Pipeline allows massive screening using alphafold

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AlphaPulldown: Version 2.0.0

AlphaPulldown fully maintains backward compatibility with input files and scripts from versions 1.x.

AlphaPulldown: Version 2.0.0 (Beta)
- Table of Contents
About AlphaPulldown
- Overview
Alphafold databases
Snakemake AlphaPulldown
Run AlphaPulldown Python Command Line Interface
Downstream analysis
Features Database

About AlphaPulldown

AlphaPulldown is a customized implementation of AlphaFold-Multimer designed for customizable high-throughput screening of protein-protein interactions. It extends AlphaFold’s capabilities by incorporating additional run options, such as customizable multimeric structural templates (TrueMultimer), MMseqs2 multiple sequence alignment (MSA) via ColabFold databases, protein fragment predictions, and the ability to incorporate mass spec data as an input using AlphaLink2.

AlphaPulldown can be used in two ways: either by a two-step pipeline made of python scripts, or by a Snakemake pipeline as a whole. For details on using the Snakemake pipeline, please refer to the separate GitHub repository.

Overview

Shows an illustrated sun in light mode and a moon with stars in dark mode.

Figure 1 Overview of AlphaPulldown worflow

The AlphaPulldown workflow involves the following 3 steps:

Create and store MSA and template features:

In this step, AlphaFold searches preinstalled databases using HMMER for each queried protein sequence and calculates multiple sequence alignments (MSAs) for all found homologs. It also searches for homolog structures to use as templates for feature generation. This step only requires CPU.

Customizable options include:
- To speed up the search process, MMSeq2 can be used instead of the default HHMER.
- Use custom MSA.
- Use a custom structural template, including a multimeric one (TrueMultimer mode).
Structure prediction:

In this step, the AlphaFold neural network runs and produces the final protein structure, requiring GPU. A key strength of AlphaPulldown is its ability to flexibly define how proteins are combined for the structure prediction of protein complexes. Here are the three main approaches you can use:

Figure 2 Three typical scenarios covered by AlphaPulldown
- Single file (custom mode or homo-oligomer mode): Create a file where each row lists the protein sequences you want to predict together or each row tells the program to model homo-oligomers with your specified number of copies.
- Multiple Files (pulldown mode): Provide several files, each containing protein sequences. AlphaPulldown will automatically generate all possible combinations by pairing rows of protein names from each file.
- All versus all: AlphaPulldown will generate all possible non-redundant combinations of proteins in the list.
Additionally, AlphaPulldown also allows:
- Select only region(s) of proteins that you want to predict instead of the full-length sequences.
- Adjust MSA depth to control the influence of the initial MSA on the final model.
- Integrate high-throughput crosslinking data with AlphaFold modeling via AlphaLink2.
Downstream analysis of results:

The results for all predicted models can be systematized using one of the following options:
- A table containing various scores and physical parameters of protein complex interactions.
- A Jupyter notebook with interactive 3D protein models and PAE plots.

Alphafold databases

For the standard MSA and features calculation, AlphaPulldown requires genetic databases. Check if you have downloaded the necessary parameters and databases (e.g., BFD, MGnify, etc.) as instructed in AlphaFold's documentation. You should have a directory structured as follows:

Databases directory

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 227,000 .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
   uniref30/                            # ~ 86 GB (download: 24.9 GB)
           # 14 files.
   uniprot/                               # ~ 98.3 GB (download: 49 GB)
       uniprot.fasta
   uniref90/                              # ~ 58 GB (download: 29.7 GB)
       uniref90.fasta

[!NOTE] Uniclust30 is the version of the database generated before 2019, UniRef30 is the one generated after 2019. Please note that AlphaPulldown is using UniRef30_2023_02 by default. This version can be downloaded by this script. Alternatively, please overwrite the default path to the uniref30 database using the --uniref30_database_path flag of create_individual_features.py.

[!NOTE] Since the local installation of all genetic databases is space-consuming, you can alternatively use the remotely-run MMseqs2 and ColabFold databases. Follow the corresponding instructions. However, for AlphaPulldown to function, you must download the parameters stored in the params/ directory of the AlphaFold database.

Snakemake AlphaPulldown

AlphaPulldown is available as a Snakemake pipeline, allowing you to sequentially execute (1) Generation of MSAs and template features, (2) Structure prediction, and (3) Results analysis without manual intervention between steps. For more details, please refer to the AlphaPulldownSnakemake repository.

[!Warning] The Snakemake version of AlphaPulldown differs slightly from the conventional scripts-based AlphaPulldown in terms of input file specifications.

1. Installation

Before installation, make sure your python version is at least 3.10.

python3 --version

Install Dependencies

pip install snakemake==7.32.4 snakedeploy==0.10.0 pulp==2.7 click==8.1 cookiecutter==2.6

Snakemake Cluster Setup

In order to allow snakemake to interface with a compute cluster, we are going to use the Snakemake-Profile for SLURM. If you are not working on a SLURM cluster you can find profiles for different architectures here. The following will create a profile that can be used with snakemake and prompt you for some additional information.

git clone https://github.com/Snakemake-Profiles/slurm.git
profile_dir="${HOME}/.config/snakemake"
mkdir -p "$profile_dir"
template="gh:Snakemake-Profiles/slurm"
cookiecutter --output-dir "$profile_dir" "$template"

During the setup process, you will be prompted to answer several configuration questions. Below are the questions and the recommended responses:

profile_name [slurm]: slurm_noSidecar
Select use_singularity: 1 (False)
Select use_conda: 1 (False)
jobs [500]: (Press Enter to accept default)
restart_times [0]: (Press Enter to accept default)
max_status_checks_per_second [10]: (Press Enter to accept default)
max_jobs_per_second [10]: (Press Enter to accept default)
latency_wait [5]: 30
Select print_shell_commands: 1 (False)
sbatch_defaults []: qos=low nodes=1
Select cluster_sidecar: 2 (no)
cluster_name []: (Press Enter to leave blank)
cluster_jobname [%r_%w]: (Press Enter to accept default)
cluster_logpath [logs/slurm/%r/%j]: (Press Enter to accept default)
cluster_config []: (Press Enter to leave blank)

After responding to these prompts, your Slurm profile named slurm_noSidecar for Snakemake will be configured as specified.

Singularity (Probably Installed Already): This pipeline makes use of containers for reproducibility. If you are working on the EMBL cluster singularity is already installed and you can skip this step. Otherwise, please install Singularity using the official Singularity guide.

Download The Pipeline: This will download the version specified by '--tag' of the snakemake pipeline and create the repository AlphaPulldownSnakemake or any other name you choose.

snakedeploy deploy-workflow \
  https://github.com/KosinskiLab/AlphaPulldownSnakemake \
  AlphaPulldownSnakemake \
  --tag 1.4.0
cd AlphaPulldownSnakemake

[!NOTE] If you want to use the latest version from GitHub replace --tag X.X.X to --branch main

Install CCP4 package: To install the software needed for the analysis step, please follow these instructions:

Download so-called Singularity image with our analysis software package

singularity pull docker://kosinskilab/fold_analysis:latest
singularity build --sandbox <writable_image_dir> fold_analysis_latest.sif

<writable_image_dir> can be any temporary directory and can be deleted later.

Download CCP4 from https://www.ccp4.ac.uk/download/#os=linux and copy to your server

tar xvzf ccp4-9.0.003-linux64.tar.gz
cd ccp4-9
cp bin/pisa bin/sc <writable_image_dir>/software/
cp /lib/* <writable_image_dir>/software/lib64/

Create a new Singularity with CCP4 included

cd <directory where you want to keep your local software>
singularity build fold_analysis_latest_withCCP4.sif <writable_image_dir>

It should create fold_analysis_latest_withCCP4.sif file.

You can delete the <writable_image_dir> now.

2. Configuration

Adjust config/config.yaml for your particular use case.

