikipedia.org/wiki/DeepMind DeepMind] at Wikipedia.</ref> demonstrated a '''major breakthrough'''<ref name="alquraishi">[https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/ AlphaFold2 @ CASP14: “It feels like one’s child has left home.”] by Mohammed AlQuraishi, December 8, 2020.</ref><ref name="casppressrelease">[https://predictioncenter.org/casp14/doc/CASP14_press_release.html Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research], CASP Press Release, November 30, 2020.</ref><ref name="callaway" /><ref name="helliwell">[https://www.iucr.org/news/newsletter/volume-28/number-4/deepmind-and-casp14 DeepMind and CASP14] by John R. Helliwell, International Union of Crystallography Newsletter, December 4, 2020.</ref>. At [[Theoretical_models#2020:_CASP_14|CASP14]], AlphaFold2 was far better able, among over 100 competing groups, to '''predict structures, including sidechain positions''', so close to the subsequently revealed X-ray crystallographic structures as to differ by little more than the differences between two independently-determined X-ray structures of the same molecule. It did this for about two-thirds of the targets in the competition. AlphaFold2 has been hailed as '''largely solving the protein structure prediction problem for single-chain proteins'''<ref name="alquraishi" /><ref name="casppressrelease" /><ref name="callaway">PMID: 33257889</ref><ref name="helliwell" />. "Never in my life had I expected to see a scientific advance so rapid." said Mohammed AlQuraishi of Columbia University<ref name="alquraishi" />. | In 2020, the '''AlphaFold2'''<ref name="senior202001">PMID: 31942072</ref><ref name="alphafoldwikipedia">[https://en.wikipedia.org/wiki/AlphaFold AlphaFold] at Wikipedia.</ref> system of [http://deepmind.com DeepMind]<ref name="deepmindblog">[https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology AlphaFold: a solution to a 50-year-old grand challenge in biology], DeepMind Blog, November 30, 2020.</ref><ref name="deepmindwikipedia">[https://en.wikipedia.org/wiki/DeepMind DeepMind] at Wikipedia.</ref> demonstrated a '''major breakthrough'''<ref name="alquraishi">[https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/ AlphaFold2 @ CASP14: “It feels like one’s child has left home.”] by Mohammed AlQuraishi, December 8, 2020.</ref><ref name="casppressrelease">[https://predictioncenter.org/casp14/doc/CASP14_press_release.html Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research], CASP Press Release, November 30, 2020.</ref><ref name="callaway" /><ref name="helliwell">[https://www.iucr.org/news/newsletter/volume-28/number-4/deepmind-and-casp14 DeepMind and CASP14] by John R. Helliwell, International Union of Crystallography Newsletter, December 4, 2020.</ref>. At [[Theoretical_models#2020:_CASP_14|CASP14]], AlphaFold2 was far better able, among over 100 competing groups, to '''predict structures, including sidechain positions''', so close to the subsequently revealed X-ray crystallographic structures as to differ by little more than the differences between two independently-determined X-ray structures of the same molecule. It did this for about two-thirds of the targets in the competition. AlphaFold2 has been hailed as '''largely solving the protein structure prediction problem for single-chain proteins'''<ref name="alquraishi" /><ref name="casppressrelease" /><ref name="callaway">PMID: 33257889</ref><ref name="helliwell" />. "Never in my life had I expected to see a scientific advance so rapid." said Mohammed AlQuraishi of Columbia University<ref name="alquraishi" />. | ||
| - | See [[Theoretical_models#2020:_CASP_14]] for more about the initial demonstration at CASP14. [[AlphaFold2_examples_from_CASP_14]] describes a detailed analysis of two of the CASP14 predictions. | + | See [[Theoretical_models#2020:_CASP_14]] for more about the initial demonstration at CASP14, and the reactions to it. [[AlphaFold2_examples_from_CASP_14]] describes a detailed analysis of two of the CASP14 predictions. |
==References== | ==References== | ||
<references /> | <references /> | ||
Revision as of 22:09, 25 July 2021
In 2020, the AlphaFold2[1][2] system of DeepMind[3][4] demonstrated a major breakthrough[5][6][7][8]. At CASP14, AlphaFold2 was far better able, among over 100 competing groups, to predict structures, including sidechain positions, so close to the subsequently revealed X-ray crystallographic structures as to differ by little more than the differences between two independently-determined X-ray structures of the same molecule. It did this for about two-thirds of the targets in the competition. AlphaFold2 has been hailed as largely solving the protein structure prediction problem for single-chain proteins[5][6][7][8]. "Never in my life had I expected to see a scientific advance so rapid." said Mohammed AlQuraishi of Columbia University[5].
See Theoretical_models#2020:_CASP_14 for more about the initial demonstration at CASP14, and the reactions to it. AlphaFold2_examples_from_CASP_14 describes a detailed analysis of two of the CASP14 predictions.
References
- ↑ Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Zidek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature. 2020 Jan;577(7792):706-710. doi: 10.1038/s41586-019-1923-7. Epub 2020 Jan, 15. PMID:31942072 doi:http://dx.doi.org/10.1038/s41586-019-1923-7
- ↑ AlphaFold at Wikipedia.
- ↑ AlphaFold: a solution to a 50-year-old grand challenge in biology, DeepMind Blog, November 30, 2020.
- ↑ DeepMind at Wikipedia.
- ↑ 5.0 5.1 5.2 AlphaFold2 @ CASP14: “It feels like one’s child has left home.” by Mohammed AlQuraishi, December 8, 2020.
- ↑ 6.0 6.1 Artificial intelligence solution to a 50-year-old science challenge could ‘revolutionise’ medical research, CASP Press Release, November 30, 2020.
- ↑ 7.0 7.1 Callaway E. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020 Dec;588(7837):203-204. doi: 10.1038/d41586-020-03348-4. PMID:33257889 doi:http://dx.doi.org/10.1038/d41586-020-03348-4
- ↑ 8.0 8.1 DeepMind and CASP14 by John R. Helliwell, International Union of Crystallography Newsletter, December 4, 2020.
