MULTICOM4 is an advanced protein structure prediction system built on AlphaFold2 and 3. It achieved remarkable success in the 16th world-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP16) concluded in December 2024, ranking 1st in protein complex structure prediction without stoichiometry information (Phase 0), 2nd in protein tertiary structure prediction, 2nd in estimating the global fold accuracy of protein complex structures, 3rd in protein complex structure prediction with stoichiometry information (Phase 1), and 5th in protein-ligand structure and binding affinity prediction.
Colored Chains: Model predicted by MULTICOM4
Brown: True structure
| Target ID | Description | Visualization |
|---|---|---|
| H0215 | A1B1, mNeonGreen with Bound Nanobody |  |
| H0233 | A2B2C2, Antibody Fab 3H4 complex, virus capsid |  |
| H0245 | A1B1, FUNComplex, Shallow MSA |  |
| T0234o | A3, Better Stoichiometry Prediction |  |
git clone --recursive https://github.com/BioinfoMachineLearning/MULTICOM4
Install AlphaFold/AlphaFold-Multimer and other required third-party packages (modified from alphafold_non_docker)
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash Miniconda3-latest-Linux-x86_64.shconda create --name multicom4 python==3.8
conda update -n base condaconda activate multicom4- Change
cudatoolkit==11.2.2version if it is not supported in your system
conda install -y -c conda-forge openmm==7.5.1 cudatoolkit==11.2.2 pdbfixer
conda install -y -c bioconda hmmer hhsuite==3.3.0 kalign2- Change
jaxlib==0.3.25+cuda11.cudnn805version if this is not supported in your system
pip install absl-py==1.0.0 biopython==1.79 chex==0.0.7 dm-haiku==0.0.9 dm-tree==0.1.6 immutabledict==2.0.0 jax==0.3.25 ml-collections==0.1.0 numpy==1.21.6 pandas==1.3.4 protobuf==3.20.1 scipy==1.7.0 tensorflow-cpu==2.9.0
pip install --upgrade --no-cache-dir jax==0.3.25 jaxlib==0.3.25+cuda11.cudnn805 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html# Replace $MULTICOM4_INSTALL_DIR with your MULTICOM4 installation directory
wget -q -P $MULTICOM4_INSTALL_DIR/tools/alphafold-v2.3.2/alphafold/common/ https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt# Replace $MULTICOM4_INSTALL_DIR with your MULTICOM4 installation directory
cd ~/anaconda3/envs/multicom4/lib/python3.8/site-packages/ && patch -p0 < $MULTICOM4_INSTALL_DIR/tools/alphafold-v2.3.2/docker/openmm.patch
# or
cd ~/miniconda3/envs/multicom4/lib/python3.8/site-packages/ && patch -p0 < $MULTICOM4_INSTALL_DIR/tools/alphafold-v2.3.2/docker/openmm.patchconda install tqdm
conda install -c conda-forge -c bioconda foldseek
conda install scikit-learn
# DHR
conda create --name fastMSA --file tools/Dense-Homolog-Retrieval/requirements.txt -c pytorch -c conda-forge -c bioconda
# ESMFold
conda env create -f esm.yml
conda activate esmfold
pip install "fair-esm[esmfold]"
pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307'# Replace $MULTICOM4_INSTALL_DIR with your MULTICOM4 installation directory
bash $MULTICOM4_INSTALL_DIR/tools/alphafold-v2.3.2/scripts/download_all_data.sh <YOUR_ALPHAFOLD_DB_DIR>
# Note: here the parameters should be the absolute paths
python download_database_and_tools.py --multicom4db_dir <YOUR_MULTICOM4_DB_DIR>
# Configure the MULTICOM4 system
# Replace $MULTICOM4_INSTALL_DIR with your MULTICOM4 installation directory
# Replace $YOUR_ALPHAFOLD_DB_DIR with your downloaded AlphaFold databases directory
python configure.py --envdir ~/miniconda3/envs/multicom4 --multicom4db_dir <YOUR_MULTICOM4_DB_DIR> --afdb_dir <YOUR_ALPHAFOLD_DB_DIR>
# e.g,
# python download_database_and_tools.py \
# --multicom4db_dir /home/multicom4/tools/multicom4_db
# python configure.py \
# --multicom4db_dir /home/multicom4/tools/multicom4_db \
# --afdb_dir /home/multicom4/tools/alphafold_databases/The configure.py python script will
- Copy the alphafold_addon scripts
- Create the configuration file (bin/db_option) for running the system
Assume the following databases have been installed as a part of the AlphaFold2/AlphaFold-Multimer installation
Additional databases will be installed for the MULTICOM system by setup.py:
- AlphaFoldDB: ~53G
- ESM Atlas: ~99G
- Metaclust: ~114G
- STRING: ~129G
- pdb_complex v2024: ~38G
- pdb_sort90 v2024: ~48G
- Uniclust30: ~87G
- DHR_DATABASE: 1.5T
- JGIclust: ~1.1T
All parameters for running AlphaFold2/AlphaFold-Multimer are defined in multicom4/common/config.py. These configurations were used for large-scale sampling during the CASP16 competition, with the exception of the number of models, which should be adjusted according to the size of the target protein. Users may modify the following parameters as needed:
# Number of models generated per checkpoint for monomer predictions using AlphaFold2. Default: 100.
# Note: Each AlphaFold2 predictor will generate 100 * 5 models.
MONOMER_PREDICTIONS_PER_MODEL = 100
# Configuration for hetero-multimer predictions using AlphaFold-Multimer. Default: 100 models.
# Note: Each AlphaFold-Multimer predictor will generate 100 * 5 models.
HETERO_MULTIMER_PREDICTIONS_PER_MODEL = 100
# Configuration for homo-multimer predictions using AlphaFold-Multimer. Default: 100 models.
# Note: Each AlphaFold-Multimer predictor will generate 100 * 5 models.
HOMO_MULTIMER_PREDICTIONS_PER_MODEL = 100conda activate multicom4
# Replace $MULTICOM4_INSTALL_DIR with your MULTICOM4 installation directory (absolute path)
export PYTHONPATH=$MULTICOM4_INSTALL_DIR
# e.g,
# conda activate MULTICOM4
# export PYTHONPATH=/home/multicom4/MULTICOM4
Now MULTICOM4 is ready for you to make predictions.
