AliCihan/AlphaPulldown

AlphaPulldown

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

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

Installation

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

conda create -n AlphaPulldown -c omnia -c bioconda -c conda-forge python==3.7 openmm pdbfixer kalign2=2.04 cctbx-base

Secondly, activate the AlphaPulldown environment and install AlphaPulldown

source activate AlphaPulldown
pip install alphapulldown
pip install -q "jax[cuda]>=0.3.8,<0.4" -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, HH-suite from Anaconda

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.

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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

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