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Fix #564
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README.md
74
README.md
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**[Documentation](https://github.com/KosinskiLab/AlphaPulldown/wiki)** | **[Precalculated Input Database](https://github.com/KosinskiLab/AlphaPulldown/wiki/Features-Database)** | **[Downstream Analysis](https://github.com/KosinskiLab/AlphaPulldown/wiki/Downstream-Analysis)**
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[AlphaPulldownSnakemake](https://github.com/KosinskiLab/AlphaPulldownSnakemake/tree/main) provides a convenient way to run AlphaPulldown using a [Snakemake pipeline](https://snakemake.readthedocs.io/en/stable/). This lets you focus entirely on **what** you want to compute, rather than **how** to manage dependencies, versioning, and cluster execution.
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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. When you deploy the workflow with `snakedeploy`, the configuration file will be copied after deployment to your local project directory as [config/config.yaml](https://github.com/KosinskiLab/AlphaPulldownSnakemake/blob/main/config/config.yaml).
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For running without Snakemake, see this [link](https://github.com/KosinskiLab/AlphaPulldown/wiki).
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## 1. Installation
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Create and activate the conda environment:
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Install required dependencies:
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```bash
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conda env create \
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-n snake \
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-f https://raw.githubusercontent.com/KosinskiLab/AlphaPulldownSnakemake/2.1.5/workflow/envs/alphapulldown.yaml
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conda activate snake
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mamba create -n snake -c conda-forge -c bioconda python=3.12 \
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snakemake snakemake-executor-plugin-slurm snakedeploy pulp click coincbc
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mamba activate snake
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```
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This environment file installs Snakemake and all required plugins via conda and pulls in `alphapulldown-input-parser` from PyPI in a single step.
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That's it, you're done!
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## 2. Configuration
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@@ -28,7 +28,7 @@ Create a new processing directory for your project:
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snakedeploy deploy-workflow \
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https://github.com/KosinskiLab/AlphaPulldownSnakemake \
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AlphaPulldownSnakemake \
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--tag 2.1.5
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--tag 2.1.6
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cd AlphaPulldownSnakemake
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```
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@@ -139,7 +139,7 @@ After completion, you'll find:
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- **Optional Jupyter notebook** with 3D visualizations and quality plots
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- **Results table** with confidence scores and interaction metrics
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# New: explore results with APLit
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# Recommended: explore results with APLit
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[APLit](https://github.com/KosinskiLab/aplit)
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is a Streamlit-based UI for browsing AlphaPulldown runs (AF2 and AF3) and AlphaJudge metrics.
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@@ -171,8 +171,11 @@ aplit --directory /path/to/project/output/predictions --no-browser
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# on your laptop
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ssh -N -L 8501:localhost:8501 user@cluster.example.org
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```
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Then open `http://localhost:8501` in your browser.
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---
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## Advanced Configuration
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@@ -188,6 +191,50 @@ feature_directory:
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> **Note**: If your features are compressed, set `compress-features: True` in the config.
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### Feature generation flags (`create_individual_features.py`)
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You can tweak the feature-generation step by editing `create_feature_arguments` (or by running the
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script manually). Commonly used flags:
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- `--data_pipeline {alphafold2,alphafold3}` – choose the feature format to emit.
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- `--db_preset {full_dbs,reduced_dbs}` – switch between the full BFD stack or the reduced databases.
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- `--use_mmseqs2` – rely on the remote MMseqs2 API; skips local jackhmmer/HHsearch database lookups.
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- `--use_precomputed_msas` / `--save_msa_files` – reuse stored MSAs or keep new ones for later runs.
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- `--compress_features` – zip the generated `*.pkl` files (`.xz` extension) to save space.
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- `--skip_existing` – leave existing feature files untouched (safe for reruns).
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- `--seq_index N` – only process the N‑th sequence from the FASTA list.
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- `--use_hhsearch`, `--re_search_templates_mmseqs2` – toggle template search implementations.
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- `--path_to_mmt`, `--description_file`, `--multiple_mmts` – enable TrueMultimer CSV-driven feature sets.
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- `--max_template_date YYYY-MM-DD` – required cutoff for template structures; keeps runs reproducible.
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### Structure analysis & reporting
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Post-inference analysis is enabled by default. You can disable it or add a project-wide summary in `config/config.yaml`:
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```yaml
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enable_structure_analysis: true # skip alphaJudge if set to false
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generate_recursive_report: true # disable if you do not need all_interfaces.csv
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recursive_report_arguments: # optional extra CLI flags for alphajudge
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--models_to_analyse: best
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# SLURM defaults (override to match your cluster)
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slurm_partition: "gpu" # which partition/queue to submit to
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slurm_qos: "normal" # optional QoS if your site uses it
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structure_inference_gpus_per_task: 1 # number of GPUs each inference job needs
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structure_inference_gpu_model: "3090" # optional GPU model constraint (remove to allow any)
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structure_inference_tasks_per_gpu: 0 # <=0 keeps --ntasks-per-gpu unset in the plugin
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```
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`structure_inference_gpus_per_task` and `structure_inference_gpu_model` are read by the
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Snakemake Slurm executor plugin and translated into `--gpus=<model>:<count>` (or `--gpus=<count>` if
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no model is specified). We no longer use `slurm_gres`; requesting GPUs exclusively through these
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fields keeps the job submission consistent across clusters.
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`structure_inference_tasks_per_gpu` toggles whether the plugin also emits `--ntasks-per-gpu`. Leaving
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the default `0` prevents that flag, which avoids conflicting with the Tres-per-task request on many
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systems. Set it to a positive integer only if your site explicitly requires `--ntasks-per-gpu`.
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### Changing Folding Backends
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To use AlphaFold3 or other backends:
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@@ -198,7 +245,7 @@ structure_inference_arguments:
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--<other-flags>
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```
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> **Note**: AlphaPulldown supports: `alphafold2`, `alphafold3` and `alphalink` backends.
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> **Note**: AlphaPulldown supports: `alphafold`, `alphafold3`, `alphalink`, and `unifold` backends.
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### Backend Specific Flags
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@@ -206,7 +253,7 @@ structure_inference_arguments:
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<summary>AlphaFold2 Flags</summary>
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```yaml
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# Whether the result pickles are going to be gzipped.
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# Whether the result pickles are going to be zipped (.xz).
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--compress_result_pickles: False
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# Whether the result pickles are going to be removed.
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@@ -321,11 +368,10 @@ structure_inference_arguments:
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### Database configuration
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Set the path to your AlphaFold databases and weights:
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Set the path to your AlphaFold databases:
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```yaml
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databases_directory: "/path/to/alphafold/databases"
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backend_weights_directory : "/path/to/alphafold/weights"
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```
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***
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