CombFold / README.md

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---
frameworks:
- JAX
language:
- en
license: apache-2.0
tags:
- OneScience
- bioscience
- protein-complex-structure-prediction
- CombFold
- AlphaFold-Multimer
- ColabFold
tasks: []
datasets: []
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">CombFold</span>
  </strong>
</p>
# Model Introduction

CombFold is an open-source pipeline developed by dina-lab3D for predicting the structures of large protein complexes. Starting from the amino acid sequences of the individual chains in a complex, CombFold uses AlphaFold-Multimer to predict multiple candidate subcomplexes and then applies combinatorial assembly algorithms to construct the complete protein complex.

The original publication reports support for complexes containing at least 18,000 amino acids and up to 32 subunits.

Paper: [Assembly of protein complexes by combining AlphaFold and combinatorial optimization](https://www.nature.com/articles/s41592-024-02174-0)

# Model Description

CombFold consists of four main stages:

1. Define subunits according to protein domains and chain composition and generate `subunits.json`;
2. Generate FASTA files for all subunit pairs and predict pairwise subcomplexes using AlphaFold-Multimer;
3. Optionally predict candidate subcomplexes containing more than two subunits;
4. Extract relative transformations between subunits from the predicted PDB structures and use a C++ combinatorial optimization algorithm to assemble the complete complex.

The main components included in the Hugging Face model package are:

- `model/CombinatorialAssembler/`: C++17 combinatorial assembler and the `AF2trans` structural transformation tool;
- `scripts/prepare_fastas.py`: generates FASTA files for pairwise or larger subunit combinations;
- `scripts/inference.py`: unified assembly entry point for the Hugging Face model package;
- `scripts/run_on_pdbs.py`: original upstream entry point for assembly from predicted PDB structures;
- `weight/`: official pretrained AlphaFold-Multimer parameters used for offline ColabFold inference;
- `requirements.txt`: additional dependencies required on top of the OneScience base environment.

CombFold itself is not a trainable neural network. Neural-network inference is performed by pretrained AlphaFold-Multimer models, while CombFold is responsible for extracting structural transformations and performing combinatorial assembly.

# Use Cases

| Use Case | Description |
| :---: | :--- |
| Large protein complex prediction | Combine multiple AlphaFold-Multimer subcomplex predictions into a complete complex structure. |
| Homomer prediction | Assemble complexes containing multiple copies of the same unique subunit according to the specified stoichiometry. |
| Heteromer prediction | Integrate predictions from different subunit pairs or groups to construct the complete structure. |
| Assembly from existing AFM results | Directly use existing AlphaFold-Multimer PDB predictions without rerunning AlphaFold-Multimer. |
| Crosslink-guided assembly | Optionally incorporate crosslinking restraints to constrain candidate complex structures. |

# Usage

## 1. OneCode

You can use the OneCode online environment for an intelligent one-click AI4S programming experience:

[Try OneCode for AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Installation

**Hardware Requirements**

- The CombFold C++ combinatorial assembly stage requires only CPU resources;
- Local AlphaFold-Multimer subcomplex prediction typically requires an accelerator;
- Memory usage for long-sequence and multi-model inference increases with the total number of residues, MSA depth, number of models, and number of recycles;
- PyTorch is not a direct runtime dependency of CombFold or the current ColabFold inference pipeline.

### Download the Model Package

Install the Hugging Face command-line tool and download the model repository:

```bash
pip install -U huggingface_hub
hf download OneScience-Group/CombFold --local-dir ./CombFold
cd CombFold
```

### Install the Runtime Environment

**OneScience DCU Base Environment**

```bash
conda create -n onescience311 python=3.11 -y
conda activate onescience311
python -m pip install onescience[bio-dcu] \
  -i http://mirrors.onescience.ai:3141/pypi/simple/ \
  --trusted-host mirrors.onescience.ai
```

Install the additional dependencies:

```bash
python -m pip install --no-deps -r requirements.txt
```

### Compile the Combinatorial Assembler

The combinatorial assembly stage of CombFold is implemented in C++17 and requires the following system-level components. These components cannot be installed through `requirements.txt`.

