AlphaPulldownSnakemake

AlphaPulldownSnakemake provides a convenient way to run AlphaPulldown using a Snakemake pipeline. This lets you focus entirely on what you want to compute, rather than how to manage dependencies, versioning, and cluster execution.

1. Installation

Install required dependencies:

mamba create -n snake -c conda-forge -c bioconda python=3.12 \
  snakemake snakemake-executor-plugin-slurm snakedeploy pulp click coincbc
mamba activate snake

That's it, you're done!

2. Configuration

Create a working directory

Create a new processing directory for your project:

snakedeploy deploy-workflow \
  https://github.com/KosinskiLab/AlphaPulldownSnakemake \
  AlphaPulldownSnakemake \
  --tag 2.1.3
cd AlphaPulldownSnakemake

Setup protein folding jobs

Create a sample sheet folds.txt listing the proteins you want to fold. The simplest format uses UniProt IDs:

P01258+P01579
P01258
P01579

Each line represents one folding job:

• P01258+P01579 - fold these two proteins together as a complex
• P01258 - fold this protein as a monomer
• P01579 - fold this protein as a monomer

Advanced protein specification options

You can also specify:

• FASTA file paths instead of UniProt IDs: /path/to/protein.fasta
• Specific residue regions: Q8I2G6:1-100 (residues 1-100 only)
• Multiple copies: Q8I2G6:2 (dimer of the same protein)
• Combinations: Q8I2G6:2:1-100+Q8I5K4 (dimer of residues 1-100 plus another protein)

Configure input files

Edit config/config.yaml and set the path to your sample sheet:

input_files:
  - "folds.txt"

Setup pulldown experiments

If you want to test which proteins from one group interact with proteins from another group, create a second file baits.txt:

Q8I2G6

And update your config:

input_files:
  - "folds.txt"
  - "baits.txt"

This will test all combinations: every protein in folds.txt paired with every protein in baits.txt.

Multi-file pulldown experiments

You can extend this logic to create complex multi-partner interaction screens by adding more input files. For example, with three files:

input_files:
  - "proteins_A.txt"  # 5 proteins
  - "proteins_B.txt"  # 3 proteins
  - "proteins_C.txt"  # 2 proteins

This will generate all possible combinations across the three groups, creating 5×3×2 = 30 different folding jobs. Each job will contain one protein from each file, allowing you to systematically explore higher-order protein complex formation.

Note: The number of combinations grows multiplicatively, so be mindful of computational costs with many files.

3. Execution

Run the pipeline locally:

snakemake --profile config/profiles/desktop --cores 8

Cluster execution

For running on a SLURM cluster, use the executor plugin:

screen -S snakemake_session
snakemake \
  --executor slurm \
  --profile config/profiles/slurm \
  --jobs 200 \
  --restart-times 5

Detach with Ctrl + A then D. Reattach later with screen -r snakemake_session.

4. Results

After completion, you'll find:

• Predicted structures in PDB/CIF format in the output directory
• Interactive Jupyter notebook with 3D visualizations and quality plots
• Results table with confidence scores and interaction metrics

Open the Jupyter notebook with:

jupyter-lab output/reports/output.ipynb

---

Advanced Configuration

Using precomputed features

If you have precomputed protein features, specify the directory:

feature_directory:
  - "/path/to/directory/with/features/"

Note: If your features are compressed, set compress-features: True in the config.

Using CCP4 for analysis

Download and install CCP4, then update your config:

analysis_container: "/path/to/fold_analysis_2.1.2_withCCP4.sif"

Changing folding backends

To use AlphaFold3 or other backends:

structure_inference_arguments:
  --fold_backend: alphafold3
  --<other-flags>

Note: AlphaPulldown supports: alphafold, alphafold3, alphalink, and unifold backends.

Database configuration

Set the path to your AlphaFold databases:

databases_directory: "/path/to/alphafold/databases"

Performance tuning

Adjust computational parameters:

save_msa: False
use_precomputed_msa: False
predictions_per_model: 1
number_of_recycles: 3