Say we have a monomer with the sequence <SEQUENCE>. The input sequence file should be in the FASTA format as follows:
>sequence_name
<SEQUENCE>
Note: It is recommended that the name of the sequence file in FASTA format should be the same as the sequence name.
Then run the following command:
# Please provide absolute path for the input parameters
sh bin/monomer/run_monomer.sh \
--option_file=bin/db_option \
--fasta_path=$YOUR_FASTA \
--output_dir=$OUTDIRoption_file is a file in the MULTICOM4 package to store the path of the databases/tools. fasta_path is the full path of the file storing the input protein sequence(s) in the FASTA format. output_dir specifies where the prediction results are stored.
$OUTPUT_DIR/ # Your output directory
N1_monomer_alignments_generation/ # Working directory for generating monomer MSAs
N2_monomer_template_search/ # Working directory for searching monomer templates
N3_monomer_structure_generation/ # Working directory for generating monomer structural predictions
N4_monomer_structure_evaluation/ # Working directory for evaluating the monomer structural predictions
- alphafold_ranking.csv # AlphaFold2 pLDDT ranking
- The predictions and ranking files are saved in the N4_monomer_structure_evaluation folder. You can check the AlphaFold2 pLDDT score ranking file (alphafold_ranking.csv) to look for the structure with the highest pLDDT score.
Say we have a homomer with 4 copies of the same sequence
<SEQUENCE>. The input file should be in the format as follows:
>sequence_1
<SEQUENCE>
>sequence_2
<SEQUENCE>
>sequence_3
<SEQUENCE>
>sequence_4
<SEQUENCE>
Then run the following command:
# Please provide absolute path for the input parameters
sh bin/multimer/run_multimer.sh \
--option_file=bin/db_option \
--fasta_path=$YOUR_FASTA \
--output_dir=$OUTDIR$OUTPUT_DIR/ # Your output directory
N1_monomer_alignments_generation/ # Working directory for generating monomer MSAs
- Subunit A
- Subunit B
- ...
N1_monomer_alignments_generation_img/ # Working directory for generating IMG MSA
- Subunit A
- Subunit B
- ...
N2_monomer_template_search/ # Working directory for searching monomer templates
- Subunit A
- Subunit B
- ...
N3_monomer_structure_generation/ # Working directory for generating monomer structural predictions
- Subunit A
- Subunit B
- ...
N4_monomer_alignments_concatenation/ # Working directory for concatenating the monomer MSAs
N5_monomer_templates_search/ # Working directory for concatenating the monomer templates
N6_multimer_structure_generation/ # Working directory for generating multimer structural predictions
N7_monomer_only_structure_evaluation # Working directory for evaluating monomer structural predictions
- Subunit A
# Rankings for all the predictions
- alphafold_ranking.csv # AlphaFold2 pLDDT ranking
- pairwise_ranking.tm # Pairwise (APOLLO) ranking
- pairwise_af_avg.ranking # Average ranking of the two
# Rankings for the predictions generated by monomer structure prediction
- alphafold_ranking_monomer.csv # AlphaFold2 pLDDT ranking
- pairwise_af_avg_monomer.ranking # Average ranking
# Rankings for the predictions extracted from multimer predictions
- alphafold_ranking_multimer.csv # AlphaFold2 pLDDT ranking
- pairwise_af_avg_multimer.ranking # Average ranking
- Subunit B
- ...
N7_multimer_structure_evaluation # Working directory for evaluating multimer structural predictions
- alphafold_ranking.csv # AlphaFold2 pLDDT ranking
- multieva.csv # Pairwise ranking using MMalign
- pairwise_af_avg.ranking # Average ranking of the two
-
The predictions and ranking files are saved in N7_multimer_structure_evaluation, similarly, you can check the AlphaFold-Multimer confidence score ranking file (alphafold_ranking.csv) to look for the structure with the highest predicted confidence score generated by AlphaFold-Multimer. The multieva.csv and pairwise_af_avg.ranking are the other two ranking files.
-
The monomer structures and ranking files are saved in N7_monomer_only_structure_evaluation if you want to check the predictions and rankings for the monomer structures.
Our CASP16 talk for protein complex structure prediction:
Our CASP16 talk for protein model quality assessment:
https://predictioncenter.org/casp16/doc/presentations/Day-2/Day2-15-Neupane-CASP16_MULTICOM_EMA.pptx
Our CASP16 talk for protein-ligand binding affinity prediction:
https://predictioncenter.org/casp16/doc/presentations/Day-3/Day3-14-Morehead-MULTICOM_ligand.pptx
@article{liu2025improving,
title={Improving AlphaFold2-and AlphaFold3-Based Protein Complex Structure Prediction With MULTICOM4 in CASP16},
author={Liu, Jian and Neupane, Pawan and Cheng, Jianlin},
journal={Proteins: Structure, Function, and Bioinformatics},
year={2025},
publisher={Wiley Online Library}
}
@article{liu2025boosting,
title={Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16},
author={Liu, Jian and Neupane, Pawan and Cheng, Jianlin},
journal={bioRxiv},
pages={2025--06},
year={2025},
publisher={Cold Spring Harbor Laboratory}
}