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Run AlphaFold2 on Instance

Prerequisites​

  • Instance(GPU recommended). Full AlphaFold databases require ~2+ TB disk; reduced DBs use much less.
  • Conda installed.
  • Read/write access to chosen data and output directories (for example /mnt/model/vdb).

1. System packages (Ubuntu)​

Update and install basic build tools and utilities:

sudo apt update -y && sudo apt upgrade -y
sudo apt install -y build-essential cmake git hmmer kalign tzdata wget libstdc++6

2. Create and activate Conda environment​

Create a Python 3.8 environment and activate it:

conda create -n alphafold python=3.8 -y
conda activate alphafold

3. Install Python dependencies​

Install key packages; follow AlphaFold README for full dependency list:

pip install jaxlib==0.4.26
conda install -c conda-forge pdbfixer libstdcxx-ng -y

(Install any additional pip packages listed in the AlphaFold repo README.)

4. Helper file required by AlphaFold​

AlphaFold expects a helper text file under alphafold/alphafold/common/:

mkdir -p alphafold/alphafold/common/
wget -q -P alphafold/alphafold/common/ https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt

5. Clone AlphaFold repo​

git clone https://github.com/google-deepmind/alphafold.git

6. cd alphafold​

Follow the repository README for any extra setup steps or environment pins.

7. Prepare databases​

Place required databases on your instance and note the root data directory (example: /mnt/model/vdb/database). Essential subpaths include pdb70, uniref90, mgnify, bfd (or small_bfd for reduced), uniref30, pdb_mmcif. If disk or time is constrained, use the reduced DB preset or precomputed MSAs.

8. Run AlphaFold (full DBs)​

Update paths (FASTA, output, data) and run the full DB preset as a single-line command:

python3 run_alphafold.py --use_gpu_relax=True \
  --fasta_paths=/mnt/model/vdb/input/T1049.fasta \
  --output_dir=/mnt/model/vdb/output \
  --data_dir=/mnt/model/vdb/database \
  --pdb70_database_path=/mnt/model/vdb/database/pdb70/pdb70 \
  --uniref90_database_path=/mnt/model/vdb/database/uniref90/uniref90.fasta \
  --mgnify_database_path=/mnt/model/vdb/database/mgnify/mgy_clusters_2022_05.fa \
  --bfd_database_path=/mnt/model/vdb/database/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
  --uniref30_database_path=/mnt/model/vdb/database/uniref30/UniRef30_2021_03 \
  --template_mmcif_dir=/mnt/model/vdb/database/pdb_mmcif/mmcif_files \
  --obsolete_pdbs_path=/mnt/model/vdb/alphafold/pdbs/pdb.json \
  --max_template_date=2025-03-04 \
  --db_preset=full_dbs \
  --model_preset=monomer \
  --use_precomputed_msas=False

9. Run AlphaFold (reduced DB option)​

If disk/runtime is constrained, use reduced DBs and the small BFD; single-line command:

python3 run_alphafold.py --use_gpu_relax=True \
  --fasta_paths=/mnt/model/vdb/input/T1049.fasta \
  --output_dir=/mnt/model/vdb/output \
  --data_dir=/mnt/model/vdb/database \
  --template_mmcif_dir=/mnt/model/vdb/database/pdb_mmcif/mmcif_files \
  --obsolete_pdbs_path=/mnt/model/vdb/alphafold/pdbs/pdb.json \
  --max_template_date=2025-03-04 \
  --db_preset=reduced_dbs \
  --model_preset=monomer \
  --use_precomputed_msas=False \
  --small_bfd_database_path=/mnt/model/vdb/database/small_bfd/bfd-first_non_consensus_sequences.fasta

10. Parameter highlights​

  • --fasta_paths: input FASTA (one or more).
  • --data_dir: root directory containing databases.
  • --output_dir: where predictions and metrics are written.
  • --max_template_date: restrict templates by date.
  • --db_preset: full_dbs or reduced_dbs.
  • --model_preset: monomer (or multimer if configured and set up).
  • --use_precomputed_msas: set True if you have precomputed MSAs.

11. Viewing predictions​

After completion, PDB files and metrics are in the output directory. Visualize PDBs with NCBI iCn3D:

Open https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html → File → Open Files → select PDB(s).

12. Common issues & troubleshooting​

  • CUDA OOM: switch to monomer, reduce DB size, use a instance with more GPU memory, or enable swap if appropriate for your environment.
  • Missing files / path errors: verify FASTA and all DB paths are correct and accessible.
  • Permission errors: ensure the runtime user can read/write the data and output directories (use chown/chmod as needed).
  • Long runtime / disk constraints: use reduced_dbs or precomputed MSAs to speed runs.

13. Example outputs​

AlphaFold writes predictions and auxiliary outputs (ranked models, metrics) into the output directory.

Example header text:

prediction_1 | score=... | model_preset=monomer
Last updated on May 15, 2026.