AlphaLink2: Modelling protein complexes with crosslinking mass spectrometry and deep learning

Code for the paper "Modelling protein complexes with crosslinking mass spectrometry and deep learning". We extend AlphaLink to protein complexes. AlphaLink2 is based on Uni-Fold and integrates crosslinking MS data directly into Uni-Fold. The current networks were trained with simulated SDA data (25 Å Cα-Cα).

case

<center> <small> Figure 1. Prediction of RpoA-RpoC with real DSSO crosslinking MS data. Satisfied crosslinks are shown in blue (< 30 Å Cα-Cα) and violated crosslinks in red (> 30 Å Cα-Cα). </small> </center>

Running AlphaLink in ColabFold

The AlphaLink2 ColabFold can be found here.

Crosslink input format

AlphaLink takes as input a CSV (.csv or .txt) with a list of crosslinked residue pairs with a false-discovery rate (FDR). That is, for inter-protein crosslinks A->B 1,50 and A->A 5,5 and an FDR=20% and 10% resp., the input would look as follows:

The crosslinks CSV has the following format (residues are 1-indexed).

residueFrom chain1 residueTo chain2 FDR

Example:

1 A 50 B 0.2
5 A 5 A 0.1

The chain IDs A..Z+ designate all chains in the FASTA file, enumerated by order of appearance. That is, the first chain gets the identifier A, the second chain the identifier B and so on. After feature generation, the chain assignment can be found in the output folder in the file "chain_id_map.json" and the final composition in the file "chains.txt". Changing "chains.txt" is an easy way to test different compositions and doesn't require regenerating the features.

Parse mzIdentML files

After installing this Python package: https://github.com/Rappsilber-Laboratory/xi-mzidentml-converter you can parse mzIdentML files with:

python parse_mzidentml.py --mzidentml Dataset3_2pcntPPI-FDR.mzid

This will generate two outputs: Dataset3_2pcntPPI-FDR.fasta and Dataset3_2pcntPPI-FDR.txt which contain the respective FASTA sequences and the associated crosslinks. Disclaimer: Since the legacy PDB format only supports single character chain IDs we limit the parsing to first 62 sequences.

Installation and preparations

Installing AlphaLink from scratch with conda/ pip

In part based on: https://github.com/kalininalab/alphafold_non_docker

Installation will take around 1-2 hours. Tested on Linux (CentOS 7/8).

Create new conda environment

conda create --name alphalink -c conda-forge python=3.10
conda activate alphalink

Install Uni-Core

For Linux:

pip install nvidia-pyindex
pip install https://github.com/dptech-corp/Uni-Core/releases/download/0.0.3/unicore-0.0.1+cu118torch2.0.0-cp310-cp310-linux_x86_64.whl

For other systems, build Uni-Core from scratch.

Install utilities

conda install -y -c conda-forge openmm==7.7.0 pdbfixer biopython==1.81
conda install -y -c bioconda hmmer hhsuite==3.3.0 kalign2

Install AlphaFold - necessary for relax

pip install tensorflow-cpu==2.19.0
git clone https://github.com/deepmind/alphafold.git

cd alphafold
python setup.py install

# download folding resources
wget --no-check-certificate https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt

# copy stereo_chemical_props.txt to the alphafold conda folder
cp stereo_chemical_props.txt $CONDA_PREFIX/lib/python3.10/site-packages/alphafold/common/

cd ..

Databases

If you are missing the databases for MSA generation, you can download them with the following script:

bash scripts/download/download_all_data.sh /path/to/database/directory full_dbs

or for the smaller databases:

bash scripts/download/download_all_data.sh /path/to/database/directory reduced_dbs

They require up to 3TB of storage.

Install AlphaLink2

git clone https://github.com/Rappsilber-Laboratory/AlphaLink2.git
cd AlphaLink2
python setup.py install

Model weights

The model weights are deposited here:

Running AlphaLink

After set up, AlphaLink can be run as follows:

    bash run_alphalink.sh \
    /path/to/the/input.fasta \        # target fasta file
    /path/to/crosslinks.pkl.gz \      # pickled and gzipped dictionary with crosslinks
    /path/to/the/output/directory/ \  # output directory
    /path/to/model_parameters.pt \    # model parameters
    /path/to/database/directory/ \    # directory of databases
    2020-05-01                        # use templates before this date

Output folder will contain the relaxed and unrelaxed PDBs and a pickle file with the PAE map.

We expose also 4 optional parameters to set the number of recycling iterations, number of samples, Neff for subsampling MSAs, and the possibility to remove MSA information for crosslinked residues.

    bash run_alphalink.sh \
    /path/to/the/input.fasta \        # target fasta file
    /path/to/crosslinks.pkl.gz \      # pickled and gzipped dictionary with crosslinks
    /path/to/the/output/directory/ \  # output directory
    /path/to/model_parameters.pt \    # model parameters
    /path/to/database/directory/ \    # directory of databases
    2020-05-01 \                      # use templates before this date
    20 \                              # use 20 recycling iterations (default: 20)
    25 \                              # generate 25 sample (default: 25)
    30 \                              # downsample MSAs to Neff 30 (default: -1, use full MSA, expects integer >= 1)
    1                                 # integer > 0 activates this option. Remove MSA information for crosslinked residues (default: -1, use full MSA)

AlphaLink IHM model deposition

Models generated with AlphaLink using experimental restraints can be published as integrative/hybrid models in PDB-Dev PDB-Dev using the make_ihm.py script which requires python-ihm.

The script takes the chain_id_map.json file, the crosslink pickle, a mmcif file generated from the .pdb output of AlphaLink2 and the accession code for the deposited data (e.g., PRIDE) as input.

To generate a mmcif file from the .pdb output of AlphaLink2 you can use Maxit.

Finally update the authors in the make_ihm.py script and if applicable add your publication as a citation before running the script.

Hardware requirements

GPU, ideally NVIDIA V100 and upwards. A100+ can make use of bfloat16 to predict larger targets.

Citing this work

If you use the code, the model parameters, or the released data of AlphaLink2, please cite

@article{stahl2024modelling,
  title={Modelling protein complexes with crosslinking mass spectrometry and deep learning},
  author={Stahl, Kolja and Warneke, Robert and Demann, Lorenz and Bremenkamp, Rica and Hormes, Bj{\"o}rn and Brock, Oliver and St{\"u}lke, J{\"o}rg and Rappsilber, Juri},
  journal={Nature communications},
  volume={15},
  number={1},
  pages={7866},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

Any work that cites AlphaLink2 should also cite AlphaFold and Uni-Fold.

Acknowledgements

AlphaLink2 is based on Uni-Fold and fine-tunes the network weights of AlphaFold.

Code License

While AlphaFold's and, by extension, Uni-Fold's source code is licensed under the permissive Apache License, Version 2.0, DeepMind's pre-trained parameters fall under the CC BY 4.0 license. Note that the latter replaces the original, more restrictive CC BY-NC 4.0 license as of January 2022

Model Parameters License

The AlphaLink parameters are made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can find details at: https://creativecommons.org/licenses/by/4.0/legalcode

Third-party software

Use of the third-party software, libraries or code referred to in the Acknowledgements section above may be governed by separate terms and conditions or license provisions. Your use of the third-party software, libraries or code is subject to any such terms and you should check that you can comply with any applicable restrictions or terms and conditions before use.

AlphaLink2

Install / Use

README