| Component | Purpose | Description |
| --- | --- | --- |
| C++17 compiler | Compile the C++ source code | `g++` is commonly used on Linux |
| GNU Make | Execute the Makefile | GNU Make 4.2.1 has been validated |
| Boost headers | Compile-time headers | The directory must contain `boost/algorithm/string.hpp` |
| Boost program_options | Link-time runtime library | Usually provided as `libboost_program_options.so` on Linux |
Boost headers are generally portable across Linux distributions, but compiled Boost libraries depend on the operating system, CPU architecture, compiler, and `libstdc++` ABI. Therefore, compiled Boost libraries should not be copied directly between Linux, macOS, Windows, or different CPU architectures.
It is recommended to use Boost headers and runtime libraries from the same Boost version.
The upstream Makefile is configured primarily for macOS Homebrew. On Linux, the following command can be used directly only when Boost is already available in the compiler's default search paths:
```bash
cd model/CombinatorialAssembler
make
cd ../..
```
#### Specify a Custom Boost Path
If Boost is not installed in the default compiler search paths, define the following environment variables:
```bash
export COMBFOLD_BOOST_INCLUDE="<Boost source or include directory>"
export COMBFOLD_BOOST_LIB="<Boost library directory>"
```
`COMBFOLD_BOOST_INCLUDE` must point to a directory that directly contains the `boost/` subdirectory.
`COMBFOLD_BOOST_LIB` must point to a directory that directly contains the `libboost_program_options` library.
Before compilation, you can verify the paths using:
```bash
test -f "${COMBFOLD_BOOST_INCLUDE}/boost/algorithm/string.hpp" \
  && echo "Boost headers OK"
find "${COMBFOLD_BOOST_LIB}" -maxdepth 1 \
  -name 'libboost_program_options*' -print
```
Compile the assembler:
```bash
cd model/CombinatorialAssembler
make -j4 \
  BOOST_INCLUDE="${COMBFOLD_BOOST_INCLUDE}" \
  BOOST_LIB="${COMBFOLD_BOOST_LIB}"
cd ../..
```
### Weights and Data Preparation
The CombFold combinatorial assembler itself does not require model weights.
Local generation of AlphaFold-Multimer subcomplex predictions requires the five official AlphaFold-Multimer v3 parameter files:
| Asset | Location in the Model Package | Purpose |
| --- | --- | --- |
| `params_model_1_multimer_v3.npz` | `weight/alphafold/params/` | AFM v3 model 1 |
| `params_model_2_multimer_v3.npz` | `weight/alphafold/params/` | AFM v3 model 2 |
| `params_model_3_multimer_v3.npz` | `weight/alphafold/params/` | AFM v3 model 3 |
| `params_model_4_multimer_v3.npz` | `weight/alphafold/params/` | AFM v3 model 4 |
| `params_model_5_multimer_v3.npz` | `weight/alphafold/params/` | AFM v3 model 5 |
Use the following ColabFold data path:
```text
--data weight/alphafold
```
If you only use existing AlphaFold-Multimer PDB predictions for combinatorial assembly, AlphaFold-Multimer weights and a DCU device are not required.
### Define Subunits
The input `subunits.json` is a JSON dictionary keyed by unique subunit names.
Each subunit contains the following fields:
- `name`: unique subunit name;
- `sequence`: amino acid sequence;
- `chain_names`: chain names corresponding to this subunit in the complete complex. The number of entries also defines its stoichiometry;
- `start_res`: starting residue index of the sequence in the original chain.

Example:

```json
{
  "A0": {
    "name": "A0",
    "chain_names": ["A", "B"],
    "start_res": 1,
    "sequence": "MKDILEKLEERRAQARLGGGEKRLEAQHKRGKLTARERIELLLDHGSFEE"
  }
}
```

The Hugging Face model package provides a complete example:

```text
scripts/example/subunits.json
scripts/example/pdbs/
```

### Quick Inference: CPU Assembly from Existing PDB Files

This is the shortest CombFold inference path and does not run AlphaFold-Multimer:

```bash
python scripts/inference.py \
  --subunits scripts/example/subunits.json \
  --pdbs scripts/example/pdbs \
  --output output/example_assembly
```

The output directory must either not exist or be empty before execution.

