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本家様 https://github.com/bytedance/Protenix
pipy https://pypi.org/project/protenix/
Toward High-Accuracy Open-Source Biomolecular Structure Prediction.
pipyのサイトからPython >=3.11みたい
git clone https://github.com/yyuu/pyenv.git /apps/pyenv
export PYENV_ROOT=/apps/pyenv
export PATH=$PYENV_ROOT/bin:$PATH
pyenv install miniforge3-26.3.2-1
source /apps/pyenv/versions/miniforge3-26.3.2-1/etc/profile.d/conda.sh
conda update conda
(既に環境があるなら)
source /apps/pyenv/versions/miniforge3-26.3.2-1/etc/profile.d/conda.sh[root@r9 ~]# conda create -n protenix python=3.11 pip
[root@r9 ~]# conda activate protenix
(protenix) [root@r9 ~]# pip install protenix
(protenix) [root@r9 ~]# conda list
# packages in environment at /apps/pyenv/versions/miniforge3-26.3.2-1/envs/protenix:
#
# Name Version Build Channel
_openmp_mutex 4.5 20_gnu conda-forge
absl-py 2.4.0 pypi_0 pypi
annotated-types 0.7.0 pypi_0 pypi
asttokens 3.0.1 pypi_0 pypi
biopython 1.85 pypi_0 pypi
biotite 1.4.0 pypi_0 pypi
biotraj 1.2.2 pypi_0 pypi
bzip2 1.0.8 hda65f42_9 conda-forge
ca-certificates 2026.5.20 hbd8a1cb_0 conda-forge
certifi 2026.5.20 pypi_0 pypi
charset-normalizer 3.4.7 pypi_0 pypi
click 8.4.1 pypi_0 pypi
colorama 0.4.6 pypi_0 pypi
comm 0.2.3 pypi_0 pypi
contourpy 1.3.3 pypi_0 pypi
cuequivariance 0.10.0 pypi_0 pypi
cuequivariance-ops-cu12 0.8.0 pypi_0 pypi
cuequivariance-ops-torch-cu12 0.8.0 pypi_0 pypi
cuequivariance-torch 0.8.0 pypi_0 pypi
cycler 0.12.1 pypi_0 pypi
decorator 5.3.1 pypi_0 pypi
deepspeed 0.17.5 pypi_0 pypi
einops 0.8.2 pypi_0 pypi
executing 2.2.1 pypi_0 pypi
fair-esm 2.0.0 pypi_0 pypi
filelock 3.29.3 pypi_0 pypi
fonttools 4.63.0 pypi_0 pypi
fsspec 2026.4.0 pypi_0 pypi
gemmi 0.6.7 pypi_0 pypi
gitdb 4.0.12 pypi_0 pypi
gitpython 3.1.50 pypi_0 pypi
hjson 3.1.0 pypi_0 pypi
icecream 2.1.7 pypi_0 pypi
idna 3.18 pypi_0 pypi
ihm 2.11 pypi_0 pypi
ipdb 0.13.13 pypi_0 pypi
ipython 9.14.1 pypi_0 pypi
ipython-pygments-lexers 1.1.1 pypi_0 pypi
ipywidgets 8.1.7 pypi_0 pypi
jedi 0.20.0 pypi_0 pypi
jinja2 3.1.6 pypi_0 pypi
joblib 1.5.3 pypi_0 pypi
jupyterlab-widgets 3.0.16 pypi_0 pypi
kiwisolver 1.5.0 pypi_0 pypi
ld_impl_linux-64 2.45.1 default_hbd61a6d_102 conda-forge
libexpat 2.8.1 hecca717_1 conda-forge
libffi 3.5.2 h3435931_0 conda-forge
libgcc 15.2.0 he0feb66_19 conda-forge
libgcc-ng 15.2.0 h69a702a_19 conda-forge
libgomp 15.2.0 he0feb66_19 conda-forge
liblzma 5.8.3 hb03c661_0 conda-forge
libnsl 2.0.1 hb9d3cd8_1 conda-forge
libsqlite 3.53.2 h0c1763c_0 conda-forge
libuuid 2.42.1 h5347b49_0 conda-forge
libxcrypt 4.4.36 hd590300_1 conda-forge
libzlib 1.3.2 h25fd6f3_2 conda-forge
markupsafe 3.0.3 pypi_0 pypi
matplotlib 3.10.5 pypi_0 pypi