If you want to use CCP4 for analysis, open config/config.yaml in a text editor and change the path to the analysis container to:

analysis_container : "/path/to/fold_analysis_latest_withCCP4.sif"

input_files This variable holds the path to your sample sheet, where each line corresponds to a folding job. For this pipeline we use the following format specification:

protein:N:start-stop[_protein:N:start-stop]*

where protein is a path to a file with '.fasta' extension or uniprot ID, N is the number of monomers for this particular protein and start and stop are the residues that should be predicted. However, only protein is required, N, start and stop can be omitted. Hence the following folding jobs for the protein example containing residues 1-50 are equivalent:

example:2
example_example
example:2:1-50
example:1-50_example:1-50
example:1:1-50_example:1:1-50

This format similarly extends for the folding of heteromers:

example1_example2

Assuming you have two sample sheets config/sample_sheet1.csv and config/sample_sheet2.csv. The following would be equivalent to computing all versus all in sample_sheet1.csv:

input_files :
  - config/sample_sheet1.csv
  - config/sample_sheet1.csv

while the snippet below would be equivalent to computing the pulldown between sample_sheet1.csv and sample_sheet2.csv

input_files :
  - config/sample_sheet1.csv
  - config/sample_sheet2.csv

This format can be extended to as many files as you would like, but keep in mind the number of folds will increase dramatically.

input_files :
  - config/sample_sheet1.csv
  - config/sample_sheet2.csv
  - ...

alphafold_data_directory This is the path to your alphafold database.

output_directory Snakemake will write the pipeline output to this directory. If it does not exist, it will be created.

save_msa, use_precomputed_msa, predictions_per_model, number_of_recycles, report_cutoff Command line arguments that were previously pasesed to AlphaPulldown's run_multimer_jobs.py and create_notebook.py (report_cutoff).

alphafold_inference_threads, alphafold_inference Slurm specific parameters that do not need to be modified by non-expert users.

only_generate_features If set to True, stops after generating features and does not perform structure prediction and reporting.

3. Execution

After following the Installation and Configuration steps, you are now ready to run the snakemake pipeline. To do so, navigate into the cloned pipeline directory and run:

snakemake \
  --use-singularity \
  --singularity-args "-B /scratch:/scratch \
    -B /g/kosinski:/g/kosinski \
    --nv " \
  --jobs 200 \
  --restart-times 5 \
  --profile slurm_noSidecar \
  --rerun-incomplete \
  --rerun-triggers mtime \
  --latency-wait 30 \
  -n

Here's a breakdown of what each argument does:

--use-singularity: Enables the use of Singularity containers. This allows for reproducibility and isolation of the pipeline environment.
--singularity-args: Specifies arguments passed directly to Singularity. In the provided example:
- -B /scratch:/scratch and -B /g/kosinski:/g/kosinski: These are bind mount points. They make directories from your host system accessible within the Singularity container. --nv ensures the container can make use of the hosts GPUs.
--profile name_of_your_profile: Specifies the Snakemake profile to use (e.g., the SLURM profile you set up for cluster execution).
--rerun-triggers mtime: Reruns a job if a specific file (trigger) has been modified more recently than the job's output. Here, mtime checks for file modification time.
--jobs 500: Allows up to 500 jobs to be submitted to the cluster simultaneously.
--restart-times 10: Specifies that jobs can be automatically restarted up to 10 times if they fail.
--rerun-incomplete: Forces the rerun of any jobs that were left incomplete in previous Snakemake runs.
--latency-wait 30: Waits for 30 seconds after a step finishes to check for the existence of expected output files. This can be useful in file-systems with high latencies.
-n: Dry-run flag. This makes Snakemake display the commands it would run without actually executing them. It's useful for testing. To run the pipeline for real, simply remove this flag.

Executing the command above will perform submit the following jobs to the cluster:

Snakemake rulegraph

Run AlphaPulldown Python Command Line Interface

AlphaPulldown can be used as a set of scripts for every particular step.

create_individual_features.py: Generates multiple sequence alignments (MSA), identifies structural templates, and stores the results in monomeric feature .pkl files.
run_multimer_jobs.py: Executes the prediction of structures.
create_notebook.py and alpha-analysis.sif: Prepares an interactive Jupyter Notebook and a Results Table, respectively.

0. Installation

0.1. Create Anaconda environment

Firstly, install Anaconda and create an AlphaPulldown environment, gathering necessary dependencies. We recommend to use mamba to speed up solving of dependencies:

conda create -n AlphaPulldown -c omnia -c bioconda -c conda-forge python==3.11 openmm==8.0 pdbfixer==1.9 kalign2 hhsuite hmmer modelcif

source activate AlphaPulldown

This usually works, but on some compute systems, users may prefer to use other versions or optimized builds of HMMER and HH-suite that are already installed.

0.2. Installation using pip

Activate the AlphaPulldown environment and install AlphaPulldown:

source activate AlphaPulldown
python3 -m pip install alphapulldown

pip install -U "jax[cuda12]"

[!NOTE] 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 the AlphaFold database. Please follow the installation instructions on the dedicated branch.

0.3. Installation for the Downstream analysis tools

Install CCP4 package: To install the software needed for the anaysis step, please follow these instructions:

singularity pull docker://kosinskilab/fold_analysis:latest
singularity build --sandbox <writable_image_dir> fold_analysis_latest.sif
# Download the top one from https://www.ccp4.ac.uk/download/#os=linux
tar xvzf ccp4-9.0.003-linux64.tar.gz
cd ccp4-9
cp bin/pisa bin/sc <writable_image_dir>/software/
cp /lib/* <writable_image_dir>/software/lib64/
singularity build <new_image.sif> <writable_image_dir>

0.4. Installation for cross-link input data by AlphaLink2 (optional!)

$\text{\color{red}Update the installation manual after resolving the dependency conflict.}$

Make sure you have installed PyTorch corresponding to the CUDA version you have. Here will take CUDA 11.7 and PyTorch 1.13.0 as an example:
```
pip install torch==1.13.0+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
```

Compile UniCore.

source activate AlphaPulldown
git clone https://github.com/dptech-corp/Uni-Core.git
cd Uni-Core
python setup.py install --disable-cuda-ext
    
# test whether unicore is successfully installed
python -c "import unicore"

You may see the following warning, but it's fine:

fused_multi_tensor is not installed corrected
fused_rounding is not installed corrected
fused_layer_norm is not installed corrected
fused_softmax is not installed corrected

Download the PyTorch checkpoints from Zenodo, unzip it, then you should obtain a file named: AlphaLink-Multimer_SDA_v3.pt

0.5. Installation for developers

Only for the developers who would like to modify AlphaPulldown's codes and test their modifications. Please add your SSH key to your GitHub account

Instructions

Clone the GitHub repo

git clone --recurse-submodules git@github.com:KosinskiLab/AlphaPulldown.git
cd AlphaPulldown 
git submodule init
git submodule update

Create the Conda environment as described in Create Anaconda environment

Install AlphaPulldown package and add its submodules to the Conda environment (does not work if you want to update the dependencies)

source activate AlphaPulldown
cd AlphaPulldown
pip install .
pip install -e . --no-deps
pip install -e ColabFold --no-deps
pip install -e alphafold --no-deps

You need to do it only once.

When you want to develop, activate the environment, modify files, and the changes should be automatically recognized.

Test your package during development using tests in test/, e.g.:

pip install pytest
pytest -s test/
pytest -s test/test_predictions_slurm.py
pytest -s test/test_features_with_templates.py::TestCreateIndividualFeaturesWithTemplates::test_1a_run_features_generation

Before pushing to the remote or submitting a pull request:
```
pip install .
pytest -s test/
```
to install the package and test. Pytest for predictions only works if SLURM is available. Check the created log files in your current directory.

1. Compute multiple sequence alignment (MSA) and template features (CPU stage)

[!Note] If you work with proteins from model organisms you can directly download the features files from the AlphaPulldown Features Database and skip this step.

1.1. Basic run

This is a general example of create_individual_features.py usage. For information on running specific tasks or parallel execution on a cluster, please refer to the corresponding sections of this chapter.

Input

At this step, you need to provide a protein FASTA format file with all protein sequences that will be used for complex prediction.