After successful execution, the main results are located at:

```text
output/example_assembly/assembled_results/output_clustered_0.pdb
output/example_assembly/assembled_results/confidence.txt
```

You can also use the original upstream positional-argument entry point:

```bash
python scripts/run_on_pdbs.py \
  scripts/example/subunits.json \
  scripts/example/pdbs \
  output/example_assembly
```

### Generate Pairwise FASTA Files

Generate FASTA files for every pair of unique subunits defined in `subunits.json`:

```bash
python scripts/prepare_fastas.py \
  scripts/example/subunits.json \
  --stage pairs \
  --output-fasta-folder output/pair_fastas \
  --max-af-size 1800
```

The output directory must not already exist.

The official example generates files such as:

```text
A0_A0.fasta
A0_G0.fasta
G0_G0.fasta
```

### Minimal DCU Pairwise Inference

For an offline smoke test on a compute node, you can use `single_sequence`, one model, and one recycle:

```bash
colabfold_batch \
  output/pair_fastas \
  output/colabfold_pairs \
  --data weight/alphafold \
  --model-type alphafold2_multimer_v3 \
  --model-order 1 \
  --num-models 1 \
  --num-recycle 1 \
  --num-relax 0 \
  --msa-mode single_sequence \
  --disable-unified-memory
```

The configuration:

```text
single_sequence + 1 model + 1 recycle
```

is intended only to verify parameter loading, JAX/DCU forward execution, and PDB output generation.

It should not be used to evaluate formal prediction accuracy.

For production-quality predictions, prepare appropriate MSA features and increase the number of models and recycles according to available memory and runtime constraints.

### End-to-End DCU-to-CPU Inference

At least one predicted PDB structure must be selected for each pair.

To select the top-ranked ColabFold structure:

```bash
mkdir -p output/combfold_pdbs
find output/colabfold_pairs -maxdepth 1 \
  -type f -name '*rank_001*.pdb' \
  -exec cp {} output/combfold_pdbs/ \;
```

Then run the CombFold combinatorial assembly stage:

```bash
python scripts/inference.py \
  --subunits scripts/example/subunits.json \
  --pdbs output/combfold_pdbs \
  --output output/end2end_assembly
```

The unified inference entry point also outputs a machine-readable summary, for example:

```text
COMBFOLD_INFERENCE_RESULT={"assembled_structures": 5, "format": "pdb", "status": "PASS", ...}
```

### Use Crosslinking Restraints

Use the `--crosslinks` option to provide a crosslink restraint file:

```bash
python scripts/inference.py \
  --subunits scripts/example/example_xlinks/subunits.json \
  --pdbs scripts/example/example_xlinks/pdbs \
  --crosslinks scripts/example/example_xlinks/crosslinks.txt \
  --output output/crosslink_assembly
```

### Optional Prediction of Larger Subcomplexes

After completing pairwise prediction, larger subcomplex FASTA files can be generated based on the pairwise results:

```bash
python scripts/prepare_fastas.py \
  scripts/example/subunits.json \
  --stage groups \
  --output-fasta-folder output/group_fastas \
  --max-af-size 1800 \
  --input-pairs-results output/combfold_pdbs
```

### Training

CombFold is an inference algorithm that performs combinatorial assembly using predictions generated by pretrained AlphaFold-Multimer models.

CombFold itself does not contain a trainable neural network, training entry point, optimizer, or training-data pipeline. Therefore, this Hugging Face model package does not provide training commands.

Retraining AlphaFold-Multimer is a separate upstream large-scale model-training task and is not part of the CombFold combinatorial assembly workflow.

# OneScience Official Resources

| Platform | OneScience Main Repository | Skills Repository |
| --- | --- | --- |
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |

# Citation and License

- Paper: [Assembly of protein complexes by combining AlphaFold and combinatorial optimization](https://www.nature.com/articles/s41592-024-02174-0)
- Official implementation: https://github.com/dina-lab3D/CombFold
- The CombFold source code is provided under the Apache License 2.0 according to the upstream repository.
- AlphaFold, AlphaFold-Multimer, ColabFold, pretrained parameters, and other third-party components are subject to their respective original copyright notices, model terms, and licenses.

	---
	frameworks:
	- JAX
	language:
	- en
	license: apache-2.0
	tags:
	- OneScience
	- bioscience
	- protein-complex-structure-prediction
	- CombFold
	- AlphaFold-Multimer
	- ColabFold
	tasks: []
	datasets: []
	---

	<p align="center">
	<strong>
	<span style="font-size: 30px;">CombFold</span>
	</strong>
	</p>

	# Model Introduction

	CombFold is an open-source pipeline developed by dina-lab3D for predicting the structures of large protein complexes. Starting from the amino acid sequences of the individual chains in a complex, CombFold uses AlphaFold-Multimer to predict multiple candidate subcomplexes and then applies combinatorial assembly algorithms to construct the complete protein complex.

