Alphafold

Alphafold3

Alphafold3 Apptainer File Image:

/hpc/share/containers/apptainer/alphafold/3.0.1/alphafold-3.0.1.sif

Download the Alphafold3 input file fold_input.json and save it in af_input folder:

fold_input.json

{
  "name": "2PV7",
  "sequences": [
    {
      "protein": {
        "id": ["A", "B"],
        "sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
      }
    }
  ],
  "modelSeeds": [1],
  "dialect": "alphafold3",
  "version": 1
}

Script slurm-alphafold.sh to run alphafold on 1 node with 1 GPU (8 tasks per node):

slurm-alphafold.sh

#!/bin/bash --login
#SBATCH --job-name=alphafold
#SBATCH --output=af_output/%x.d%j/%x.o%j
#SBATCH --error=af_output/%x.d%j/%x.e%j
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=8
#SBATCH --time=0-02:00:00
#SBATCH --mem=10G
#SBATCH --partition=gpu
#SBATCH --qos=gpu
#SBATCH --gres=gpu:a100_40g:1
##SBATCH --account=<account>
 
shopt -q login_shell || exit 1
test -n "$SLURM_NODELIST" || exit 1
test $SLURM_NNODES -eq 1 || exit 1
 
module load apptainer
module load alphafold/3.0.1
 
test -n "$ALPHAFOLD_CONTAINER" || exit 1
 
ALPHAFOLD_N_CPU=$SLURM_CPUS_PER_TASK
ALPHAFOLD_INPUT_DIR="$PWD/af_input"
ALPHAFOLD_OUTPUT_DIR="$PWD/af_output/${SLURM_JOB_NAME}.d${SLURM_JOB_ID}"
ALPHAFOLD_MODEL_DIR="$(dirname "$ALPHAFOLD_CONTAINER")/models"
ALPHAFOLD_DB_DIR='/hpc/share/databases/alphafold/3'
 
mkdir -p "$ALPHAFOLD_OUTPUT_DIR"
 
apptainer exec \
    --bind '/opt/hpc/system/nvidia/driver:/usr/local/nvidia/bin' \
    --bind '/opt/hpc/system/nvidia/driver:/usr/local/nvidia/lib' \
    --bind "$ALPHAFOLD_INPUT_DIR:/root/af_input" \
    --bind "$ALPHAFOLD_OUTPUT_DIR:/root/af_output" \
    --bind "$ALPHAFOLD_MODEL_DIR:/root/models" \
    --bind "$ALPHAFOLD_DB_DIR:/root/public_databases" \
    "$ALPHAFOLD_CONTAINER" \
    python /app/alphafold/run_alphafold.py \
    --json_path=/root/af_input/fold_input.json \
    --model_dir=/root/models \
    --db_dir=/root/public_databases \
    --pdb_database_path=/root/public_databases/mmcif_files \
    --output_dir=/root/af_output \
    --jackhmmer_n_cpu=$ALPHAFOLD_N_CPU \
    --nhmmer_n_cpu=$ALPHAFOLD_N_CPU

The processing result will be saved in the af output folder.