alphapulldown 2.0.0b1

Pipeline allows massive screening using alphafold

AlphaPulldown

🥳 AlphaPulldown has entered the era of version 1.x

We have brought some exciting useful features to AlphaPulldown and updated its computing environment.

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
• 🆕 integrates cross-link mass spec data with AlphaFold predictions via AlphaLink2 models
• 🆕 able 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. 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

Create Anaconda environment

Firstly, install Anaconda and create AlphaPulldown environment, gathering necessary dependencies

conda create -n AlphaPulldown -c omnia -c bioconda -c conda-forge python==3.10 openmm==8.0 pdbfixer==1.9 kalign2 cctbx-base pytest importlib_metadata hhsuite

Optionally, if you do not have it yet on your system, install HMMER from Anaconda

source activate AlphaPulldown
conda install -c bioconda hmmer

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.

Installation using pip

Activate the AlphaPulldown environment and install AlphaPulldown

source activate AlphaPulldown

python3 -m pip install alphapulldown==1.0.4
pip install jax==0.4.23 jaxlib==0.4.23+cuda11.cudnn86 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

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

How to develop

Follow the instructions at Developing guidelines

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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 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 2 is a case where custom and homo-oligomer modes are used. Manual: example_2

Example 3 is demonstrating the usage of multimeric templates for guiding AlphaFold predictions. Manual: example_3

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:

@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}
}