We’re excited to introduce Protenix — a trainable, open-source PyTorch reproduction of AlphaFold 3.
Protenix is built for high-accuracy structure prediction. It serves as an initial step in our journey toward advancing accessible and extensible research tools for the computational biology community.
- Protenix-Dock: Our implementation of a classical protein-ligand docking framework that leverages empirical scoring functions. Without using deep neural networks, Protenix-Dock delivers competitive performance in rigid docking tasks.
- 2025-05-30: Protenix-v0.5.0 is now available! You may try Protenix-v0.5.0 by accessing the server, or upgrade to the latest version using pip.
- 2025-01-16: The preview version of constraint feature is released to branch
constraint_esm. - 2025-01-16: The training data pipeline is released.
- 2025-01-16: The MSA pipeline is released.
- 2025-01-16: Use local colabfold_search to generate protenix-compatible MSA.
We benchmarked the performance of Protenix-v0.5.0 against Boltz-1 and Chai-1 across multiple datasets, including PoseBusters v2, AF3 Nucleic Acid Complexes, AF3 Antibody Set, and our curated Recent PDB set.
We will soon release the benchmarking toolkit, including the evaluation datasets, data curation pipeline, and metric calculators, to support transparent and reproducible benchmarking.
pip3 install protenixFor development on a CPU-only machine, it is convenient to install with the --cpu flag in editable mode:
python3 setup.py develop --cpu
Check the detailed guide: Docker Installation.
For details on the input JSON format and expected outputs, please refer to the Input/Output Documentation.
If your input is a .pdb or .cif file, you can convert it into a JSON file for inference.
# ensure `release_data/ccd_cache/components.cif` or run:
python scripts/gen_ccd_cache.py -c release_data/ccd_cache/ -n [num_cpu]
# for PDB
# download pdb file
wget https://files.rcsb.org/download/7pzb.pdb
# run with pdb/cif file, and convert it to json file for inference.
protenix tojson --input examples/7pzb.pdb --out_dir ./output
# for CIF (same process)
# download cif file
wget https://files.rcsb.org/download/7pzb.cif
# run with pdb/cif file, and convert it to json file for inference.
protenix tojson --input examples/7pzb.cif --out_dir ./outputWe provide an independent MSA search utility. You can run it using either a JSON file or a protein FASTA file.
# run msa search with json file, it will write precomputed msa dir info to a new json file.
protenix msa --input examples/example_without_msa.json --out_dir ./output
# run msa search with fasta file which only contains protein.
protenix msa --input examples/prot.fasta --out_dir ./outputIf you installed Protenix via pip, you can run the following command to perform model inference:
# the default n_cycle/n_step/n_samples is 10/200/5 respectively, you can modify it by passing --cycle x1 --step x2 --sample x3
# run with example.json, which contains precomputed msa dir.
protenix predict --input examples/example.json --out_dir ./output --seeds 101
# run with multiple json files, the default seed is 101.
protenix predict --input ./jsons_dir/ --out_dir ./output
# if the json do not contain precomputed msa dir,
# add --use_msa_server to search msa and then predict.
# if mutiple seeds are provided, split them by comma.
protenix predict --input examples/example_without_msa.json --out_dir ./output --seeds 101,102 --use_msa_serverAlternatively you can run inference by: Alternatively, run inference via script:
bash inference_demo.shThe script accepts the following arguments:
input_json_path: Path to a JSON file that fully specifies the input structure.dump_dir: Directory where inference results will be saved.dtype: Data type used during inference. Supported options:bf16andfp32.use_msa: Whether to enable MSA features (default: true).use_esm: Whether to enable ESM features (default: false).
Note: By default, layernorm and EvoformerAttention kernels are disabled for simplicity. To enable them and speed up inference, see the Kernels Setup Guide.
Refer to the Training Documentation for setup and details.
Protenix now allows users to specify contacts, enabling the model to leverage additional inter-chain information as constraint guidance! We benchmarked this feature on the PoseBusters dataset and a curated protein-antibody interface subset. Results show that Protenix can generate significantly more accurate structures when guided by constraints. You can try it out via the constraint_esm branch.
Tips: Our online service already supports constraint inputs — no local setup required! However, for local command-line usage, be sure to check out the
constraint_esmbranch, as this feature is not yet included in the main branch.
For details on memory usage and runtime during training and inference, refer to the Training & Inference Cost Documentation.
If you use Protenix in your research, please cite the following:
@article{chen2025protenix,
title={Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction},
author={Chen, Xinshi and Zhang, Yuxuan and Lu, Chan and Ma, Wenzhi and Guan, Jiaqi and Gong, Chengyue and Yang, Jincai and Zhang, Hanyu and Zhang, Ke and Wu, Shenghao and Zhou, Kuangqi and Yang, Yanping and Liu, Zhenyu and Wang, Lan and Shi, Bo and Shi, Shaochen and Xiao, Wenzhi},
year={2025},
doi = {10.1101/2025.01.08.631967},
journal = {bioRxiv}
}
We welcome contributions from the community to help improve Protenix!
📄 Check out the Contributing Guide to get started.
✅ Code Quality:
We use pre-commit hooks to ensure consistency and code quality. Please install them before making commits:
pip install pre-commit
pre-commit install🐞 Found a bug or have a feature request? Open an issue.
The implementation of LayerNorm operators refers to both OneFlow and FastFold.
We also adopted several module implementations from OpenFold, except for LayerNorm, which is implemented independently.
We are committed to fostering a welcoming and inclusive environment. Please review our Code of Conduct for guidelines on how to participate respectfully.
If you discover a potential security issue in this project, or think you may have discovered a security issue, we ask that you notify Bytedance Security via our security center or vulnerability reporting email.
Please do not create a public GitHub issue.
The Protenix project including both code and model parameters is released under the Apache 2.0 License. It is free for both academic research and commercial use.
We welcome inquiries and collaboration opportunities for advanced applications of our model, such as developing new features, fine-tuning for specific use cases, and more. Please feel free to contact us at [email protected].