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alphafold2_install

Deep Learning linux
发布日期:   2024-10-18
更新日期:   2024-10-18
文章字数:   3.3k
阅读时长:   19 分

原始的AlphaFold2需要docker环境，目前我们服务器还没有配置docker，就先用conda环境替代吧。

1.软件下载

git clone https://github.com/kalininalab/alphafold_non_docker.git

wget https://github.com/google-deepmind/alphafold/archive/refs/tags/v2.3.2.tar.gz
tar -zxvf v2.3.2.tar.gz

# 环境配置建议参考alphafold_non_docker里的README.md，特别注意CUDA版本相关的包
conda create -n alphafold2
conda activate alphafold2
conda install -y -c conda-forge openmm=8.0.0 pdbfixer python=3.10 -c conda-forge
conda install nvidia/label/cuda-12.4.0::cuda-toolkit 
conda install cudnn=8.9.2.26=cuda12_0 -c nvidia
conda install -y -c bioconda hmmer hhsuite==3.3.0 kalign2 cudnn=8.9
pip install absl-py==1.0.0 biopython==1.79 chex==0.0.7 dm-haiku==0.0.13 dm-tree==0.1.6 immutabledict==2.0.0 ml-collections==0.1.0 numpy==1.22 pandas==1.3.4 protobuf==3.20.1 scipy==1.11.4 tensorflow-cpu==2.9.0
pip3 install --upgrade --no-cache-dir jax==0.4.23 jaxlib==0.4.23+cuda12.cudnn89 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# 下载依赖数据库(非常大)
bash alphafold_non_docker/download_db.sh -d database

