AlphaFold: Difference between revisions

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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" /><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" />.

==References==

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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" /><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" />.
 ==References==
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