	The original publication reports support for complexes containing at least 18,000 amino acids and up to 32 subunits.

	Paper: [Assembly of protein complexes by combining AlphaFold and combinatorial optimization](https://www.nature.com/articles/s41592-024-02174-0)

	# Model Description

	CombFold consists of four main stages:

	1. Define subunits according to protein domains and chain composition and generate `subunits.json`;
	2. Generate FASTA files for all subunit pairs and predict pairwise subcomplexes using AlphaFold-Multimer;
	3. Optionally predict candidate subcomplexes containing more than two subunits;
	4. Extract relative transformations between subunits from the predicted PDB structures and use a C++ combinatorial optimization algorithm to assemble the complete complex.

	The main components included in the Hugging Face model package are:

	- `model/CombinatorialAssembler/`: C++17 combinatorial assembler and the `AF2trans` structural transformation tool;
	- `scripts/prepare_fastas.py`: generates FASTA files for pairwise or larger subunit combinations;
	- `scripts/inference.py`: unified assembly entry point for the Hugging Face model package;
	- `scripts/run_on_pdbs.py`: original upstream entry point for assembly from predicted PDB structures;
	- `weight/`: official pretrained AlphaFold-Multimer parameters used for offline ColabFold inference;
	- `requirements.txt`: additional dependencies required on top of the OneScience base environment.

	CombFold itself is not a trainable neural network. Neural-network inference is performed by pretrained AlphaFold-Multimer models, while CombFold is responsible for extracting structural transformations and performing combinatorial assembly.

	# Use Cases

	\| Use Case \| Description \|
	\| :---: \| :--- \|
	\| Large protein complex prediction \| Combine multiple AlphaFold-Multimer subcomplex predictions into a complete complex structure. \|
	\| Homomer prediction \| Assemble complexes containing multiple copies of the same unique subunit according to the specified stoichiometry. \|
	\| Heteromer prediction \| Integrate predictions from different subunit pairs or groups to construct the complete structure. \|
	\| Assembly from existing AFM results \| Directly use existing AlphaFold-Multimer PDB predictions without rerunning AlphaFold-Multimer. \|
	\| Crosslink-guided assembly \| Optionally incorporate crosslinking restraints to constrain candidate complex structures. \|

	# Usage

	## 1. OneCode

	You can use the OneCode online environment for an intelligent one-click AI4S programming experience:

	[Try OneCode for AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

	## 2. Manual Installation

	Hardware Requirements

	- The CombFold C++ combinatorial assembly stage requires only CPU resources;
	- Local AlphaFold-Multimer subcomplex prediction typically requires an accelerator;
	- Memory usage for long-sequence and multi-model inference increases with the total number of residues, MSA depth, number of models, and number of recycles;
	- PyTorch is not a direct runtime dependency of CombFold or the current ColabFold inference pipeline.

	### Download the Model Package

	Install the Hugging Face command-line tool and download the model repository:

	```bash
	pip install -U huggingface_hub

	hf download OneScience-Group/CombFold --local-dir ./CombFold
	cd CombFold
	```

	### Install the Runtime Environment

	OneScience DCU Base Environment

	```bash
	conda create -n onescience311 python=3.11 -y
	conda activate onescience311

	python -m pip install onescience[bio-dcu] \
	-i http://mirrors.onescience.ai:3141/pypi/simple/ \
	--trusted-host mirrors.onescience.ai
	```

	Install the additional dependencies:

	```bash
	python -m pip install --no-deps -r requirements.txt
	```

	### Compile the Combinatorial Assembler

	The combinatorial assembly stage of CombFold is implemented in C++17 and requires the following system-level components. These components cannot be installed through `requirements.txt`.

	\| Component \| Purpose \| Description \|
	\| --- \| --- \| --- \|
	\| C++17 compiler \| Compile the C++ source code \| `g++` is commonly used on Linux \|
	\| GNU Make \| Execute the Makefile \| GNU Make 4.2.1 has been validated \|
	\| Boost headers \| Compile-time headers \| The directory must contain `boost/algorithm/string.hpp` \|
	\| Boost program_options \| Link-time runtime library \| Usually provided as `libboost_program_options.so` on Linux \|

	Boost headers are generally portable across Linux distributions, but compiled Boost libraries depend on the operating system, CPU architecture, compiler, and `libstdc++` ABI. Therefore, compiled Boost libraries should not be copied directly between Linux, macOS, Windows, or different CPU architectures.

