Metadata-Version: 2.1
Name: alphapulldown
Version: 0.40.4
Summary: Pipeline allows massive screening using alphafold
Home-page: https://github.com/KosinskiLab/AlphaPulldown
Author: Dingquan Yu
Author-email: dingquan.yu@embl-hamburg.de
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: <3.12,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: absl-py>=0.13.0
Requires-Dist: dm-haiku>=0.0.9
Requires-Dist: dm-tree>=0.1.8
Requires-Dist: h5py==3.10.0
Requires-Dist: matplotlib
Requires-Dist: ml-collections>=0.1.1
Requires-Dist: numpy
Requires-Dist: pandas>=1.5.3
Requires-Dist: tensorflow>=2.14.0
Requires-Dist: importlib-resources>=6.1.0
Requires-Dist: biopython>=1.78
Requires-Dist: nbformat>=5.9.2
Requires-Dist: py3Dmol==2.0.4
Requires-Dist: ipython==8.16.1
Requires-Dist: tqdm>=4.66.1
Requires-Dist: appdirs>=1.4.4
Requires-Dist: jupyterlab
Requires-Dist: ipywidgets

# AlphaPulldown
[![Downloads](https://static.pepy.tech/badge/alphapulldown)](https://pepy.tech/project/alphapulldown)  [![python3.10](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-3100/) ![GPL3 license](https://img.shields.io/badge/license-GPLv3-green)

<span style="color:red">*This is a development version of AlphaPulldown! The stable version is available at [0.30.7 branch](https://github.com/KosinskiLab/AlphaPulldown/tree/temp_main)</span>.


AlphaPulldown is a Python package that streamlines protein-protein interaction screens and high-throughput modelling of higher-order oligomers using AlphaFold-Multimer:
* provides a convenient command line interface to screen a bait protein against many candidates, calculate all-versus-all pairwise comparisons, test alternative homo-oligomeric states, and model various parts of a larger complex
* separates the CPU stages (MSA and template feature generation) from GPU stages (the actual modeling)
* allows modeling fragments of proteins without recalculation of MSAs and keeping the original full-length residue numbering in the models
* summarizes the results in a CSV table with AlphaFold scores, pDockQ and mpDockQ, PI-score, and various physical parameters of the interface
* provides a Jupyter notebook for an interactive analysis of PAE plots and models
* (New) allows to integrate experimental models into AlphaFold pipeline using custom multimeric databases

## Pre-installation
Check if you have downloaded necessary parameters and databases (e.g. BFD, MGnify etc.) as instructed in [AlphFold's documentation](https://github.com/deepmind/alphafold). You should have a directory like below:
 ```
 alphafold_database/                             # Total: ~ 2.2 TB (download: 438 GB)
    bfd/                                   # ~ 1.7 TB (download: 271.6 GB)
        # 6 files.
    mgnify/                                # ~ 64 GB (download: 32.9 GB)
        mgy_clusters_2018_12.fa
    params/                                # ~ 3.5 GB (download: 3.5 GB)
        # 5 CASP14 models,
        # 5 pTM models,
        # 5 AlphaFold-Multimer models,
        # LICENSE,
        # = 16 files.
    pdb70/                                 # ~ 56 GB (download: 19.5 GB)
        # 9 files.
    pdb_mmcif/                             # ~ 206 GB (download: 46 GB)
        mmcif_files/
            # About 180,000 .cif files.
        obsolete.dat
    pdb_seqres/                            # ~ 0.2 GB (download: 0.2 GB)
        pdb_seqres.txt
    small_bfd/                             # ~ 17 GB (download: 9.6 GB)
        bfd-first_non_consensus_sequences.fasta
    uniclust30/                            # ~ 86 GB (download: 24.9 GB)
        uniclust30_2018_08/
            # 13 files.
    uniprot/                               # ~ 98.3 GB (download: 49 GB)
        uniprot.fasta
    uniref90/                              # ~ 58 GB (download: 29.7 GB)
        uniref90.fasta
 ```

## Installation using pip

**Firstly**, install [Anaconda](https://www.anaconda.com/) and create AlphaPulldown environment, gathering necessary dependencies 
```bash
conda create -n AlphaPulldown -c omnia -c bioconda -c conda-forge python==3.10 openmm==8.0 pdbfixer==1.9 kalign2 cctbx-base pytest
```

**Secondly**, activate the AlphaPulldown environment and install AlphaPulldown
```bash
source activate AlphaPulldown
python3 -m pip install alphapulldown==1.00.0
pip install jax==0.3.25 jaxlib==0.3.25+cuda11.cudnn805 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```

**Optionally**, if you do not have these software yet on your system, install [HMMER](http://hmmer.org/documentation.html), [HH-suite](https://github.com/soedinglab/hh-suite) from Anaconda
```bash
source activate AlphaPulldown
conda install -c bioconda hmmer hhsuite
```
This usually works, but on some compute systems users may wish to use other versions or optimized builds of already installed HMMER and HH-suite.

**For older versions of AlphaFold**: 
If you haven't updated your databases according to the requirements of AlphaFold 2.3.0, you can still use AlphaPulldown with your older version of AlphaFold database. Please follow the installation instructions on the [dedicated branch](https://github.com/KosinskiLab/AlphaPulldown/tree/AlphaFold-2.2.0)

## How to develop
Follow the instructions at [Developing guidelines](./Developing.md)

------

## Manuals
AlphaPulldown supports four different modes of massive predictions: 

* ```pulldown``` - to screen a list of "bait" proteins against a list or lists of other proteins
* ```all_vs_all``` - to model all pairs of a protein list
* ```homo-oligomer``` - to test alternative oligomeric states
* ```custom``` - to model any combination of proteins and their fragments, such as a pre-defined list of pairs or fragments of a complex

AlphaPulldown will return models of all interactions, summarize results in a score table, and will provide a [Jupyter](https://jupyter.org/) notebook for an interactive analysis, including PAE plots and 3D displays of models colored by chain and pLDDT score.

## Examples

Example 1 is a case where ```pulldown``` mode is used. Manual: [example_1](./example_1.md)

Example 2 is a case where ```custom``` and ```homo-oligomer``` modes are used. Manual: [example_2](./example_2.md) 

Example 3 is demonstrating the usage of multimeric templates for guiding AlphaFold predictions. Manual: [example_3](./example_3.md) 

```all_vs_all``` mode can be viewed as a special case of the ```pulldown``` mode thus the instructions of this mode are added as Appendix in both manuals mentioned above. 

## Citations
If you use this package, please cite as the following:
```python
@Article{AlphaPUlldown,
  author  = {Dingquan Yu, Grzegorz Chojnowski, Maria Rosenthal, and Jan Kosinski},
  journal = {Bioinformatics},
  title   = {AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer},
  year    = {2023},
  volume  = {39},
  issue  = {1},
  doi     = {https://doi.org/10.1093/bioinformatics/btac749}
}
```