Example of a FASTA file (sequences.fasta):

>proteinA
SEQUENCEOFPROTEINA
>proteinB
SEQUENCEOFPROTEINB

Script Execution

Activate the AlphaPulldown environment and run the script create_individual_features.py as follows:

source activate AlphaPulldown
create_individual_features.py \
  --fasta_paths=<sequences.fasta> \
  --data_dir=<path to alphafold databases> \
  --output_dir=<dir to save the output objects> \ 
  --max_template_date=<any date you want, format like: 2050-01-01> \

Instead of <sequences.fasta> provide a path to your input FASTA file. You can also provide multiple comma-separated files.
Instead of <path to alphafold databases> provide a path to the genetic database (see 0. Alphafold-databases of the installation part).
Instead of <dir to save the output objects> provide a path to the output directory, where your features files will be saved.
A date in the flag --max_template_date is needed to restrict the search of protein structures that are deposited before the indicated date. Unless the date is later than the date of your local genomic database's last update, the script will search for templates among all available structures.

Features calculation script create_individual_features.py has several optional FLAGS:

Full list of arguments (FLAGS):

--[no]save_msa_files: By default is False to save storage stage but can be changed into True. If it is set to True, the program will create an individual folder for each protein. The output directory will look like:

output_dir
     |- proteinA.pkl
     |- proteinA
           |- uniref90_hits.sto
           |- pdb_hits.sto
           |- etc.
     |- proteinB.pkl
     |- proteinB
           |- uniref90_hits.sto
           |- pdb_hits.sto
           |- etc.

If save_msa_files=False then the output_dir will look like:

output_dir
     |- proteinA.pkl
     |- proteinB.pkl

--[no]use_precomputed_msas: Default value is False. However, if you have already had MSA files for your proteins, please set the parameter to be True and arrange your MSA files in the format as below:
```
example_directory
     |- proteinA 
           |- uniref90_hits.sto
           |- pdb_hits.sto
           |- ***.a3m
           |- etc
     |- proteinB
           |- ***.sto
           |- etc
```
Then, in the command line, set the output_dir=/path/to/example_directory.
--[no]skip_existing: Default is False but if you have run the 1st step already for some proteins and now add new proteins to the list, you can change skip_existing to True in the command line to avoid rerunning the same procedure for the previously calculated proteins.
--seq_index: Default is None and the program will run predictions one by one in the given files. However, you can set seq_index to a different number if you wish to run an array of jobs in parallel then the program will only run the corresponding job specified by the seq_index. e.g. the program only calculates features for the 1st protein in your FASTA file if seq_index is set to be 1. See also the Slurm sbatch script above for an example of how to use it for parallel execution. :exclamation: seq_index starts from 1.
--[no]use_mmseqs2: Use mmseqs2 remotely or not. Default is False.

FLAGS related to TrueMultimer mode:

--path_to_mmt: Path to directory with multimeric template mmCIF files.
--description_file: Path to the text file with descriptions for generating features. Please note, the first column must be an exact copy of the protein description from your FASTA files. Please consider shortening them in FASTA files using your favorite text editor for convenience. These names will be used to generate pickle files with monomeric features! The description.csv for the NS1-P85B complex should look like:

>sp|P03496|NS1_I34A1,3L4Q.cif,A
>sp|P23726|P85B_BOVIN,3L4Q.cif,C

In this example, we refer to the NS1 protein as chain A and to the P85B protein as chain C in multimeric template 3L4Q.cif.

Please note, that your template will be renamed to a PDB code taken from _entry_id. If you use a *.pdb file instead of *.cif, AlphaPulldown will first try to parse the PDB code from the file. Then it will check if the filename is 4-letter long. If it is not, it will generate a random 4-letter code and use it as the PDB code.

--threshold_clashes: Threshold for VDW overlap to identify clashes. The VDW overlap between two atoms is defined as the sum of their VDW radii minus the distance between their centers. If the overlap exceeds this threshold, the two atoms are considered to be clashing. A positive threshold is how far the VDW surfaces are allowed to interpenetrate before considering the atoms to be clashing. (default: 1000, i.e. no threshold, for thresholding, use 0.6-0.9).
--hb_allowance: Additional allowance for hydrogen bonding (default: 0.4) used for identifying clashing residues to be removed from a multimeric template. An allowance > 0 reflects the observation that atoms sharing a hydrogen bond can come closer to each other than would be expected from their VDW radii. The allowance is only subtracted for pairs comprised of a donor (or donor-borne hydrogen) and an acceptor. This is equivalent to using smaller radii to characterize hydrogen-bonding interactions.
--plddt_threshold: Threshold for pLDDT score (default: 0) to be removed from a multimeric template (all residues with pLDDT>plddt_threshold are removed and modeled from scratch). Can be used only when multimeric templates are models generated by AlphaFold.
--new_uniclust_dir: Please use this if you want to overwrite the default path to the uniclust database.
--[no]use_hhsearch: Use hhsearch instead of hmmsearch when looking for structure template. Default is False.
--[no]multiple_mmts: Use multiple multimeric templates per chain. Default is False.

Output

The result of create_individual_features.py run is pickle format features for each protein from the input FASTA file (e.g. sequence_name_A.pkl and sequence_name_B.pkl) stored in the output_dir.

[!NOTE] The name of the pickles will be the same as the descriptions of the sequences in FASTA files (e.g. >protein_A in the FASTA file will yield proteinA.pkl). Note that special symbols such as | : ; #, after > will be replaced with _.

Next step

Proceed to the next step 2.1 Basic Run.

1.2. Example bash scripts for SLURM (EMBL cluster)

If you run AlphaPulldown on a computer cluster, you may want to execute feature creation in parallel. Here, we provide an example of code that is suitable for a cluster that utilizes SLURM Workload Manager.

For more details about the SLURM on the EMBL cluster, please refer to the [EMBL Cluster wiki](https://wiki.embl.de/cluster/Main_Page) using the EMBL network.

Input

For the following example, we will use example_2_sequences.fasta as input.

Script Execution

Create the create_individual_features_SLURM.sh script and place the following code in it using vi, nano, or any other text editor. Replace input parameters with the appropriate arguments for the create_individual_features.py script as described in Basic run or any other type of run you intend to execute:

#!/bin/bash

#A typical run takes a couple of hours but may be much longer
#SBATCH --job-name=array
#SBATCH --time=10:00:00

#log files:
#SBATCH -e logs/create_individual_features_%A_%a_err.txt
#SBATCH -o logs/create_individual_features_%A_%a_out.txt

#qos sets priority
#SBATCH --qos=low

#Limit the run to a single node
#SBATCH -N 1

#Adjust this depending on the node
#SBATCH --ntasks=8
#SBATCH --mem=64000

module load HMMER/3.4-gompi-2023a
module load HH-suite/3.3.0-gompi-2023a
eval "$(conda shell.bash hook)"
module load CUDA/11.8.0
module load cuDNN/8.7.0.84-CUDA-11.8.0
conda activate AlphaPulldown

# CUSTOMIZE THE FOLLOWING SCRIPT PARAMETERS FOR YOUR SPECIFIC TASK:
####
create_individual_features.py \
  --fasta_paths=example_1_sequences.fasta \
  --data_dir=/scratch/AlphaFold_DBs/2.3.2

/ \
  --output_dir=/scratch/mydir/test_AlphaPulldown/ \
  --max_template_date=2050-01-01 \
  --skip_existing=True \
  --seq_index=$SLURM_ARRAY_TASK_ID
#####

Make the script executable by running:

chmod +x create_individual_features_SLURM.sh

Next, execute the following commands, replacing <sequences.fasta> with the path to your input FASTA file:

mkdir logs
#Count the number of jobs corresponding to the number of sequences:
count=`grep ">" <sequences.fasta> | wc -l`
#Run the job array, 100 jobs at a time:
sbatch --array=1-$count%100 create_individual_features_SLURM.sh

If you have several FASTA files, use the following commands:

Example for two files (For more files, create count3, count4, etc., variables and add them to the sum of counts):

mkdir logs
#Count the number of jobs corresponding to the number of sequences:
count1=`grep ">" <sequences1.fasta> | wc -l`
count2=`grep ">" <sequences2.fasta> | wc -l`
count=$(( $count1 + $count2 )) 
#Run the job array, 100 jobs at a time:
sbatch --array=1-$count%100 create_individual_features_SLURM.sh

Next step

Proceed to the next step 2.2 Example run with SLURM (EMBL cluster).