matplotlib-inline 0.2.2 pypi_0 pypi
ml-collections 1.1.0 pypi_0 pypi
modelcif 1.4 pypi_0 pypi
mpmath 1.3.0 pypi_0 pypi
msgpack 1.2.0 pypi_0 pypi
ncurses 6.6 hdb14827_0 conda-forge
networkx 3.6.1 pypi_0 pypi
ninja 1.13.0 pypi_0 pypi
numpy 2.4.1 pypi_0 pypi
nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi
nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
nvidia-ml-py 13.610.43 pypi_0 pypi
nvidia-nccl-cu12 2.26.2 pypi_0 pypi
nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
openssl 3.6.3 h35e630c_0 conda-forge
opt-einsum 3.4.0 pypi_0 pypi
optree 0.17.0 pypi_0 pypi
packaging 26.2 pyhc364b38_0 conda-forge
pandas 2.3.1 pypi_0 pypi
parso 0.8.7 pypi_0 pypi
pdbeccdutils 1.0.0 pypi_0 pypi
pexpect 4.9.0 pypi_0 pypi
pillow 12.2.0 pypi_0 pypi
pip 26.1.2 pyh8b19718_0 conda-forge
platformdirs 4.10.0 pypi_0 pypi
prompt-toolkit 3.0.52 pypi_0 pypi
protenix 2.0.0 pypi_0 pypi
protobuf 6.31.1 pypi_0 pypi
psutil 7.2.2 pypi_0 pypi
ptyprocess 0.7.0 pypi_0 pypi
pure-eval 0.2.3 pypi_0 pypi
py-cpuinfo 9.0.0 pypi_0 pypi
py3dmol 2.5.2 pypi_0 pypi
pydantic 2.13.4 pypi_0 pypi
pydantic-core 2.46.4 pypi_0 pypi
pygments 2.20.0 pypi_0 pypi
pyparsing 3.3.2 pypi_0 pypi
python 3.11.15 h7508c33_1_cpython conda-forge
python-dateutil 2.9.0.post0 pypi_0 pypi
pytz 2026.2 pypi_0 pypi
pyyaml 6.0.2 pypi_0 pypi
rdkit 2025.9.3 pypi_0 pypi
readline 8.3 h853b02a_0 conda-forge
requests 2.34.2 pypi_0 pypi
scikit-learn 1.7.1 pypi_0 pypi
scikit-learn-extra 0.3.0 pypi_0 pypi
scipy 1.17.1 pypi_0 pypi
sentry-sdk 2.62.0 pypi_0 pypi
setuptools 82.0.1 pyh332efcf_0 conda-forge
six 1.17.0 pypi_0 pypi
smmap 5.0.3 pypi_0 pypi
stack-data 0.6.3 pypi_0 pypi
sympy 1.14.0 pypi_0 pypi
threadpoolctl 3.6.0 pypi_0 pypi
tk 8.6.13 noxft_h366c992_103 conda-forge
torch 2.7.1 pypi_0 pypi
torchaudio 2.7.1 pypi_0 pypi
torchvision 0.22.1 pypi_0 pypi
tqdm 4.67.1 pypi_0 pypi
traitlets 5.15.1 pypi_0 pypi
triton 3.3.1 pypi_0 pypi
typing-extensions 4.15.0 pypi_0 pypi
typing-inspection 0.4.2 pypi_0 pypi
tzdata 2026.2 pypi_0 pypi
urllib3 2.7.0 pypi_0 pypi
wandb 0.21.1 pypi_0 pypi
wcwidth 0.8.1 pypi_0 pypi
wheel 0.47.0 pyhd8ed1ab_0 conda-forge
widgetsnbextension 4.0.15 pypi_0 pypi
zstd 1.5.7 hb78ec9c_6 conda-forge
(protenix) [root@r9 ~]#確認
(protenix) [root@r9 ~]# python
Python 3.11.15 | packaged by conda-forge | (main, Jun 11 2026, 03:34:02) [GCC 14.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.cuda.is_available())
True
>>> print(torch.cuda.get_device_name())
/apps/pyenv/versions/miniforge3-26.3.2-1/envs/protenix/lib/python3.11/site-packages/torch/cuda/__init__.py:287: UserWarning:
NVIDIA RTX PRO 2000 Blackwell with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_90.
