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4ca8a65692
Big thanks to @stianale for reporting this in https://github.com/google-deepmind/alphafold3/issues/492. PiperOrigin-RevId: 872892863 Change-Id: Ia24bb492daea44534be4ac743fb304bc72fe9741
63 lines
3.2 KiB
Markdown
63 lines
3.2 KiB
Markdown
# Known Issues
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## Numerical performance for CUDA Capability 7.x GPUs
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All CUDA Capability 7.x GPUs (e.g. V100) produce obviously bad output, with lots
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of clashing residues (the clashes cause a ranking score of -99 or lower), unless
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the environment variable `XLA_FLAGS` is set to include
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`--xla_disable_hlo_passes=custom-kernel-fusion-rewriter`.
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## Incorrect handling of two-letter atoms in SMILES ligands
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Between commits https://github.com/google-deepmind/alphafold3/commit/f8df1c7 and
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https://github.com/google-deepmind/alphafold3/commit/4e4023c, AlphaFold 3
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handled incorrectly any two-letter atoms (e.g. Cl, Br) in ligands defined using
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SMILES strings.
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## MSA discrepancy between AlphaFold 3 and AlphaFold Server
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### The root cause of the problem
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The released AlphaFold 3 and AlphaFold Server use the same model weights and
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equivalent featurisation and model code. However, the way they run genetic
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search is slightly different. The released AlphaFold 3 searches each database in
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one go, while AlphaFold Server has a sharded version of each database (split
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into multiple smaller FASTA files) and searches all of the shards in parallel.
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The results of these parallel searches are then merged together at the end.
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The discrepancy is caused by a different (deeper) MSA on AlphaFold Server in
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some cases. We discovered that the issue is caused by running sharded Jackhmmer
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in AlphaFold Server without the `--domZ` flag (has to be set together with the
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`--Z` flag and set to the same value) which means that effectively the AlphaFold
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Server is running with roughly 100× more permissive `--domE` filter. This means
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more sequences are sometimes included in the MSA.
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We are keeping behaviour unchanged in both the released AlphaFold 3 and in the
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AlphaFold Server, however, we are giving users with local installs an option to
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replicate AlphaFold Server behaviour locally. In our large scale tests the
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difference did not matter, it is only very specific inputs that get better
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accuracy with the deeper MSA.
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See https://github.com/google-deepmind/alphafold3/issues/492 for an example
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input where a protein-DNA complex gets significantly higher ipTM and pTM with
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AlphaFold Server compared to a local run.
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### Replicating AlphaFold Server behaviour locally
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If you want to replicate AlphaFold Server behaviour (i.e. better folding
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accuracy in some cases), you can increase the value of the Jackhmmer/Nhmmer
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`--domE` flag by 100× compared to its default value.
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Alternatively, you can run the sharded MSA search while not setting the `--domZ`
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value – you would have to modify the code to do it. We added support for
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searching against sharded databases in AlphaFold 3 in
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https://github.com/google-deepmind/alphafold3/commit/805adc3863841d83d631ccd18136ad58ce3ecb34
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and the way to run AlphaFold 3 with sharded databases is documented in
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https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md#sharded-genetic-databases.
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It can provide 10–30× speedup (potentially even more, depending on hardware) of
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the genetic search.
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In general, we recommend experimenting with MSA if you are seeing a prediction
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with low predicted confidence. Typically adding more *relevant* sequences in the
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MSA will increase AlphaFold prediction accuracy and model confidence scores.
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