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AlphaFold v2.3.0

This technical note describes updates in the code and model weights that were made to produce AlphaFold v2.3.0 including updated training data.

We have fine-tuned new AlphaFold-Multimer weights using identical model architecture but a new training cutoff of 2021-09-30. Previously released versions of AlphaFold and AlphaFold-Multimer were trained using PDB structures with a release date before 2018-04-30, a cutoff date chosen to coincide with the start of the 2018 CASP13 assessment. The new training cutoff represents ~30% more data to train AlphaFold and more importantly includes much more data on large protein complexes. The new training cutoff includes 4× the number of electron microscopy structures and in aggregate twice the number of large structures (more than 2,000 residues)1. Due to the significant increase in the number of large structures, we are also able to increase the size of training crops (subsets of the structure used to train AlphaFold) from 384 to 640 residues. These new AlphaFold-Multimer models are expected to be substantially more accurate on large protein complexes even though we use the same model architecture and training methodology as our previously released AlphaFold-Multimer paper.

These models were initially developed in response to a request from the CASP organizers to better understand baselines for the progress of structure prediction in CASP15, and because of the significant increase in accuracy for large targets, we are making them available as the default multimer models. Since they were developed as baselines, we have emphasized minimal changes to our previous AlphaFold-Multimer system while accommodating larger complexes. In particular, we increase the number of chains used at training time from 8 to 20 and increase the maximum number of MSA sequences from 1,152 to 2,048 for 3 of the 5 AlphaFold-Multimer models.

For the CASP15 baseline, we also used somewhat more expensive inference settings that have been found externally to improve AlphaFold accuracy. We increase the number of seeds per model to 202 and increase the maximum number of recyclings to 20 with early stopping3. Increasing the number of seeds to 20 is recommended for very large or difficult targets but is not the default due to increased computational time.

Overall, we expect these new models to be the preferred models whenever the stoichiometry of the complex is known, including known monomeric structures. In cases where the stoichiometry is unknown, such as in genome-scale prediction, it is likely that single chain AlphaFold will be more accurate on average unless the chain has several thousand residues.

The predicted structures used for the CASP15 baselines are available here.

Footnotes

  1. wwPDB Consortium. "Protein Data Bank: the single global archive for 3D macromolecular structure data." Nucleic Acids Res. 47, D520–D528 (2018). ↩

  2. Johansson-Åkhe, Isak, and Björn Wallner. "Improving peptide-protein docking with AlphaFold-Multimer using forced sampling." Frontiers in bioinformatics 2 (2022): 959160-959160. ↩

  3. Gao, Mu, et al. "AF2Complex predicts direct physical interactions in multimeric proteins with deep learning." Nature communications 13.1 (2022): 1-13. ↩