1.3. Run using MMseqs2 and ColabFold Databases (Faster)

MMseqs2 is another method for homolog search and MSA generation. It offers an alternative to the default HMMER and HHblits used by AlphaFold. The results of these different approaches might lead to slightly different protein structure predictions due to variations in the captured evolutionary information within the MSAs. AlphaPulldown supports the implementation of MMseqs2 search made by ColabFold, which also provides a web server for MSA generation, so no local installation of databases is needed.

Cite: If you use MMseqs2, please remember to cite: Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. Nature Methods (2022) doi: 10.1038/s41592-022-01488-1

Run MMseqs2 Remotely

CAUTION: To avoid overloading the remote server, do not submit a large number of jobs simultaneously. If you want to calculate MSAs for many sequences, please use MMseqs2 locally.

To run create_individual_features.py using MMseqs2 remotely, add the --use_mmseqs2=True flag:

source activate AlphaPulldown
create_individual_features.py \
  --fasta_paths=<sequences.fasta> \
  --data_dir=<path to alphafold databases> \
  --output_dir=<dir to save the output objects> \
  --use_mmseqs2=True \
  --max_template_date=<any date you want, format like: 2050-01-01> \

Output

After the script run is finished, your output_dir will look like this:

output_dir
   |-proteinA.a3m
   |-proteinA_env/
   |-proteinA.pkl
   |-proteinB.a3m
   |-proteinB_env/
   |-proteinB.pkl
   ...

Proceed to the next step 2.1 Basic Run.

Run MMseqs2 Locally

AlphaPulldown does NOT provide an interface or code to run MMseqs2 locally, nor will it install MMseqs2 or any other required programs. The user must install MMseqs2, ColabFold databases, ColabFold search, and other required dependencies and run MSA alignments first. An example guide can be found on the ColabFold GitHub.

Suppose you have successfully run MMseqs2 locally using the colab_search program; it will generate an A3M file for each protein of your interest. Thus, your output_dir should look like this:

output_dir
   |-0.a3m
   |-1.a3m
   |-2.a3m
   |-3.a3m
   ...

These a3m files from colabfold_search are inconveniently named. Thus, we have provided a rename_colab_search_a3m.py script to help you rename all these files. Download the script from https://github.com/KosinskiLab/AlphaPulldown/blob/main/alphapulldown/scripts/rename_colab_search_a3m.py and run:

cd output_dir
python rename_colab_search_a3m.py path_to_fasta_file_you_used_as_input_for_colabfold_search

Then your output_dir will become:

output_dir
   |-proteinA.a3m
   |-proteinB.a3m
   |-proteinC.a3m
   |-proteinD.a3m
   ...

Here, proteinA, proteinB, etc., correspond to the names in your input FASTA file (e.g., >proteinA will give you proteinA.a3m, >proteinB will give you proteinB.a3m, etc.).

NOTE: You can also provide your own custom MSA file in .a3m format instead of using the files created by MMSeq2 or standard HHMER. Place appropriately named files in the output directory and use the code as follows.

After this, go back to your project directory with the original FASTA file and point to this directory in the command:

source activate AlphaPulldown
create_individual_features.py \
  --fasta_paths=<sequences.fasta> \
  --data_dir=<path to alphafold databases> \
  --output_dir=<output_dir> \
  --skip_existing=False \
  --use_mmseqs2=True \
  --seq_index=<any number you want or skip the flag to run all one after another>

AlphaPulldown will automatically search each protein's corresponding a3m files. In the end, your output_dir will look like:

output_dir
   |-proteinA.a3m
   |-proteinA.pkl
   |-proteinB.a3m
   |-proteinB.pkl
   |-proteinC.a3m
   |-proteinC.pkl
   ...

Next step

Proceed to the next step 2.1 Basic Run.

1.4. Run with custom templates (TrueMultimer)

Instead of using the default search through the PDB database for structural templates, you can provide a custom database. AlphaPulldown supports a feature called "True Multimer," which allows AlphaFold to use multi-chain structural templates during the prediction process. This can be beneficial for protein complexes where the arrangement of the chains may vary.

Details

Input

Prepare a FASTA File: Create a FASTA file containing all protein sequences that will be used for predictions as outlined in 1.1 Basic run.
Create a Template Directory: Designate a folder (e.g., templates) to store your custom template files in PDB or CIF format.
Create a description file: This description.csv file links protein sequences to templates:

>proteinA,TMPL.cif,A
>proteinB,TMPL.cif,B

Column 1 (>proteinA): Must exactly match protein descriptions from your FASTA file, including the > symbol.
Column 2 (TMPL.cif): The filename of your template structure in PDB or CIF format.
Column 3 (A): The chain ID within the template that the query sequence corresponds to.

The result pkl files will be stored as: proteinA.TMPL.cif.A.pkl and proteinA.TMPL.cif.A.pkl.

For Multiple Templates: If you want to provide multiple templates for a single protein, add additional rows with the same protein name but different templates and chain IDs and use flag --multiple_template=True:

>proteinA,TMPL.cif,A
>proteinA,TMP2.cif,B
>proteinB,TMPL.cif,B

CAUTION: Even though you can use multiple templates for a single chain, this feature is not fully supported in AlphaPulldown yet and might bring unexpected results.

NOTE: Your template will be renamed to a PDB code taken from _entry_id. If you use a *.pdb file instead of *.cif, AlphaPulldown will first try to parse the PDB code from the file. Then it will check if the filename is 4-letter long. If it is not, it will generate a random 4-letter code and use it as the PDB code.

Script Execution

Tip: If you have already generated feature files (.pkl) for protein sequences without custom templates or with the

different templates, instead of generating them once again with the new template, you can go directly to the prediction step and use templates and description.csv in combination with previously generated features. During the run, it will replace templates in the features .pkl file with the new one.

Run the following script:

create_individual_features.py \
  --fasta_paths=<sequences.fasta> \
  --path_to_mmt=<path to template directory> \
  --description_file=<description.csv> \
  --data_dir=<path to alphafold databases> \
  --output_dir=<dir to save the output objects> \
  --max_template_date=<any date you want, format like: 2050-01-01> \
  --save_msa_files=True \
  --use_precomputed_msas=True \
  --skip_existing=True

Compared to 1.1. Basic run, this example differs in:

--path_to_mmt=<path to template directory>: Path to your templates directory with custom template files in PDB or CIF format.
--description_file=<description.csv>: Path to the description file.

Warning! Feature files generated with custom templates have the same names as standard feature files. To avoid overwriting existing feature files or skipping their generation if --skip_existing=True, please create a separate directory to save custom features.

Output

Pickle format features for each protein in the description.csv file stored in the output_dir.

Next step

Go to the next step 2.4. Run with custom templates (TrueMultimer).

2. Predict structures (GPU stage)

2.1. Basic run

This is a general example of run_multimer_jobs.py usage. For information on running specific tasks or parallel execution on a cluster, please refer to the corresponding sections of this chapter.

Input

This step requires the pickle files (.pkl) generated during the first step. Additionally, you'll need to provide a list of protein combinations protein_list.txt you intend to predict.

Here's how to structure your combinations file protein_list.txt. Protein names should correspond to the names of features files (proteinA for proteinA.pkl):

Prediction of monomers:

proteinA
proteinB
proteinC,1-100

Each line is a separate prediction.
Lines like proteinA will trigger a prediction of the entire sequence.
To predict specific residue ranges (e.g., the first 100 residues of proteinC), use the format "proteinC,1-100".

