AlphaFold: Difference between revisions

[[Image:Hassabis_Demis.jpg|300px|right|thumb| Demis Hassabis - DeepMind]]

[[Image:Jumper_John.jpg|300px|right|thumb| John Jumper - DeepMind]]

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 [https://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" />. But consider also "The joys and perils of AlphaFold"<ref name="perils">PMID: 34668287</ref>.

In 2022, at [[CASP]] 15, AlphaFold2 continued to outperform all other methods in the majority of cases (see a summary of results at [[Theoretical_models#2022:_CASP_15|Theoretical models]]).

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If you want an AlphaFold-predicted structure for a protein sequence:

* Uploading the predicted PDB file to [http://FirstGlance.Jmol.Org FirstGlance in Jmol] will automatically '''color it by estimated reliability per residue'''. Examples: [http://bioinformatics.org/molvis/images/firstglance-with-alphafold.png Snapshot], [http://firstglance.jmol.org/fg.htm?mol=AF-Q9AY27-F1-model_v1.pdb Interactive].

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*See short [https://mediasite.embl.de/Mediasite/Play/a320afff218d4a3cbad6ea6eca5212931d superb lecture] on AlphaFold by the CEO of ''DeepMind'', '''Dennis Hassabis''', that was given at the EMBL, Heidelberg, on 3-Feb-2022, entitled '''Using AI to accelerate scientific discovery'''.

*see [https://youtu.be/UqeQfRDA8Yk EMBL-EBI-Training Video] six superb short talks on AlphaFold2

==AlphaFold published July 2021==

AlphaFold was published in July, 2021<ref name="af2021">PMID: 34265844</ref>. Methods were described in considerable detail. The source code, trained weights, and inference script were made available under an '''open-source license'''. Structure prediction required about one GPU (Graphics Processing Unit) minute per model of about 384 amino acids.

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If you want an AlphaFold-predicted structure for a protein sequence:

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Google provides "Colaboratories" (Colabs). A Colab "allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education"<ref name="colabfaq">[https://research.google.com/colaboratory/faq.html Collaboratory FAQ] at Google.</ref>.

For those unfamiliar with Colabs, the user interface may look unfamiliar, but the instructions are clear and straightforward to use. The mentions of "Runtime -> Run after" refer to the Runtime pull-down menu at the very top of the page. Getting a result may take several hours.

==Advances since 2021==

*RoseTTAFoldNA<ref>PMID: 37996753</ref> offers a leap forward in predicting structures of complexes of proteins and nucleic acids, but in November 2023 is not yet available as a free server.

==See Also==

*[[AlphaFold/Index]], a list of pages in Proteopedia about Alphafold.

==References==