AI Folding

Owned by Sam DeLuca
Last updated: Jan 29, 2024 by Ben Baker
2 min read

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).

 

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

Model a multimer (AlphaFold only)

Inputs

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

Options

  • --ai-tool

    • AI 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) (See Notes for more detail)

    • default = openfold

  • --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 = false

  • --gpu-type

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

    • default = t4

Outputs

  • alignments (directory)

    • Alignment data relevant to AI tool predictions

  • 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

References

AlphaFold Github

OpenFold Github

ESMFold Github