安装的时候非常混乱，这里给出我的环境，基于CUDA12.4, 可以直接通过conda安装：
conda env create -f alphafold2.yml

alphafold2.yml(CUDA12.4)
name: alphafold2
channels:
  - bioconda
  - nvidia
  - nvidia/label/cuda-12.4.0
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - bzip2=1.0.8=h4bc722e_7
  - ca-certificates=2024.9.24=h06a4308_0
  - cuda-cccl=12.4.99=0
  - cuda-command-line-tools=12.4.0=0
  - cuda-compiler=12.4.0=0
  - cuda-cudart=12.4.99=0
  - cuda-cudart-dev=12.4.99=0
  - cuda-cudart-static=12.4.99=0
  - cuda-cuobjdump=12.4.99=0
  - cuda-cupti=12.4.99=0
  - cuda-cupti-static=12.4.99=0
  - cuda-cuxxfilt=12.4.99=0
  - cuda-documentation=12.4.99=0
  - cuda-driver-dev=12.4.99=0
  - cuda-gdb=12.4.99=0
  - cuda-libraries=12.4.0=0
  - cuda-libraries-dev=12.4.0=0
  - cuda-libraries-static=12.4.0=0
  - cuda-nsight=12.4.99=0
  - cuda-nsight-compute=12.4.0=0
  - cuda-nvcc=12.4.99=0
  - cuda-nvdisasm=12.4.99=0
  - cuda-nvml-dev=12.4.99=0
  - cuda-nvprof=12.4.99=0
  - cuda-nvprune=12.4.99=0
  - cuda-nvrtc=12.4.99=0
  - cuda-nvrtc-dev=12.4.99=0
  - cuda-nvrtc-static=12.4.99=0
  - cuda-nvtx=12.4.99=0
  - cuda-nvvp=12.4.99=0
  - cuda-opencl=12.4.99=0
  - cuda-opencl-dev=12.4.99=0
  - cuda-profiler-api=12.4.99=0
  - cuda-sanitizer-api=12.4.99=0
  - cuda-toolkit=12.4.0=0
  - cuda-tools=12.4.0=0
  - cuda-version=12.6=3
  - cuda-visual-tools=12.4.0=0
  - cudnn=8.9.2.26=cuda12_0
  - gds-tools=1.9.0.20=0
  - hhsuite=3.3.0=py310pl5321hc31ed2c_13
  - hmmer=3.4=hdbdd923_2
  - kalign2=2.04=h031d066_7
  - ld_impl_linux-64=2.43=h712a8e2_1
  - libblas=3.9.0=24_linux64_openblas
  - libcblas=3.9.0=24_linux64_openblas
  - libcublas=12.4.2.65=0
  - libcublas-dev=12.4.2.65=0
  - libcublas-static=12.4.2.65=0
  - libcufft=11.2.0.44=0
  - libcufft-dev=11.2.0.44=0
  - libcufft-static=11.2.0.44=0
  - libcufile=1.9.0.20=0
  - libcufile-dev=1.9.0.20=0
  - libcufile-static=1.9.0.20=0
  - libcurand=10.3.5.119=0
  - libcurand-dev=10.3.5.119=0
  - libcurand-static=10.3.5.119=0
  - libcusolver=11.6.0.99=0
  - libcusolver-dev=11.6.0.99=0
  - libcusolver-static=11.6.0.99=0
  - libcusparse=12.3.0.142=0
  - libcusparse-dev=12.3.0.142=0
  - libcusparse-static=12.3.0.142=0
  - libffi=3.4.2=h7f98852_5
  - libgcc=14.2.0=h77fa898_1
  - libgcc-ng=14.2.0=h69a702a_1
  - libgfortran=14.2.0=h69a702a_1
  - libgfortran-ng=14.2.0=h69a702a_1
  - libgfortran5=14.2.0=hd5240d6_1
  - libgomp=14.2.0=h77fa898_1
  - liblapack=3.9.0=24_linux64_openblas
  - libnpp=12.2.5.2=0
  - libnpp-dev=12.2.5.2=0
  - libnpp-static=12.2.5.2=0
  - libnsl=2.0.1=hd590300_0
