AlphaFold: Difference between revisions

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==AlphaFold Database of Predictions==

Also in July, 2021, DeepMind made available over 300,000 structure predictions from amino acid sequences in their free [https://alphafold.ebi.ac.uk/ AlphaFold DB]<ref name="deepminddb">[https://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">[https://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">[https://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">[https://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" />. The limitations of these predictions were enumerated<ref name="impacts" />, including

Also in July, 2021, DeepMind made available over 300,000 structure predictions from amino acid sequences in their free [https://alphafold.ebi.ac.uk/ AlphaFold DB]<ref name="deepminddb">[https://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">[https://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">[https://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">[https://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" />. The limitations of these predictions were enumerated<ref name="impacts" />, including:

* Inability to predict protein-protein or protein-DNA/RNA/ligand complexes.

* Does not predict ligands, cofactors, metals, ions, glycosylation, etc.

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* Does not predict the folding pathway.

* Has not been trained to predict structural consequences of point mutations.

Nevertheless, these predictions have many potential benefits<ref name="impacts" />, including:

* Simplifying [[X-ray crystallography]] by enabling solution of the phase problem by molecular replacement using the predicted model.

* Assisting crystallographers in defining domain boundaries in order to crystallize domains when crystallization of full length proteins is problematic.

* Helping to interpret >5,000 [[cryo-EM]] maps previously deposited in the [[EMDB]] that could not be interpreted as atomic models, as well as helping to interpret lower resolution EM maps as atomic models.

==References==

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 ==AlphaFold Database of Predictions==
-Also in July, 2021, DeepMind made available over 300,000 structure predictions from amino acid sequences in their free [https://alphafold.ebi.ac.uk/ AlphaFold DB]<ref name="deepminddb">[https://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">[https://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">[https://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">[https://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" />. The limitations of these predictions were enumerated<ref name="impacts" />, including
+Also in July, 2021, DeepMind made available over 300,000 structure predictions from amino acid sequences in their free [https://alphafold.ebi.ac.uk/ AlphaFold DB]<ref name="deepminddb">[https://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">[https://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">[https://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">[https://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" />. The limitations of these predictions were enumerated<ref name="impacts" />, including:
 * Inability to predict protein-protein or protein-DNA/RNA/ligand complexes.
 * Does not predict ligands, cofactors, metals, ions, glycosylation, etc.
@@ Line 30: / Line 30: @@
 * Does not predict the folding pathway.
 * Has not been trained to predict structural consequences of point mutations.
+Nevertheless, these predictions have many potential benefits<ref name="impacts" />, including:
+* Simplifying [[X-ray crystallography]] by enabling solution of the phase problem by molecular replacement using the predicted model.
+* Assisting crystallographers in defining domain boundaries in order to crystallize domains when crystallization of full length proteins is problematic.
+* Helping to interpret >5,000 [[cryo-EM]] maps previously deposited in the [[EMDB]] that could not be interpreted as atomic models, as well as helping to interpret lower resolution EM maps as atomic models.
 ==References==
 <references />