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The AI Folding API provides a common interface to AI based protein structure prediction tools. The API currently supports OpenFold, AlphaFold, and ESMFold. The API runs a post-processing protocol on the results to minimize the output structure into the Rosetta energy function and correct any atomic level errors in sidechain positioning (See Notes for more detail).

Contents

Quickstart

Inputs

Options

Outputs

Notes

References

Release Notes

Quickstart

Model a monomer using AlphaFold

cyrus engine submit ai-folding input.fasta --mode=monomer --ai-tool=alphafold```

Model multiple monomers in parallel using OpenFold

cyrus engine submit ai-folding input.fasta input2.fasta --mode=monomer --ai-tool=openfold

Model a monomer using OpenFold with weights trained by DeepMind (AlphaFold)

Default = OpenFold weights

cyrus engine submit ai-folding input.fasta --mode=monomer --ai-tool=openfold --model-sets=alphafold

Create a model using AlphaFold's SingleSeq mode with 2 recycles

cyrus engine submit ai-folding input.fasta --mode=singleseq --ai-tool=alphafold --af-n-recycles=2

Model a multimer (AlphaFold only)

cyrus engine submit ai-folding input.fasta --mode=multimer --ai-tool=alphafold

Inputs

FASTA file containing sequence(s) of interest to model.

Options

  • --ai-toolAI folding tool to run (openfold, alphafold, esmfold), default = alphafold

  • --mode Mode to run with AI tool (monomer, multimer, singleseq), default = monomer

  • --model-sets The set of model weights to use with OpenFold (alphafold, openfold), default = openfold (See Notes for more detail)

  • --existing-model-data Location of existing model data in GCS, default = null

  • --precomputed-alignments Directory path to precomputed alignments that will be upload and used for AlphaFold jobs, default = null

  • --run-relax Enable or disable the Rosetta relax phase of post-processing, default = true

  • --gpu-type Select the GPU type to use (t4, a100), default = t4 (See Notes for more detail)

Outputs

  • predictions (directory) AI tool model predictions

  • initial_molprobity_reports (directory) Molprobity report for models output by AI tool

  • rosetta_relaxed_models (directory) Rosetta relaxed AI tool models

  • final_molprobity_reports (directory) Molprobity report for relaxed models

Notes

Model weight sets

  • alphafold - weights trained by DeepMind

  • openfold - weights trained by the AlQuarashi Lab for OpenFold

API Post-Processing

The API post-processing protocol consists of the following three steps:

  1. Generate a molprobity report for the models output from the AI tool

  2. Idealize and relax the models output from the AI tool with Rosetta

  3. Generate a molprobity report for the relaxed models.

Modeling large proteins

The amount of GPU memory required increases quadratically with the number of amino acids in the system being modeled. If you are modeling a protein longer than 1500 residues or so, add the following options to the ai-folding submit command: --gpu-type=a100 --run-relax=false

References

AlphaFold Github

OpenFold Github

ESMFold Github

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