How to predict structures with AlphaFold: Difference between revisions

* New in 2024<ref name="af3" />: [https://alphafoldserver.com AlphaFoldServer.Com]. Using AlphaFold3, predicts homo- and hetro-multimers involving protein, DNA, RNA, ligands, and modified residues. Straightforward to use; Guide and FAQ provided. Predictions are templated without user control (see FAQ). Free for non-commercial use -- see [https://alphafoldserver.com/terms Terms of Service] and [https://alphafoldserver.com/output-terms Output Terms of Use]. From the DeepMind team<ref name="af3" />.

**Predicted models are in [[Mmcif format|mmCIF format]] only. To convert to [[PDB format]] for use in [[FirstGlance in Jmol|FirstGlance]], see [[Converting AlphaFold3 CIF to PDB]].

**To easily obtain average [[pLDDT]] (predicted confidence) for a range of residues, see [[FirstGlance/How to get average pLDDT from AlphaFold models]]

* New in 2024<ref name="rfaa">PMID: 38452047</ref>: RosettaFold All-Atom (RFAA) predicts multimers of protein and nucleic acids with ligands. From the Baker team<ref name="rfaa" />. [https://neurosnap.ai/service/RoseTTAFold%20All-Atom A free server limited to very small numbers of jobs is available from Neurosnap].

* New in 2024<ref name="combfold">PMID: 38326495</ref>: [https://neurosnap.ai/service/CombFold CombFold] predicts the structures of large protein complexes from subunit sequences using AlphaFold Multimer paired with a cominatorial method to assemble subunits. From Shor and Schneidman-Duhovny<ref name="combfold" />.

* New in 2022<ref name="colabfold">PMID: 35637307</ref>: [https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb ColabFold AlphaFold2_advanced]. Predicts homo- and hetero-multimers using methods from the Steinegger/Mirdita team<ref name="colabfold" /><ref name="colabfold2024">PMID: 39402428</ref>, before AlphaFold-multimer<ref name="afmultimer">[https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1.full Protein complex prediction with AlphaFold-Multimer], Preprint, Evans et al. 2021.</ref> was available. Does NOT use templates. [https://www.ebi.ac.uk/training/online/courses/alphafold/accessing-and-predicting-protein-structures-with-alphafold/predicting-protein-structures-with-colabfold-and-alphafold-colab/ See Instructions from EMBL-EBI].

*New in 2022<ref name="af2021">PMID: 34265844</ref><ref name="afmultimer" />: [https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb AlphaFold2/Multimer Colab] able to predict protein multimers. From the DeepMind team<ref name="af2021" /><ref name="afmultimer" />. [https://www.ebi.ac.uk/training/online/courses/alphafold/accessing-and-predicting-protein-structures-with-alphafold/predicting-protein-structures-with-colabfold-and-alphafold-colab/ See Instructions from EMBL-EBI].

*New in 2022<ref name="alphafill">PMID: 36424442</ref>: [https://alphafill.eu/ AlphaFill] “transplants” missing ligands, cofactors and (metal) ions into AlphaFold models. From the Perrakis team<ref name="alphafill" />. Ligand positioning is approximate. See [[Alphafold#Ligands:_AlphaFill |CAUTION]] provided by the AlphaFill team:

*New in 2021<ref name="rosettafold">PMID: 34282049</ref>: [https://robetta.bakerlab.org/ RoseTTAFold at Robetta] is an independent design from the Baker team<ref name="rosettafold" />, influenced by the design of AlphaFold2. Predicts monomers and multimers. Comparing results of RoseTTAFold with results of AlphaFold2/3 is worthwhile. At [https://robetta.bakerlab.org/ Robetta], open the Structure Prediction menu at the top, and choose Submit. ''Be sure to check RoseTTAFold under Optional!''

[http://firstglance.jmol.org FirstGlance in Jmol] automatically colors its initial view of uploaded AlphaFold or RoseTTAFold models by estimated reliability per residue ('''{{Font color|blue|blue for high confidence}}, {{Font color|red|red for low confidence}}'''). After you go to other views or tools, you can always get back to this color scheme by clicking ''Reliability Estimates'' in the ''Views'' tab.

You can easily visualize

* Estimated reliability per residue