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