  - libnvfatbin=12.4.99=0
  - libnvfatbin-dev=12.4.99=0
  - libnvjitlink=12.4.99=0
  - libnvjitlink-dev=12.4.99=0
  - libnvjpeg=12.3.1.89=0
  - libnvjpeg-dev=12.3.1.89=0
  - libnvjpeg-static=12.3.1.89=0
  - libopenblas=0.3.27=pthreads_hac2b453_1
  - libsqlite=3.46.1=hadc24fc_0
  - libstdcxx=14.2.0=hc0a3c3a_1
  - libstdcxx-ng=14.2.0=h4852527_1
  - libuuid=2.38.1=h0b41bf4_0
  - libxcrypt=4.4.36=hd590300_1
  - libzlib=1.3.1=hb9d3cd8_2
  - ncurses=6.5=he02047a_1
  - nsight-compute=2024.1.0.13=0
  - ocl-icd=2.3.2=hd590300_1
  - ocl-icd-system=1.0.0=1
  - openmm=8.0.0=py310h9995159_4
  - openssl=3.3.2=hb9d3cd8_0
  - pdbfixer=1.9=pyh1a96a4e_0
  - perl=5.32.1=0_h5eee18b_perl5
  - pip=24.2=pyh8b19718_1
  - python=3.10.15=h4a871b0_2_cpython
  - python_abi=3.10=5_cp310
  - readline=8.2=h8228510_1
  - setuptools=75.1.0=pyhd8ed1ab_0
  - tk=8.6.13=noxft_h4845f30_101
  - tzdata=2024b=hc8b5060_0
  - wheel=0.44.0=pyhd8ed1ab_0
  - xz=5.2.6=h166bdaf_0
  - pip:
      - absl-py==1.0.0
      - astunparse==1.6.3
      - biopython==1.79
      - cachetools==5.5.0
      - certifi==2024.8.30
      - charset-normalizer==3.4.0
      - chex==0.0.7
      - contextlib2==21.6.0
      - dm-haiku==0.0.13
      - dm-tree==0.1.6
      - flatbuffers==1.12
      - gast==0.4.0
      - google-auth==2.35.0
      - google-auth-oauthlib==0.4.6
      - google-pasta==0.2.0
      - grpcio==1.67.0
      - h5py==3.12.1
      - idna==3.10
      - immutabledict==2.0.0
      - jax==0.4.23
      - jaxlib==0.4.23+cuda12.cudnn89
      - jmp==0.0.4
      - keras==2.9.0
      - keras-preprocessing==1.1.2
      - libclang==18.1.1
      - markdown==3.7
      - markupsafe==3.0.1
      - ml-collections==0.1.0
      - ml-dtypes==0.5.0
      - numpy==1.22.0
      - oauthlib==3.2.2
      - opt-einsum==3.4.0
      - packaging==24.1
      - pandas==1.3.4
      - protobuf==3.20.1
      - pyasn1==0.6.1
      - pyasn1-modules==0.4.1
      - python-dateutil==2.9.0.post0
      - pytz==2024.2
      - pyyaml==6.0.2
      - requests==2.32.3
      - requests-oauthlib==2.0.0
      - rsa==4.9
      - scipy==1.11.4
      - six==1.16.0
      - tabulate==0.9.0
      - tensorboard==2.9.0
      - tensorboard-data-server==0.6.1
      - tensorboard-plugin-wit==1.8.1
      - tensorflow-cpu==2.9.0
      - tensorflow-estimator==2.9.0
      - tensorflow-io-gcs-filesystem==0.37.1
      - termcolor==2.5.0
      - toolz==1.0.0
      - typing-extensions==4.12.2
      - urllib3==2.2.3
      - werkzeug==3.0.4
      - wrapt==1.16.0
prefix: /data/chaofan/miniconda3/envs/alphafold2