	It is recommended to use Boost headers and runtime libraries from the same Boost version.

	The upstream Makefile is configured primarily for macOS Homebrew. On Linux, the following command can be used directly only when Boost is already available in the compiler's default search paths:

	```bash
	cd model/CombinatorialAssembler
	make
	cd ../..
	```

	#### Specify a Custom Boost Path

	If Boost is not installed in the default compiler search paths, define the following environment variables:

	```bash
	export COMBFOLD_BOOST_INCLUDE="<Boost source or include directory>"
	export COMBFOLD_BOOST_LIB="<Boost library directory>"
	```

	`COMBFOLD_BOOST_INCLUDE` must point to a directory that directly contains the `boost/` subdirectory.

	`COMBFOLD_BOOST_LIB` must point to a directory that directly contains the `libboost_program_options` library.

	Before compilation, you can verify the paths using:

	```bash
	test -f "${COMBFOLD_BOOST_INCLUDE}/boost/algorithm/string.hpp" \
	&& echo "Boost headers OK"

	find "${COMBFOLD_BOOST_LIB}" -maxdepth 1 \
	-name 'libboost_program_options*' -print
	```

	Compile the assembler:

	```bash
	cd model/CombinatorialAssembler

	make -j4 \
	BOOST_INCLUDE="${COMBFOLD_BOOST_INCLUDE}" \
	BOOST_LIB="${COMBFOLD_BOOST_LIB}"

	cd ../..
	```

	### Weights and Data Preparation

	The CombFold combinatorial assembler itself does not require model weights.

	Local generation of AlphaFold-Multimer subcomplex predictions requires the five official AlphaFold-Multimer v3 parameter files:

	\| Asset \| Location in the Model Package \| Purpose \|
	\| --- \| --- \| --- \|
	\| `params_model_1_multimer_v3.npz` \| `weight/alphafold/params/` \| AFM v3 model 1 \|
	\| `params_model_2_multimer_v3.npz` \| `weight/alphafold/params/` \| AFM v3 model 2 \|
	\| `params_model_3_multimer_v3.npz` \| `weight/alphafold/params/` \| AFM v3 model 3 \|
	\| `params_model_4_multimer_v3.npz` \| `weight/alphafold/params/` \| AFM v3 model 4 \|
	\| `params_model_5_multimer_v3.npz` \| `weight/alphafold/params/` \| AFM v3 model 5 \|

	Use the following ColabFold data path:

	```text
	--data weight/alphafold
	```

	If you only use existing AlphaFold-Multimer PDB predictions for combinatorial assembly, AlphaFold-Multimer weights and a DCU device are not required.

	### Define Subunits

	The input `subunits.json` is a JSON dictionary keyed by unique subunit names.

	Each subunit contains the following fields:

	- `name`: unique subunit name;
	- `sequence`: amino acid sequence;
	- `chain_names`: chain names corresponding to this subunit in the complete complex. The number of entries also defines its stoichiometry;
	- `start_res`: starting residue index of the sequence in the original chain.

	Example:

	```json
	{
	"A0": {
	"name": "A0",
	"chain_names": ["A", "B"],
	"start_res": 1,
	"sequence": "MKDILEKLEERRAQARLGGGEKRLEAQHKRGKLTARERIELLLDHGSFEE"
	}
	}
	```

	The Hugging Face model package provides a complete example:

	```text
	scripts/example/subunits.json
	scripts/example/pdbs/
	```

	### Quick Inference: CPU Assembly from Existing PDB Files

	This is the shortest CombFold inference path and does not run AlphaFold-Multimer:

	```bash
	python scripts/inference.py \
	--subunits scripts/example/subunits.json \
	--pdbs scripts/example/pdbs \
	--output output/example_assembly
	```

	The output directory must either not exist or be empty before execution.

