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

Using the API

The “AI Folding” api provides a common interface to AI based protein structure prediction tools. The API currently supports openfold (https://github.com/CyrusBiotechnology/openfold) and alphafold ( https://github.com/deepmind/alphafold#alphafold-output ).

To model a single chain protein with alphafold or openfold

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

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

Multiple fasta files can be evaluated in a single job like this:

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

The Openfold tool can run with either the weights trained by deepmind for alphafold, or weights trained by the AlQuarashi lab for openfold. The openfold trained weights are the default, to use the alphafold weights with the openfold tool, run

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

When modeling a large protein, you can use a larger a100 GPU with --gpu-type=a100 and disable the rosetta relax post-processing step with --run-relax=false

Currently, openfold does not support multichain modeling

To model a multi-chain protein

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

API Outputs

The AI Folding API runs a post-processing protocol on the results of the AI tool. The purpose of the post-processing is to minimize the output structure into the rosetta energy function and correct any atomic detail level errors in sidechain positioning. The 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 rosetta relaxed models.

Alongside the raw output from the AI tool, the API will produce the following directories:

  • initial_molprobity_reports – Molprobity reports from step 1 of the post-processing

  • rosetta_relaxed_models – relaxed models from step 2

  • final_molprobity_reports -- relaxed models from step 3

An explanation of the alphafold output files can be found here: https://github.com/deepmind/alphafold#alphafold-output

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