Theoretical models: Difference between revisions
From Proteopedia
Jump to navigationJump to search
Eric Martz (talk | contribs) |
Eric Martz (talk | contribs) |
||
| Line 55: | Line 55: | ||
===2020: CASP 14=== | ===2020: CASP 14=== | ||
The best predictions at CASP 13 (2018) correctly predicted "folds"and the topology of secondary structure elements (helices and beta strands), but fell short of correctly predicting entire structures in detail. | The best predictions at CASP 13 (2018) correctly predicted "folds" and the topology of secondary structure elements (helices and beta strands), but fell short of correctly predicting entire structures in detail. | ||
In CASP 14 (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>. 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 CASP 14 (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>. 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" />. | ||
