AlphaFold: Difference between revisions
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[[Image:Hassabis_Demis.jpg|300px|right|thumb| Demis Hassabis - DeepMind]] | [[Image:Hassabis_Demis.jpg|300px|right|thumb| Demis Hassabis - DeepMind]] | ||
[[Image:Jumper_John.jpg|300px|right|thumb| John Jumper - DeepMind]] | [[Image:Jumper_John.jpg|300px|right|thumb| John Jumper - DeepMind]] | ||
<table style="background-color: | <table style="background-color:#ffffb0;"><tr><td> | ||
October 2024: David Baker, Demis Hassabis, and John M. Jumper share the [[Nobel_Prizes_for_3D_Molecular_Structure#2020-2029|Nobel Prize in Chemistry]], Baker for "computational protein design", Hassabis and Jumper for "protein structure prediction", namely, AlphaFold. | October 2024: David Baker, Demis Hassabis, and John M. Jumper share the [[Nobel_Prizes_for_3D_Molecular_Structure#2020-2029|Nobel Prize in Chemistry]], Baker for "computational protein design", Hassabis and Jumper for "protein structure prediction", namely, AlphaFold. | ||
* [https://www.youtube.com/watch?v=cx7l9ZGFZkw 22 min video] explaining their contributions. | * [https://www.youtube.com/watch?v=cx7l9ZGFZkw 22 min video] explaining their contributions. | ||
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<table style="background-color:#e0ffe0;border:1px solid black;font-size:120%;"><tr><td> | <table style="background-color:#e0ffe0;border:1px solid black;font-size:120%;"><tr><td> | ||
If you want an AlphaFold-predicted structure for a protein sequence: | If you want an AlphaFold-predicted structure for a protein sequence: | ||
* If a prediction is already in the [[#AlphaFold Database of Predictions|AlphaFold Database]], simply download it. | * If a prediction is already in the [[#AlphaFold Database of Predictions|AlphaFold Database]], simply download it. Limited to single chain proteins without ligands. | ||
* Otherwise, follow [[How to predict structures with AlphaFold]]. | * Otherwise, and for multiple chain (protein/nucleic acid) structures with ligands, follow [[How to predict structures with AlphaFold]]. | ||
* 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]. | * 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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2021 Resources: [For more recent resources and servers, see [[How to predict structures with AlphaFold]].] | |||
* See [https://www.ebi.ac.uk/training/online/courses/alphafold AlphaFold A practical Guide] Superb EMBL-EBI Interactive online tutorial on AlphaFold2 (~3 hours) | * See [https://www.ebi.ac.uk/training/online/courses/alphafold AlphaFold A practical Guide] Superb EMBL-EBI Interactive online tutorial on AlphaFold2 (~3 hours) | ||
*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 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'''. | ||
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==AlphaFold published July 2021== | ==AlphaFold published July 2021== | ||
[For recent prediction servers, see [[How to predict structures with AlphaFold]].] | |||
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. | 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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<table style="background-color:#e0ffe0;border:1px solid black;font-size:120%;"><tr><td> | <table style="background-color:#e0ffe0;border:1px solid black;font-size:120%;"><tr><td> | ||
If you want an AlphaFold-predicted structure for a protein sequence: | If you want an AlphaFold-predicted structure for a protein sequence: | ||
* If a prediction is already in the [[#AlphaFold Database of Predictions|AlphaFold Database]], simply download it. | * If a prediction is already in the [[#AlphaFold Database of Predictions|AlphaFold Database]], simply download it. Limited to single chain proteins without ligands. | ||
* Otherwise, follow [[How to predict structures with AlphaFold]]. | * Otherwise, and for multiple chain (protein/nucleic acid) structures with ligands, follow [[How to predict structures with AlphaFold]]. | ||
* Uploading the predicted PDB file to [http://FirstGlance.Jmol.Org FirstGlance in Jmol] will automatically '''color it by estimated reliability per residue'''. [http://bioinformatics.org/molvis/images/firstglance-with-alphafold.png | * 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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==Advances since 2021== | ==Advances since 2021== | ||
*See a list of newer servers at [[How to predict structures with AlphaFold]]. | |||
*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. | *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== | ==See Also== | ||
*[[How to predict structures with AlphaFold]]. | |||
*[[AlphaFold/Index]], a list of pages in Proteopedia about Alphafold. | *[[AlphaFold/Index]], a list of pages in Proteopedia about Alphafold. | ||
*[[How To Find A Structure]] covers both [[empirical models]] and the advantages of comparing them with AlphaFold models due to [[missing residues and incomplete sidechains]] prevalent in empirical models. | |||
==References== | ==References== | ||