	After successful execution, the main results are located at:

	```text
	output/example_assembly/assembled_results/output_clustered_0.pdb
	output/example_assembly/assembled_results/confidence.txt
	```

	You can also use the original upstream positional-argument entry point:

	```bash
	python scripts/run_on_pdbs.py \
	scripts/example/subunits.json \
	scripts/example/pdbs \
	output/example_assembly
	```

	### Generate Pairwise FASTA Files

	Generate FASTA files for every pair of unique subunits defined in `subunits.json`:

	```bash
	python scripts/prepare_fastas.py \
	scripts/example/subunits.json \
	--stage pairs \
	--output-fasta-folder output/pair_fastas \
	--max-af-size 1800
	```

	The output directory must not already exist.

	The official example generates files such as:

	```text
	A0_A0.fasta
	A0_G0.fasta
	G0_G0.fasta
	```

	### Minimal DCU Pairwise Inference

	For an offline smoke test on a compute node, you can use `single_sequence`, one model, and one recycle:

	```bash
	colabfold_batch \
	output/pair_fastas \
	output/colabfold_pairs \
	--data weight/alphafold \
	--model-type alphafold2_multimer_v3 \
	--model-order 1 \
	--num-models 1 \
	--num-recycle 1 \
	--num-relax 0 \
	--msa-mode single_sequence \
	--disable-unified-memory
	```

	The configuration:

	```text
	single_sequence + 1 model + 1 recycle
	```

	is intended only to verify parameter loading, JAX/DCU forward execution, and PDB output generation.

	It should not be used to evaluate formal prediction accuracy.

	For production-quality predictions, prepare appropriate MSA features and increase the number of models and recycles according to available memory and runtime constraints.

	### End-to-End DCU-to-CPU Inference

	At least one predicted PDB structure must be selected for each pair.

	To select the top-ranked ColabFold structure:

	```bash
	mkdir -p output/combfold_pdbs

	find output/colabfold_pairs -maxdepth 1 \
	-type f -name 'rank_001.pdb' \
	-exec cp {} output/combfold_pdbs/ \;
	```

	Then run the CombFold combinatorial assembly stage:

	```bash
	python scripts/inference.py \
	--subunits scripts/example/subunits.json \
	--pdbs output/combfold_pdbs \
	--output output/end2end_assembly
	```

	The unified inference entry point also outputs a machine-readable summary, for example:

	```text
	COMBFOLD_INFERENCE_RESULT={"assembled_structures": 5, "format": "pdb", "status": "PASS", ...}
	```

	### Use Crosslinking Restraints

	Use the `--crosslinks` option to provide a crosslink restraint file:

	```bash
	python scripts/inference.py \
	--subunits scripts/example/example_xlinks/subunits.json \
	--pdbs scripts/example/example_xlinks/pdbs \
	--crosslinks scripts/example/example_xlinks/crosslinks.txt \
	--output output/crosslink_assembly
	```

	### Optional Prediction of Larger Subcomplexes

	After completing pairwise prediction, larger subcomplex FASTA files can be generated based on the pairwise results:

	```bash
	python scripts/prepare_fastas.py \
	scripts/example/subunits.json \
	--stage groups \
	--output-fasta-folder output/group_fastas \
	--max-af-size 1800 \
	--input-pairs-results output/combfold_pdbs
	```

	### Training

	CombFold is an inference algorithm that performs combinatorial assembly using predictions generated by pretrained AlphaFold-Multimer models.

	CombFold itself does not contain a trainable neural network, training entry point, optimizer, or training-data pipeline. Therefore, this Hugging Face model package does not provide training commands.

	Retraining AlphaFold-Multimer is a separate upstream large-scale model-training task and is not part of the CombFold combinatorial assembly workflow.

	# OneScience Official Resources

	\| Platform \| OneScience Main Repository \| Skills Repository \|
	\| --- \| --- \| --- \|
	\| Gitee \| https://gitee.com/onescience-ai/onescience \| https://gitee.com/onescience-ai/oneskills \|
	\| GitHub \| https://github.com/onescience-ai/OneScience \| https://github.com/onescience-ai/oneskills \|

	# Citation and License

	- Paper: [Assembly of protein complexes by combining AlphaFold and combinatorial optimization](https://www.nature.com/articles/s41592-024-02174-0)
	- Official implementation: https://github.com/dina-lab3D/CombFold
	- The CombFold source code is provided under the Apache License 2.0 according to the upstream repository.
	- AlphaFold, AlphaFold-Multimer, ColabFold, pretrained parameters, and other third-party components are subject to their respective original copyright notices, model terms, and licenses.