If you want to use the NVIDIA RTX PRO 2000 Blackwell GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
warnings.warn(
NVIDIA RTX PRO 2000 Blackwell
>>> quit();
(protenix) [root@r9 ~]#っと今入っている torch 2.7.1 ではblackwellは無理みたい
なので差し替え
(protenix) [root@r9 ~]# python -m pip uninstall -y torch torchvision torchaudio triton
(protenix) [root@r9 ~]# python -m pip cache purge
(protenix) [root@r9 ~]# python -m pip install --pre torch==2.7.1 torchaudio==2.7.1 torchvision==0.22.1 --index-url https://download.pytorch.org/whl/cu128
(protenix) [root@r9 ~]# conda list
:
numpy 2.4.1 pypi_0 pypi
nvidia-cublas-cu12 12.8.3.14 pypi_0 pypi
nvidia-cuda-cupti-cu12 12.8.57 pypi_0 pypi
nvidia-cuda-nvrtc-cu12 12.8.61 pypi_0 pypi
nvidia-cuda-runtime-cu12 12.8.57 pypi_0 pypi
nvidia-cudnn-cu12 9.7.1.26 pypi_0 pypi
nvidia-cufft-cu12 11.3.3.41 pypi_0 pypi
nvidia-cufile-cu12 1.13.0.11 pypi_0 pypi
nvidia-curand-cu12 10.3.9.55 pypi_0 pypi
nvidia-cusolver-cu12 11.7.2.55 pypi_0 pypi
nvidia-cusparse-cu12 12.5.7.53 pypi_0 pypi
nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
nvidia-ml-py 13.610.43 pypi_0 pypi
nvidia-nccl-cu12 2.26.2 pypi_0 pypi
nvidia-nvjitlink-cu12 12.8.61 pypi_0 pypi
nvidia-nvtx-cu12 12.8.55 pypi_0 pypi
openssl 3.6.3 h35e630c_0 conda-forge
:
python 3.11.15 h7508c33_1_cpython conda-forge
:
torch 2.7.1+cu128 pypi_0 pypi
torchaudio 2.7.1+cu128 pypi_0 pypi
torchvision 0.22.1+cu128 pypi_0 pypi
:
(protenix) [root@r9 ~]#
(protenix) [root@r9 ~]# python
Python 3.11.15 | packaged by conda-forge | (main, Jun 11 2026, 03:34:02) [GCC 14.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.cuda.is_available())
True
>>> print(torch.cuda.get_device_name())
NVIDIA RTX PRO 2000 Blackwell
>>> quit();
(protenix) [root@r9 ~]#「/apps/modulefiles/protenix」
#%Module
set root /apps/pyenv/versions/miniforge3-26.3.2-1/envs/protenix
prepend-path PATH $root/bin
prepend-path LD_LIBRARY_PATH $root/lib[saber@r9 ~]$ module load protenix
[saber@r9 ~]$ cd test
[saber@r9 test]$ git clone https://github.com/bytedance/Protenix
Cloning into 'Protenix'...
remote: Enumerating objects: 2642, done.
remote: Total 2642 (delta 0), reused 0 (delta 0), pack-reused 2642 (from 1)
Receiving objects: 100% (2642/2642), 105.22 MiB | 27.46 MiB/s, done.
Resolving deltas: 100% (1644/1644), done.
[saber@r9 test]$ cd Protenix/
[saber@r9 Protenix]$
[saber@r9 Protenix]$ protenix --version
protenix, version 2.0.0
[saber@r9 Protenix]$ protenix --help
Usage: protenix [OPTIONS] COMMAND [ARGS]...
Protenix: A trainable reproduction of AlphaFold 3.
This CLI provides tools for structure prediction, data conversion, and
MSA/template searching.
Options:
--version Show the version and exit.
-h, --help Show this message and exit.
Commands:
json Convert PDB or CIF files to JSON files for Protenix inference.
msa Perform MSA search using MMseqs2.
mt Perform MSA search followed by template search.
pred Run predictions with Protenix using various input formats.
prep Perform MSA search, template search, and RNA MSA search...
[saber@r9 Protenix]$
[saber@r9 Protenix]$
[saber@r9 Protenix]$ protenix pred -i examples/input.json -o ./output -n protenix_base_default_v1.0.0
