AlphaFold
AlphaFold is an AI program developed by DeepMind that predicts protein structures from amino acid sequences.
Databases
The necessary databases are pre-mounted on GPU nodes at /alphafold_storage/alphafold_db — no download needed.
Required Parameters
| Parameter | Description |
|---|---|
-d <data_dir> |
Path to the supporting data directory |
-o <output_dir> |
Path to store results |
-f <fasta_paths> |
Path to FASTA file(s). Multiple sequences in one file = multimer. Multiple files comma-separated = fold sequentially |
-t <max_template_date> |
Maximum template release date (YYYY-MM-DD) |
Optional Parameters
| Parameter | Default | Description |
|---|---|---|
-g |
true | Enable NVIDIA GPU runtime |
-r |
true | Run final relaxation step |
-e |
true | Run relax on GPU |
-n |
all cores | OpenMM threads |
-a |
0 | CUDA_VISIBLE_DEVICES — comma-separated GPU list |
-m |
monomer | Model preset: monomer, monomer_casp14, monomer_ptm, multimer |
-c |
full_dbs | MSA database preset: reduced_dbs or full_dbs |
-p |
false | Use precomputed MSAs from disk |
-l |
5 | Predictions per model (multimer only) |
-b |
false | Benchmark mode — excludes compilation time |
Example Job Script
#!/bin/bash
#SBATCH --job-name=AlphaFold-Multimer
#SBATCH --partition=gpu2
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=32G
#SBATCH --gres=gpu:1
#SBATCH --output=alphafold_%j.out
#SBATCH --error=alphafold_%j.err
module load alphafold/alphafold_non_docker_2.3.1
bash $ALPHAFOLD_SCRIPT_PATH/run_alphafold.sh \
-d $ALPHAFOLD_DB_PATH \
-o ~/output_dir \
-f $ALPHAFOLD_SCRIPT_PATH/examples/query.fasta \
-t $(date +%Y-%m-%d)Memory Guidelines
- Monomer — at least 32GB RAM
- Multimer — at least 64GB RAM; large/complex structures may need 128GB+
Additional Resources
- Sample FASTA:
/home/alphafold_folder/alphafold_multimer_non_docker/example/query.fasta - alphafold_non_docker GitHub repository