Prediction of complexes:

proteinA;proteinB;proteinC
proteinB;proteinB 
proteinB,4
proteinC,2;proteinA
proteinA,4,1-100;proteinB

Use semicolons (;) to separate protein names within a complex.
Instead of repeating the protein name for homo-oligomers, specify the number of copies after the protein's name (e.g., proteinB,4 for a tetramer).
Combine residue ranges and homooligomer notation for specific predictions (e.g., proteinA,4,1-100;proteinB).

Script Execution: Structure Prediction

To predict structures, activate the AlphaPulldown environment and run the script run_multimer_jobs.py as follows:

source activate AlphaPulldown
run_multimer_jobs.py \
  --mode=custom \
  --monomer_objects_dir=<dir that stores feature pickle files> \
  --data_dir=<path to alphafold databases> I am running a few minutes late; my previous meeting is running over.
  --protein_lists=<protein_list.txt> \
  --output_path=<path to output directory> \ 
  --num_cycle=<any number e.g. 3> \
  --num_predictions_per_model=1

Explanation of arguments:

Instead of <dir that stores feature pickle files> provide the path to the directory containing the .pkl feature files generated in the first step. The path is the same as --output_dir for create_individual_features.py.
Instead of <protein_list.txt> provide the path to a text file containing a list of protein combinations to be modeled.
Instead of <path to output directory> provide a path where subdirectories containing the final structures will be saved.
Instead of <path to alphafold databases> provide a path to the genetic database (see 0. Alphafold-databases of the installation part).
--num_cycle: specifies the number of times the AlphaFold neural network will run, using the output of one cycle as input for the next. Increasing this number may improve the quality of the final structures (especially for large complexes), but it will also increase the runtime.
--num_predictions_per_model: Specifies the number of predictions per model. The number of predicted structures is N*5. The default value is 1, which gives 5 structures.

Full list of arguments (FLAGS):

--alphalink_weight: Path to AlphaLink neural network weights
--data_dir: Path to params directory
--[no]dry_run: Report number of jobs that would be run and exit without running them. Default is False.
--job_index: index of sequence in the FASTA file, starting from 1 (an integer)
--mode: <pulldown|all_vs_all|homo-oligomer|custom>: choose the mode of running multimer jobs (default: 'pulldown')
--models_to_relax: <None|All|Best>: Which models to relax. Default is None, meaning no model will be relaxed (default: 'None')
--monomer_objects_dir: a list of directories where monomer objects are stored (a comma-separated list)
--oligomer_state_file: path to oligomer state files
--output_path: output directory where the region data is going to be stored
--protein_lists: protein list files (a comma-separated list)
--unifold_model_name: <multimer_af2|multimer_ft|multimer|multimer_af2_v3|multimer_af2_model45_v3>: choose unifold model structure (default: 'multimer_af2')
--unifold_param: Path to UniFold neural network weights
--[no]use_alphalink: Whether AlphaLink models are going to be used. Default is False (default: 'false')
--[no]use_unifold: Whether UniFold models are going to be used. Default is False (default: 'false')

alphapulldown.scripts.run_structure_prediction:

--[no]benchmark: Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins (default: 'false')
--[no]compress_result_pickles: Whether the result pickles are going to be gzipped. Default is False (default: 'false')
--crosslinks: Path to crosslink information pickle for AlphaLink
--data_directory: Path to directory containing model weights and parameters
--description_file: Path to the text file with multimeric template instruction
--desired_num_msa: A desired number of msa to pad (an integer)
--desired_num_res: A desired number of residues to pad (an integer)
--features_directory: Path to computed monomer features; repeat this option to specify a list of values
--fold_backend: Folding backend that should be used for structure prediction (default: 'alphafold')
-i,--input: Folds in format [fasta_path:number:start-stop],; repeat this option to specify a list of values
--model_names: Names of models to use, e.g. model_2_multimer_v3 (default: all models)
--model_preset: <monomer|monomer_casp14|monomer_ptm|multimer>: Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model (default: 'monomer')
--msa_depth: Number of sequences to use from the MSA (by default is taken from AF model config) (an integer)
--[no]msa_depth_scan: Run predictions for each model with logarithmically distributed MSA depth (default: 'false')
--[no]multimeric_template: Whether to use multimeric templates (default: 'false')
--[no]no_pair_msa: Do not pair the MSAs when constructing multimer objects (default: 'false')
--num_cycle: Number of recycles, defaults to 3 (default: '3') (an integer)
--num_predictions_per_model: Number of predictions per model, defaults to 1 (default: '1') (an integer)
-o,--output_directory: Path to output directory. Will be created if not exists
--path_to_mmt: Path to directory with multimeric template mmCIF files
--protein_delimiter: Delimiter for proteins of a single fold (default: '+')
`

--random_seed`: The random seed for the data pipeline. By default, this is randomly generated. Note that even if this is set, Alphafold may still not be deterministic, because processes like GPU inference are nondeterministic (an integer)

--[no]remove_result_pickles: Whether the result pickles are going to be removed (default: 'true')
--[no]skip_templates: Do not use template features when modeling (default: 'false')
--[no]use_ap_style: Change output directory to include a description of the fold as seen in previous alphapulldown versions (default: 'false')
--[no]use_gpu_relax: Whether to run Amber relaxation on GPU. Default is True (default: 'true')

Output

The run_multimer_jobs.py script generates a set of directories for each specified protein complex. The full structure of the output directories is the following:

<complex_name>/
    <complex_name>_PAE_plot_ranked_{0,1,2,3,4}.png
    ranked_{0,1,2,3,4}.pdb
    ranked_{0,1,2,3,4}.cif
    ranked_{0,1,2,3,4}.zip
    ranking_debug.json
    result_model_{1,2,3,4,5}_*.pkl
    timings.json
    confidence_model_{1,2,3,4,5}.json
    pae_model_{1,2,3,4,5}_ptm_pred_0.json
    unrelaxed_model_{1,2,3,4,5}_*.pdb

Please refer to the AlphaFold manual for more details on output files.

Key files:

ranked_{0,1,2,3,4}.cif: Structure files ranked from best to worst predicted quality in modelcif format.
<complex_name>_PAE_plot_ranked_{0,1,2,3,4}.png: Plots of predicted aligned errors (PAE) providing a visual representation of the structure's confidence.

[!Caution] AlphaPulldown is designed for screening, so its default output doesn't relax structures. To relax the top-ranked structure (ranked_0.pdb), you can run AlphaPulldown with the --models_to_relax=Best flag, or --models_to_relax=All to relax all models.

Next step

Proceed to the next step 3. Analysis and Visualization.

The results of structure predictions can be very large. To copy the cropped output results for storage, use the truncate_pickles.py script by following the instructions provided Decrease the size of AlphaPulldown output.

Additionally, you can prepare the structures for deposition by creating .cif files containing all the necessary information about your predicted models. To do this, follow the instructions provided Convert Models from PDB Format to ModelCIF Format.

2.2. Example run with SLURM (EMBL cluster)

Details

Input

For this step, you need an example input file: custom_mode.txt and features (.pkl) files generated in the previous step 1.2. Example run with SLURM (EMBL cluster).

