JoseFcoMH/af3_screening

A snakemake workflow for protein-protein interaction screening with Alphafast (mmseqs + Alphafold 3)

Overview

Latest release: None, Last update: 2026-08-13

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=JoseFcoMH/af3_screening

Quality control: linting: failed formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/JoseFcoMH/af3_screening . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Usage

It’s probably best to specify at least the mmseqs database path and placing it in persistent storage, since it takes a long time to set up.

A persistent predictions database will avoid running the same prediction across different workflow invocations, provided they share the same database directory.

At least one of reference_structures or reference_proteins must be provided.

Params

Using the config file in the test directory as a base:

reference_proteins: proteinlists.txt 
reference_structures: structurelist.txt
proteins_screened: preys.txt

total_max_res: 4000
prediction_batch_size: 4000

out_dir: .test
scratch_dir : /tmp/af3_pipeline/test
predictions_database: /tmp/af3_pipeline/test/test_db
uniprot_path: /tmp/af3_pipeline/test/uniprot_db/uniprot_sprot.fasta
udocker_dir: ~/.udocker

af_repo_dir: /work/repos/alphafast
af_mmseqs_db: /work/datadir/mmseqs2_dbs
af_weights_dir: /work/datadir/

reference_proteins: path to a file containing, on each line, one path to a file containing a list of uniprot accessions (also one per line).
reference_structures: path to a file containing, on each line, one path to a structure in .cif format. Sequences will be extracted from the file, and each prey will be projected to it too.
proteins_screened: path to a file containing a list of uniprot accessions. Predictions will be the product of this list and the concatenated reference_proteins.
total_max_res: AF inputs whose chains surpass this threshold won’t be considered for prediction (optional, default 4000).
prediction_batch_size: max pairs to predict at a time (optional, default 4000).
out_dir: Outputs will be saved here.
scratch_dir: scratch directory. Work will happen mostly here, so it should be local (optional, default out_dir/tmp).
predictions_database: path to a directory containing compressed AF outputs (optional).
uniprot_path: path where uniprot-swissprot will be saved to (optional).
udocker_dir: path to persistent udocker dir (optional, default env var UDOCKER_DIR or ~/.udocker if not set)
af_repo_dir: alphafast repo dir (optional).
af_mmseqs_db: path to mmseqs db (optional).
af_weights_dir: path to directory containing Alphafold 3 weights.

Linting and formatting

Linting results

Using workflow specific profile profiles/default for setting default command line arguments.
Snakefile at: /tmp/tmpr0wec_xw/workflow
AttributeError in file "/tmp/tmpr0wec_xw/workflow/Snakefile", line 19:
'PosixPath' object has no attribute 'decode'
  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/smart_open/smart_open_lib.py", line 73, in parse_uri
  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/smart_open/smart_open_lib.py", line 52, in _sniff_scheme
  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/urllib/parse.py", line 493, in urlsplit
  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/urllib/parse.py", line 133, in _coerce_args
  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/urllib/parse.py", line 117, in _decode_args
  File "/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/urllib/parse.py", line 117, in <genexpr>

Formatting results

[DEBUG] 
[DEBUG] 
[DEBUG] 
[DEBUG] 
[DEBUG] 
[DEBUG] 
[DEBUG] In file "/tmp/tmpr0wec_xw/workflow/rules/preprocess.smk":  Formatted content is different from original
[INFO] 1 file(s) would be changed 😬
[INFO] 5 file(s) would be left unchanged 🎉

snakefmt version: 0.11.5