Alphapulldown

🚀 Automated Workflows with Snakemake
Introduced a streamlined pipeline for modeling tasks, leveraging **Snakemake** for automation, scalability, and reproducibility. Installation is now simplified with **Docker/Singularity** containers.

🔧 Unified Configuration Syntax
All AlphaPulldown modes now share a **unified configuration syntax**, simplifying input setup and enabling automatic sequence retrieval with **UniProt IDs**.

🛠️ Modular Backend Support
Reorganized the codebase to support multiple modeling backends flexibly. **UniFold** and **AlphaLink2** are now integrated, with the potential to add more in the future.

🔗 Cross-Link-Driven Modeling
**AlphaLink2** integration supports modeling with cross-linking mass spectrometry (**XL-MS**) data, enhancing the accuracy of complex structural models.

💾 Significant Storage Optimization
Achieved more than **90% reduction** in storage through input feature and output file compression, promoting sustainability in large-scale modeling.

📦 ModelCIF Format Support
Models can now be stored in **ModelCIF format**, aligning with FAIR principles for improved model accessibility and simpler model deposition in databases.

🧩 Extended Modeling Capabilities
Increased flexibility with customizable modeling parameters, multimeric template support, and options to control **MSA** and template impact on predictions.

📊 Enhanced Analysis Pipeline
Enriched the analysis toolkit with additional evaluation metrics like average **pLDDT** and **PAE** scores at protein interfaces for better assessment of model confidence.

🔄 Improved Codebase and Documentation
Refactored code, introduced **CI/CD pipelines**, automated testing, and expanded documentation for a smoother user experience.

🌐 Repository of Precomputed Features
Released a web-based [**repository of precomputed input features**](https://github.com/KosinskiLab/AlphaPulldown?tab=readme-ov-file#features-database) for multiple model organisms, reducing redundant computations and accelerating workflows.

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Release Notes

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**🚀 Automated Workflows with Snakemake**
Introduced a streamlined pipeline for modeling tasks, leveraging Snakemake for automation, scalability, and reproducibility. Installation is now simplified with Docker/Singularity containers.

**🔧 Unified Configuration Syntax**
All AlphaPulldown modes now share a unified configuration syntax, simplifying input setup and enabling automatic sequence retrieval with UniProt IDs.

**🛠️ Modular Backend Support**
Reorganized codebase to support multiple modeling backends flexibly. UniFold and AlphaLink2 are now integrated, with the potential to add more in the future.

**🔗 Cross-Link-Driven Modeling**
AlphaLink2 integration supports modeling with cross-linking mass spectrometry (XL-MS) data, enhancing the accuracy of complex structural models.

**💾 Significant Storage Optimization**
Achieved more than 90% reduction in storage through input feature and output file compression, promoting sustainability in large-scale modeling.

**📦 ModelCIF Format Support**
Models can now be stored in ModelCIF format, aligning with FAIR principles for improved model accessibility and simpler model deposition in databases.

**🧩 Extended Modeling Capabilities**
Increased flexibility with customizable modeling parameters, multimeric template support, and options to control MSA and template impact on predictions.

**📊 Enhanced Analysis Pipeline**
Enriched the analysis toolkit with additional evaluation metrics like average pLDDT and PAE scores at protein interfaces for better assessment of model confidence..

**🔄 Improved Codebase and Documentation**
Refactored code, introduced CI/CD pipelines, automated testing, and expanded documentation.

**🌐 Repository of Precomputed Features**
Released a web-based [repository of precomputed input features](https://github.com/KosinskiLab/AlphaPulldown?tab=readme-ov-file#features-database) for multiple model organisms, reducing redundant computations and accelerating workflows.

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1) Update biopython to the latest version
2) Instructions on how to install cpp4 within the analysis container
3) Fix run_multimer_jobs.py relative paths
4) Remove run_alphafold.py and stereo_chemicsl_props.py from installation
5) Made compatible with python>3.10
6) Multiple minor code optimizations

There is only a single fix: generating features with mmseqs2 previously did not work because of 399
Many thanks to [kashyapchhatbar](https://github.com/kashyapchhatbar) for finding this out and reporting!

1. Merged updated manual, credits to gilep
2. Fixed bug: predictions for homo-oligomers using whole ranges.
3. `ranking_debug.json`: save iptm for multimers OR ptm for monomers.
4. Added `modelcif` dependency to the `pulldown.docker` file and github actions.
5. Used system.entities instead of system.target_entities for `convert_to_modelcif.py`.
6. Added tests for `modelcif` and `run_multimer_jobs.py`.
7. Used the correct docker image from `kosinskilab` for the analysis pipeline.

1. Removed dependencies from the result pickles.
2. Fixed the "key error" while running the create_notebook.py. Thanks for the help from DimaMolod and the report from gebauer
3. Beautified and updated the manual. Thanks for the work from gilep . The updated manual is close to be finished and will be merged soon after the release once it's proof-read.
4. Updated the singularity image of the analysis pipeline so that it handles the exceptions caused by monomeric models.

You could download this version by running pip install alphapulldown==2.0.0b3

**Note**: please download the new singularity image: [alpha-analysis.sif](https://www.embl-hamburg.de/AlphaPulldown/downloads/alpha-analysis.sif) . Since all the dependencies in the result files are removed in this version, there is no need to build two singularity images for jax version 0.3 and jax version 0.4, as in the previous versions.

If you have created models using the versions prior to the alphapulldown==2.0.0b3, please rerun the run_multimer_jobs.py before using the updated the singularity image. The rerun will be quick as only the result pickles are to be changed.