Script Execution

Create the run_multimer_jobs_SLURM.sh script and place the following code in it using vi, nano, or any other text editor. Replace input parameters with the appropriate arguments for the run_multimer_jobs.sh script as described in Basic run or any other type of run you intend to execute:

#!/bin/bash

#A typical run takes couple of hours but may be much longer
#SBATCH --job-name=array
#SBATCH --time=2-00:00:00

#log files:
#SBATCH -e logs/run_multimer_jobs_%A_%a_err.txt
#SBATCH -o logs/run_multimer_jobs_%A_%a_out.txt

#qos sets priority
#SBATCH --qos=low

#SBATCH -p gpu
#lower end GPUs might be sufficient for pairwise screens:
#SBATCH -C "gpu=2080Ti|gpu=3090"

#Reserve the entire GPU so no-one else slows you down
#SBATCH --gres=gpu:1

#Limit the run to a single node
#SBATCH -N 1

#Adjust this depending on the node
#SBATCH --ntasks=8
#SBATCH --mem=64000

module load Anaconda3 
module load CUDA/11.8.0
module load cuDNN/8.7.0.84-CUDA-11.8.0
source activate AlphaPulldown

MAXRAM=$(echo `ulimit -m` '/ 1024.0'|bc)
GPUMEM=`nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits|tail -1`
export XLA_PYTHON_CLIENT_MEM_FRACTION=`echo "scale=3;$MAXRAM / $GPUMEM"|bc`
export TF_FORCE_UNIFIED_MEMORY='1'

# CUSTOMIZE THE FOLLOWING SCRIPT PARAMETERS FOR YOUR SPECIFIC TASK:
####
run_multimer_jobs.py \
  --mode=custom \
  --monomer_objects_dir=<dir that stores feature pickle files> \
  --protein_lists=<protein_list.txt> \
  --output_path=<path to output directory> \
  --num_cycle=<any number e.g. 3> \
  --data_dir=/scratch/AlphaFold_DBs/2.3.2/ \
  --num_predictions_per_model=1 \
  --job_index=$SLURM_ARRAY_TASK_ID
####

Make the script executable by running:

chmod +x run_multimer_jobs_SLURM.sh

Next, for custom mode, execute the following commands, replacing <protein_list.txt> with the path to your protein combinations file:

mkdir -p logs
#Count the number of jobs corresponding to the number of sequences:
count=`grep -c "" <protein_list.txt>` #count lines even if the last one has no end of line
sbatch --array=1-$count example_data/run_multimer_jobs_SLURM.sh

For pulldown mode for two files (for more files, create count3, count4, etc. variables and add them as a multiplier to the product):

mkdir -p logs
#Count the number of jobs corresponding to the number of sequences:
count1=`grep -c "" <protein_list1.txt>` #count lines even if the last one has no end of line
count2=`grep -c "" <protein_list2.txt>` #count lines even if the last one has no end of line
count=$(( $count1 * $count2 ))
sbatch --array=1-$count example_data/run_multimer_jobs_SLURM.sh

For all_vs_all mode:

mkdir -p logs
#Count the number of jobs corresponding to the number of sequences:
count1=`grep -c "" <protein_list.txt>` #count lines even if the last one has no end of line
count=$(( $count1 * ( $count1 + 1) / 2 ))
sbatch --array=1-$count example_data/run_multimer_jobs_SLURM.sh

Output and the next step

The output and next step are the same as those for the 2.1. Basic Run.

2.3. Pulldown mode

Instead of manually typing all combinations of proteins, AlphaPulldown provides two different modes of automatic generation of such combinations.

Multiple inputs "pulldown" mode

This mode allows providing two or more lists of proteins that will generate all combinations of proteins from one list with all proteins from another list. If you want to emulate in silico pulldown of some hypothetical proteinA bait against proteins B-G you can use two protein_list.txt files:

The first protein_list1.txt:

proteinA

The second protein_list2.txt:

proteinB
proteinC
proteinD
proteinE
proteinF
proteinG

This results in the following combinations of proteins: A-B, A-C, A-D, A-E, A-F, A-G.

You can add a third protein_list3.txt:

proteinX


proteinZ

The resulting models will contain proteins A-B-X, A-B-Z, A-C-X, A-C-Z...

In fact, you can provide as many files as you wish, the number of combinations you will receive is the product of numbers of lines in the input files.

Lines in the files do not necessarily have to be single proteins. Input files follow the same rules as described for 2.1 Basic run. It can contain several protein names, indicate a number of oligomers, and have residue ranges.

To run run_multimer_jobs.py in pulldown mode, use the following script:

run_multimer_jobs.py \
  --mode=pulldown \
  --monomer_objects_dir=<dir that stores feature pickle files> \
  --protein_lists=<protein_list1.txt>,<protein_list2.txt> \
  --output_path=<path to output directory> \
  --data_dir=<path to AlphaFold data directory> \
  --num_cycle=<any number e.g. 3>

Compared to 2.1 Basic run, this example differs in:

--mode=pulldown flag that defines the mode of the run.
Instead of <protein_list1.txt>,<protein_list2.txt>, provide the paths to the files containing the list of protein combinations to be modeled.

2.4. All versus All mode

In this mode, AlphaPulldown takes lines from the input protein_list.txt file and generates all possible combinations of these lines. It is useful when you have a set of proteins, and you want to find out which interact with which.

If you provide the list of proteins:

proteinA
proteinB
proteinC
proteinD
proteinE

The resulting models will be all possible combinations of proteins A-A, A-B, A-C, A-D, A-E, B-B, B-C, B-D, B-E, C-C, C-D...

[!Caution] The number of predictions rapidly increases with the number of lines in the input protein_list.txt.

Lines in files should not necessarily be single proteins. Input files follow the same rules as described for 2.1 Basic run. It can contain several protein names, indicate a number of oligomers, and have residue ranges.

To run run_multimer_jobs.py in all_vs_all mode, use the following script:

run_multimer_jobs.py \
  --mode=all_vs_all \
  --monomer_objects_dir=<dir that stores feature pickle files> \
  --protein_lists=<protein_list.txt> \
  --output_path=<path to output directory> \
  --data_dir=<path to AlphaFold data directory> \
  --num_cycle=<any number e.g. 3>

Compared to 2.1 Basic run, this example differs in:

--mode=all_vs_all flag that defines the mode of the run.

Output and the next step

The output and next step are the same as those for the 2.1. Basic Run.

2.5. Run with Custom Templates (TrueMultimer)

If you have some experimental models you want to integrate into AlphaFold pipeline to guide the modeling, you can do so using custom multimeric databases created on the fly.

Input

This step requires the feature files (.pkl) generated with custom templates during the first step. Additionally, you'll need to provide a list of protein combinations in protein_list.txt that you intend to predict.

Alternatively, if you haven't generated feature files with a custom template but have feature files from the 1.1. Basic Run or features with different templates, you can provide those feature files in combination with the templates directory containing custom templates and description.csv as outlined in 1.4. Run with Custom Templates (TrueMultimer).

Script Execution for TrueMultimer Structure Prediction

Run the script create_individual_features.py as described in the 2.1. Basic Run but with the flag --multimeric_mode=True.

[!NOTE] To increase the impact of the custom templates on the final prediction (making the model more similar to the template), you can decrease the influence of the MSA by specifying the MSA depth with the --msa_depth=<number> flag.

Alternatively, run the script create_individual_features.py with features, new templates, and a description file by adding the following flags:

--path_to_mmt=<path to template directory>: Path to your templates directory with custom template files in PDB or CIF format. Same as for 1.4. Run with Custom Templates (TrueMultimer)
--description_file=<description.csv>: Path to the description file. Same as for 1.4. Run with Custom Templates (TrueMultimer)
--multimeric_mode=True

Output and the next step

The output and next step are the same as those for the 2.1. Basic Run.

2.6. Run with crosslinking-data (AlphaLink2)

As Stahl et al., 2023 showed, integrating cross-link data with AlphaFold could improve the modelling quality in some challenging cases. Thus, AlphaPulldown has integrated the AlphaLink2 pipeline, allowing users to combine cross-link data with AlphaFold Multimer inference without needing to calculate MSAs from scratch again.

Cite: If you use AlphaLink2, please remember to cite: Stahl, K., Demann, L., Bremenkamp, R., Warneke, R., Hormes, B., Stülke, J., Brock, O., Rappsilber, J., Der, S.-M. ", & Mensch, S. (2024). Modelling protein complexes with crosslinking mass spectrometry and deep learning. BioRxiv, 2023.06.07.544059. https://doi.org/10.1101/2023.06.07.544059

Before using, install AlphaLink2 as described here.

Input

Besides features (.pkl) files generated in the 1.1. Basic Run you need to prepare cross-link input data:

As instructed by AlphaLink2, information of cross-linked residues between 2 proteins, inter-protein crosslinks A->B 1,50 and 30,80 and an FDR=20%, should look like:

{'proteinA': {'proteinB': [(1, 50, 0.2), (30, 80, 0.2)]}}

and intra-protein crosslinks follow the same format:

{'proteinA': {'proteinA': [(5, 20, 0.2)]}}

The keys in these dictionaries should be the same as your pickle files created in the first step of AlphaPulldown. For example, you should have proteinA.pkl and proteinB.pkl already calculated.

