AlphaFold: Difference between revisions

In 2020, the '''AlphaFold2'''<ref name="senior202001">PMID: 31942072</ref><ref name="alphafoldwikipedia">[http://en.wikipedia.org/wiki/AlphaFold AlphaFold] at Wikipedia.</ref> system of [http://deepmind.com DeepMind]<ref name="deepmindblog">[http://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">[http://en.wikipedia.org/wiki/DeepMind DeepMind] at Wikipedia.</ref> demonstrated a '''major breakthrough'''<ref name="alquraishi">[http://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">[http://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">[http://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 July, 2021, DeepMind made available over 300,000 structure predictions from amino acid sequences in their free [http://alphafold.ebi.ac.uk/ AlphaFold DB]<ref name="deepminddb">[http://deepmind.com/research/case-studies/alphafold#a_treasure_trove We’ve made AlphaFold predictions freely available to anyone in the scientific community] at DeepMind.com (date of release not specified, approximately July 2021).</ref><ref name="afdbebi">[http://www.ebi.ac.uk/pdbe/about/news/alphafold%E2%80%99s-protein-structure-predictions-now-available-explore AlphaFold’s protein structure predictions now available to explore] at the European Bioinformatics Institute, July 23, 2021.</ref><ref name="impacts">[http://www.embl.org/news/science/alphafold-potential-impacts/ Great expectations – the potential impacts of AlphaFold DB] at the European Bioinformatics Institute, July 22, 2021</ref><ref name="human">[http://www.embl.org/news/science/alphafold-database-launch/ DeepMind and EMBL release the most complete database of predicted 3D structures of human proteins] at the European Bioinformatics Institute, July 22, 2021.</ref>. These predictions include nearly all ~20,000 proteins in the human proteome, 36% with very high confidence, and another 22% with high confidence<ref name="human" /><ref name="human-nature">PMID: 34293799</ref>. Also included are ''E. coli'', fruit fly, mouse, zebrafish, malaria parasite and tuberculosis bacteria<ref name="human" />. Limitations of these predictions were enumerated<ref name="impacts" />, including:

Also in July, 2021, Minkyung Baek and a large team in the [http://www.bakerlab.org/ group of David Baker] published their  '''RoseTTAFold''' employing a three-track network, based in part on methods inspired by AlphaFold but not yet fully-detailed by DeepMind. They reported "accuracies approaching those of DeepMind in CASP14"<ref name="baek1">PMID: 34282049</ref>. At the time of its release in July, 2021, it had outperformed all other available structure prediction ''servers''<ref name="baek1" />.

The [http://robetta.bakerlab.org/ RoseTTAFold Server] was made freely available. (Open the ''Structure Prediction'' menu at the top and choose ''Submit''. At the form, be sure to check ''RoseTTAFold'' before submitting your job).

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">[http://research.google.com/colaboratory/faq.html Collaboratory FAQ] at Google.</ref>.

DeepMind has provided an [http://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb Alphafold Colab] that uses a "slightly simplified" version of AlphaFold version 2.0: "While accuracy will be near-identical to the full AlphaFold system on many targets, a small fraction have a large drop in accuracy due to the smaller MSA and lack of templates.". The AlphaFold Colab is '''free to use'''. The code is executed in a virtual machine private to your account, and data are stored on Google Drive. Nothing is installed on your computer; "everything happens in the cloud on Google Colab"<ref name="alphafoldcolab">[http://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb Alphafold Colab].</ref>

- [http://github.com/sokrypton/ColabFold#making-protein-folding-accessible-to-all-via-google-colab A Guide to the free RoseTTAFold and AlphaFold 2 Colab notebooks]

- A [http://www.youtube.com/watch?v=Rfw7thgGTwI video covering an overview, comparison of some of the methods and how people are already extending them, how to submit and interpret, and a tutorial on how to use AlphaFold2 Colab] is available. The video was recorded on August 4th, 2021 presented by Sergey Ovchinnikov and Martin Steinegger, hosted by Chris Bahl for the Boston Protein Design and Modeling Club

: (Report by Oana Stroe, Senior Communications Officer at EMBL-EBI. 28 July 2021 at [http://bit.ly/2UZREPx FEBS Network])