AlphaFold: Difference between revisions
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* If a prediction is already in the [[#AlphaFold Database of Predictions|AlphaFold Database]], simply download it. Limited to single chain proteins without ligands. | * If a prediction is already in the [[#AlphaFold Database of Predictions|AlphaFold Database]], simply download it. Limited to single chain proteins without ligands. | ||
* Otherwise, and for multiple chain (protein/nucleic acid) structures with ligands, 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]]. | ||
* | * Dropping the predicted model into [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], [https://molviz.org/firstglance/fgij/fg.htm?mol=AFDB-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'''. | ||