Dictionaries like these should be stored in .pkl.gz files and provided to AlphaPulldown in the next step. You can use the script from AlphaLink2 to prepare these pickle files.

Warning! The dictionaries are 0-indexed, i.e., residues start from 0.

Run with AlphaLink2 prediction via AlphaPulldown

Within the same conda environment, run in e.g. custom mode:

run_multimer_jobs.py --mode=custom \
--num_predictions_per_model=1 \
--output_path=/scratch/scratch/user/output/models \
--data_dir=/g/alphafold/AlphaFold_DBs/2.3.0/ \
--protein_lists=custom.txt \
--monomer_objects_dir=/scratch/user/output/features \
--job_index=$SLURM_ARRAY_TASK_ID --alphalink_weight=/scratch/user/alphalink_weights/AlphaLink-Multimer_SDA_v3.pt \
--use_alphalink=True --crosslinks=/path/to/crosslinks.pkl.gz

The other modes provided by AlphaPulldown also work in the same way.

Output and the next step

The output and next step are the same as those for the 2.1. Basic Run.

3. Analysis and Visualization

The resulting predictions from the step 2 can be used directly as they are. However, for evaluation systematization and ranking of the prediction, you can use an interactive Jupyter Notebook and/or table with models scores.

Create Jupyter Notebook

Go to the model's output directory from the step 2.

cd <models_output_dir>

And run the script in the activated conda environment:

source activate AlphaPulldown
create_notebook.py --cutoff=5.0 --output_dir=<models_output_dir>

--cutoff: check the value of PAE between chains. In the case of multimers, the analysis program will create the notebook only from models with inter-chain PAE values smaller than the cutoff. Increase this parameter if you miss predictions in your notebook (e.g., 50).
--output_dir: Directory where predicted models are stored in this, . in this example.
--pae_figsize: Figsize of pae_plot, default is 50.

This command will generate an output.ipynb, which you can open using JupyterLab. JupyterLab is installed with AlphaPulldown. To view the notebook, launch it with:

jupyter-lab output.ipynb

Next step

For usage of the Jupyter Notebook, refer to the Downstream analysis section of this manual.

Create Results table

Making a CSV table with structural properties and scores requires the download of the singularity image fold_analysis.sif. Please refer to the installation instruction.

To execute the singularity image (i.e. the sif file) run:

singularity exec \
    --no-home \
    --bind </path/to/output/dir/with/predictions>:/mnt \
    <path to your downloaded image>/fold_analysis.sif \
    run_get_good_pae.sh \
    --output_dir=/mnt \
    --cutoff=10

cutoff is to check the value of PAE between chains. In the case of multimers, the analysis program will create the notebook only from models with inter-chain PAE values smaller than the cutoff. If you do not want to filter out the models use very high cutoff e.g. --cutoff=100.

Next step

For usage of the Results table, refer to the Downstream analysis section of this manual.

Downstream analysis

Jupyter notebook

Jupyter notebook output.ipynb is

Generated during the Create Jupyter Notebook for Scripts-Based Alphapulldown.
Stored in the output/reports directory for Snakemake AlphaPulldown. Snakemake AlphaPulldown also generates a notebook with all cells executed in the report.html file, which can be copied locally and opened in a browser without JupyterLab.

You can output.ipynb open using JupyterLab. JupyterLab is installed with AlphaPulldown. To view the notebook, launch it with:

jupyter-lab output.ipynb

[!Note] If you run AlphaPulldown on a remote computer cluster, you will need a graphical connection, network mount of the remote directory, or a copy of the entire <models_output_dir> to open the notebook in a browser.

Jupyter Notebook remote access

To connect remotely, first launch Jupyter Notebook on the cluster. You can choose a different port number if the selected one is already in use:

jupyter-lab --no-browser --port=8895 output.ipynb

The output of this command will provide you with a link and a token for connection. The token is a unique string that is required for authentication when you first access the Jupyter Notebook interface. Here is an example of what the output might look like:

http://localhost:8895/?token=abc123def456

Next, establish an SSH tunnel using your local machine's command line. The port numbers should match those used in the previous command. Replace with your EMBL login, or if you are using a different cluster, provide the address of that cluster and your login in the format <login>@<address>:

ssh -N -f -L localhost:8895:localhost:8895 <login>@login01.cluster.embl.de

After establishing the SSH tunnel, you can access the Jupyter Notebook in your web browser. Open your browser and navigate to the following URL:

http://localhost:8895

You will be prompted to enter the token provided earlier when you launched Jupyter Lab on the cluster. Copy and paste the token from the command output into the browser prompt to gain access.

In the JupyterLab window, choose output.ipynb if it does not open automatically. Then, go to Run > Run All Cells. After all cells have been executed for every protein complex, you will see PAE plots, interactive structures colored by pLDDT, and interactive structures colored by a chain.

To zoom in on PAE plots, double-click on them. To increase the number of displayed interactive models, add the argument models to the parse_results() or parse_results_colour_chains() functions.

parse_results('./ProteinA_and_ProteinB', models=10)

[!WARNING] If the Jupyter Notebook contains too many proteins, some interactive structures may disappear due to memory limitations. To restore the output of the cell, simply rerun it by selecting the cell and going to Run > Run Selected Cell or pressing Shift + Enter.

Results table

Results table:

predictions_with_good_interpae.csv is generated during the Create Results table for Scripts-Based Alphapulldown.
analysis.csv generated in the output/reports for Snakemake AlphaPulldown

$\text{\color{red}Change description, add explain scores}$

By default, you will have a CSV file named predictions_with_good_interpae.txt created in the directory /path/to/your/output/dir as you have given in the command above. predictions_with_good_interpae.txt reports: 1. iptm, iptm+ptm scores provided by AlphaFold 2. mpDockQ score developed by Bryant et al., 2022 3. PI_score developed by Malhotra et al., 2021. The detailed explanations of these scores can be found in our paper and an example screenshot of the table is below. example

Results management scripts

AlphaPulldown provides scripts to help optimize data storage and prepare structures for deposition.

Decrease the size of AlphaPulldown output

The most space-consuming part of the structure prediction results are pickle files result_model_{1,2,3,4,5}_*.pkl files. Please refer to the AlphaFold manual for more details on output files. Some information in these files is needed only for very special tasks. The truncate_pickles.py script copies the output of AlphaPulldown to a new directory and deletes the specified information from the pickle files. It may decrease the size of the output up to 100 times.

source activate AlphaPulldown
truncate_pickles.py \
  --src_dir=</path/to/source> \
  --dst_dir=</path/to/destination> \
  --keys_to_exclude=aligned_confidence_probs,distogram,masked_msa \
  --number_of_threads=4

--src_dir=</path/to/source>: Replace </path/to/source> with the path to the structures output directory. This should be the same as the --output_path for the run_multimer_jobs.py script from the Predict Structures step.
--dst_dir=</path/to/destination>: Replace </path/to/destination> with the path of the directory to copy the truncated results to.
--keys_to_exclude=aligned_confidence_probs,distogram,masked_msa: A comma-separated list of keys that should be excluded from the copied pickle files. The default keys are "aligned_confidence_probs,distogram,masked_msa".
--number_of_threads=4: Number of threads to run in parallel.

Convert Models from PDB Format to ModelCIF Format

With PDB files now being marked

as a legacy format, here is a way to convert PDB files produced by the AlphaPulldown pipeline into mmCIF files, including the ModelCIF extension.

In addition to the general mmCIF tables, ModelCIF adds information relevant for a modeling experiment. This includes target-sequence annotation and a modeling protocol, describing the process by which a model was created, including software used with its parameters. To help users assess the reliability of a model, various quality metrics can be stored directly in a ModelCIF file or in associated files registered in the main file. ModelCIF is also the preferred format for ModelArchive.

As AlphaPulldown relies on AlphaFold to produce model coordinates, multiple models may be predicted in a single experiment. To accommodate different needs, convert_to_modelcif.py offers three major modes:

Convert all models into ModelCIF in separate files.
Only convert a specific single model.
Convert a specific model to ModelCIF but keep additional models in a Zip archive associated with the representative ModelCIF formatted model.

1. Convert all models to separate ModelCIF files

The most general call of the conversion script, without any non-mandatory arguments, will create a ModelCIF file and an associated Zip archive for each model of each complex found in the --ap_output directory:

source activate AlphaPulldown
convert_to_modelcif.py \
  --ap_output <output path of run_multimer_jobs.py>

--ap_output: Path to the structures directory. This should be the same as the --output_path for the run_multimer_jobs.py script from the Predict Structures step.

The output is stored in the path that --ap_output points to. After running convert_to_modelcif.py, you should find a ModelCIF file and a Zip archive for each model PDB file in the AlphaPulldown output directory:

Output

ap_output
    protein1_and_protein2
        |-ranked_0.cif
        |-ranked_0.pdb
        |-ranked_0.zip
        |-ranked_1.cif
        |-ranked_1.pdb
        |-ranked_1.zip
        |-ranked_2.cif
        |-ranked_2.pdb
        |-ranked_2.zip
        |-ranked_3.cif
        |-ranked_3.pdb
        |-ranked_3.zip
        |-ranked_4.cif
        |-ranked_4.pdb
        |-ranked_4.zip
        ...
    ...

2. Only convert a specific single model for each complex

If only a single model should be translated to ModelCIF, use the --model_selected option. Provide the ranking of the model as the value. For example, to convert the model ranked 0:

source activate AlphaPulldown
convert_to_modelcif.py \
  --ap_output <output path of run_multimer_jobs.py> \
  --model_selected 0

This will create only one ModelCIF file and Zip archive in the path pointed at by --ap_output:

Output

ap_output
    protein1_and_protein2
        |-ranked_0.cif
        |-ranked_0.pdb
        |-ranked_0.zip
        |-ranked_1.pdb
        |-ranked_2.pdb
        |-ranked_3.pdb
        |-ranked_4.pdb
        ...
    ...

Besides --model_selected, the arguments are the same as for scenario 1.

3. Have a representative model and keep associated models

Sometimes you want to focus on a certain model from the AlphaPulldown pipeline but don't want to completely discard the other models generated. For this, convert_to_modelcif.py can translate all models to ModelCIF but store the excess in the Zip archive of the selected model. This is achieved by adding the option --add_associated together with --model_selected.

source activate AlphaPulldown
convert_to_modelcif.py \
  --ap_output <output path of run_multimer_jobs.py> \
  --model_selected 0 \
  --add-associated

Arguments are the same as in scenarios 1 and 2 but include --add_associated.

The output directory looks similar to when only converting a single model:

Output

ap_output
    protein1_and_protein2
        |-ranked_0.cif
        |-ranked_0.pdb
        |-ranked_0.zip
        |-ranked_1.pdb
        |-ranked_2.pdb
        |-ranked_3.pdb
        |-ranked_4.pdb
        ...
    ...

But a peek into ranked_0.zip shows that it stored ModelCIF files and Zip archives for all remaining models of this modeling experiment:

Output

ranked_0.zip
    |-ranked_0_local_pairwise_qa.cif
    |-ranked_1.cif
    |-ranked_1.zip
    |-ranked_2.cif
    |-ranked_2.zip
    |-ranked_3.cif
    |-ranked_3.zip
    |-ranked_4.cif
    |-ranked_4.zip

Associated Zip Archives

convert_to_modelcif.py produces two kinds of output: ModelCIF files and Zip archives for each model. The latter are called "associated files/archives" in ModelCIF terminology. Associated files are registered in their corresponding ModelCIF file by categories ma_entry_associated_files and ma_associated_archive_file_details. Historically, this scheme was created to offload AlphaFold's pairwise alignment error lists, which drastically increase file size. Nowadays, the Zip archives are used for all kinds of supplementary information on models, not handled by ModelCIF.

Miscellaneous Options

At this time, there is only one option left unexplained: --compress. It tells the script to compress ModelCIF files using Gzip. In the case of --add_associated, the ModelCIF files in the associated Zip archive are also compressed.

Features Database

Instead of generating feature files locally, you can download them from the AlphaPulldown Features Database, which contains precomputed protein features for major model organisms.

Installation

[!NOTE] For EMBL cluster users: You can access the directory with generated features files at /g/alphafold/input_features/

To access the Features Database, you need to install the MinIO Client (mc).

Steps:

Download the mc binary.
Make the binary executable.
Move it to your PATH for system-wide access.

Example for AMD64 architecture:

curl -O https://dl.min.io/client/mc/release/linux-amd64/mc
chmod +x mc
sudo mv mc /usr/local/bin/

Verify installation:

To ensure mc is correctly installed, you can run:

mc --help

Configuration

Set up an alias for easy access to the AlphaPulldown Features Database hosted at EMBL:

mc alias set embl https://s3.embl.de "" "" --api S3v4

This alias allows you to interact with the Features Database as if it were a local directory.

Downloading Features

Once mc is installed and configured, you can start accessing the Features Database. The mc commands mimic standard bash commands.

List available organisms:

To view the list of available organisms with precomputed feature files, run:

mc ls embl/alphapulldown/input_features

Each organism directory contains compressed .pkl.xz feature files, named according to their UniProt ID.

Download specific protein features:

For example, to download the feature file for the protein with UniProt ID Q6BF25 from Escherichia coli, use:

mc cp embl/alphapulldown/input_features/Escherichia_coli/Q6BF25.pkl.xz Q6BF25.pkl.xz

Download all features for an organism:

To download all feature files for proteins from a specific organism, such as E. coli, copy the entire directory:

mc cp --recursive embl/alphapulldown/input_features/Escherichia_coli/ ./Escherichia_coli/

Alternatively, you can mirror the contents of the organism’s directory, ensuring all files are synced between the source and your local directory:

mc mirror embl/alphapulldown/input_features/Escherichia_coli/ Escherichia_coli/

This command mirrors the remote directory to your local system, keeping both locations in sync.

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alphapulldown 2.0.1

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AlphaPulldown: Version 2.0.0

Table of Contents

About AlphaPulldown

Overview

Alphafold databases

Snakemake AlphaPulldown

1. Installation

2. Configuration

3. Execution

Run AlphaPulldown Python Command Line Interface

0. Installation

0.1. Create Anaconda environment

0.2. Installation using pip

0.3. Installation for the Downstream analysis tools

0.4. Installation for cross-link input data by AlphaLink2 (optional!)

0.5. Installation for developers

1. Compute multiple sequence alignment (MSA) and template features (CPU stage)

1.1. Basic run

Input

Script Execution

Output

Next step

1.2. Example bash scripts for SLURM (EMBL cluster)

Input

Script Execution

Next step

1.3. Run using MMseqs2 and ColabFold Databases (Faster)

Run MMseqs2 Remotely

Output

Run MMseqs2 Locally

Next step

1.4. Run with custom templates (TrueMultimer)

Input

Script Execution

Output

Next step

2. Predict structures (GPU stage)

2.1. Basic run

Input

Script Execution: Structure Prediction

Output

Next step

2.2. Example run with SLURM (EMBL cluster)

Input

Script Execution

Output and the next step

2.3. Pulldown mode

Multiple inputs "pulldown" mode

2.4. All versus All mode

Output and the next step

2.5. Run with Custom Templates (TrueMultimer)

Input

Script Execution for TrueMultimer Structure Prediction

Output and the next step

2.6. Run with crosslinking-data (AlphaLink2)

Input

Run with AlphaLink2 prediction via AlphaPulldown

Output and the next step

3. Analysis and Visualization

Create Jupyter Notebook

Next step

Create Results table

Next step

Downstream analysis

Jupyter notebook

Results table

Results management scripts

Decrease the size of AlphaPulldown output

Convert Models from PDB Format to ModelCIF Format

1. Convert all models to separate ModelCIF files

2. Only convert a specific single model for each complex