这个是我之前基于CUDA11.6的环境：

alphafold2.yml(CUDA11.6)
name: alphafold2
channels:
  - nvidia
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - blas=1.0=mkl
  - ca-certificates=2023.7.22=hbcca054_0
  - cuda-nvcc=11.6.124=hbba6d2d_0
  - cudatoolkit=11.2.2=hbe64b41_10
  - fftw=3.3.9=h27cfd23_1
  - hhsuite=3.3.0=py38pl5321h8ded8fe_5
  - hmmer=3.3.2=h87f3376_2
  - intel-openmp=2021.4.0=h06a4308_3561
  - kalign2=2.04=hec16e2b_3
  - libedit=3.1.20221030=h5eee18b_0
  - libffi=3.2.1=hf484d3e_1007
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libnsl=2.0.0=h5eee18b_0
  - libstdcxx-ng=11.2.0=h1234567_1
  - mkl=2021.4.0=h06a4308_640
  - mkl-service=2.4.0=py38h7f8727e_0
  - mkl_fft=1.3.1=py38hd3c417c_0
  - mkl_random=1.2.2=py38h51133e4_0
  - ncurses=6.4=h6a678d5_0
  - numpy-base=1.24.3=py38h31eccc5_0
  - ocl-icd=2.3.1=h7f98852_0
  - ocl-icd-system=1.0.0=1
  - openmm=7.5.1=py38ha082873_1
  - openssl=1.1.1l=h7f98852_0
  - parallel=20230922=ha770c72_0
  - pdbfixer=1.7=pyhd3deb0d_0
  - perl=5.32.1=0_h5eee18b_perl5
  - pip=23.2.1=py38h06a4308_0
  - python=3.8.0=h0371630_2
  - python_abi=3.8=2_cp38
  - readline=7.0=h7b6447c_5
  - setuptools=68.0.0=py38h06a4308_0
  - six=1.16.0=pyhd3eb1b0_1
  - sqlite=3.33.0=h62c20be_0
  - tk=8.6.12=h1ccaba5_0
  - wheel=0.41.2=py38h06a4308_0
  - xz=5.4.2=h5eee18b_0
  - zlib=1.2.13=h5eee18b_0
  - pip:
      - absl-py==1.0.0
      - alphapulldown==0.30.7
      - anyio==4.0.0
      - appdirs==1.4.4
      - argon2-cffi==23.1.0
      - argon2-cffi-bindings==21.2.0
      - arrow==1.3.0
      - asttokens==2.4.0
      - astunparse==1.6.3
      - async-lru==2.0.4
      - attrs==23.1.0
      - babel==2.13.0
      - backcall==0.2.0
      - beautifulsoup4==4.12.2
      - biopython==1.78
      - bleach==6.1.0
      - cachetools==5.3.1
      - certifi==2023.7.22
      - cffi==1.16.0
      - charset-normalizer==3.3.0
      - chex==0.0.7
      - comm==0.1.4
      - contextlib2==21.6.0
      - cycler==0.12.1
      - debugpy==1.8.0
      - decorator==5.1.1
      - defusedxml==0.7.1
      - dm-haiku==0.0.9
      - dm-tree==0.1.6
      - exceptiongroup==1.1.3
      - executing==2.0.0
      - fastjsonschema==2.18.1
      - flatbuffers==1.12
      - fqdn==1.5.1
      - gast==0.4.0
      - google-auth==2.23.2
      - google-auth-oauthlib==0.4.6
      - google-pasta==0.2.0
      - grpcio==1.59.0
      - h5py==3.1.0
      - idna==3.4
      - immutabledict==2.0.0
      - importlib-metadata==6.8.0
      - importlib-resources==5.8.0
      - ipykernel==6.25.2
      - ipython==8.12.3
      - ipywidgets==8.1.1
      - isoduration==20.11.0
      - jax==0.3.25
      - jaxlib==0.3.25+cuda11.cudnn805
      - jedi==0.19.1
      - jinja2==3.1.2
      - jmp==0.0.4
      - json5==0.9.14
      - jsonpointer==2.4
      - jsonschema==4.19.1
      - jsonschema-specifications==2023.7.1
      - jupyter-client==8.4.0
      - jupyter-core==5.4.0
      - jupyter-events==0.7.0
      - jupyter-lsp==2.2.0
      - jupyter-server==2.7.3
      - jupyter-server-terminals==0.4.4
      - jupyterlab==4.0.7
      - jupyterlab-pygments==0.2.2
      - jupyterlab-server==2.25.0
      - jupyterlab-widgets==3.0.9
      - keras==2.9.0
      - keras-preprocessing==1.1.2
      - kiwisolver==1.4.5
      - libclang==16.0.6
      - markdown==3.4.4
      - markupsafe==2.1.3
      - matplotlib==3.3.3
      - matplotlib-inline==0.1.6
      - mistune==2.0.5
      - ml-collections==0.1.0
      - ml-dtypes==0.2.0
      - nbclient==0.8.0
      - nbconvert==7.4.0
      - nbformat==5.4.0
      - nest-asyncio==1.5.8
      - notebook-shim==0.2.3
      - numpy==1.21.6
      - oauthlib==3.2.2
      - opt-einsum==3.3.0
      - overrides==7.4.0
      - packaging==23.2
      - pandas==1.3.4
      - pandocfilters==1.5.0
      - parso==0.8.3
      - pexpect==4.8.0
      - pickleshare==0.7.5
      - pillow==10.0.1
      - pkgutil-resolve-name==1.3.10
      - platformdirs==3.11.0
      - prometheus-client==0.17.1
      - prompt-toolkit==3.0.39
      - protobuf==3.20.1
      - psutil==5.9.5
      - ptyprocess==0.7.0
      - pure-eval==0.2.2
      - py3dmol==2.0.1
      - pyasn1==0.5.0
      - pyasn1-modules==0.3.0
      - pycparser==2.21
      - pygments==2.16.1
      - pyparsing==3.1.1
      - python-dateutil==2.8.2
      - python-json-logger==2.0.7
      - pytz==2023.3.post1
      - pyyaml==6.0.1
      - pyzmq==25.1.1
      - referencing==0.30.2
      - requests==2.31.0
      - requests-oauthlib==1.3.1
      - rfc3339-validator==0.1.4
      - rfc3986-validator==0.1.1
      - rpds-py==0.10.6
      - rsa==4.9
      - scipy==1.7.0
      - send2trash==1.8.2
      - sniffio==1.3.0
      - soupsieve==2.5
      - stack-data==0.6.3
      - tabulate==0.9.0
      - tensorboard==2.9.0
      - tensorboard-data-server==0.6.1
      - tensorboard-plugin-wit==1.8.1
      - tensorflow==2.9.0
      - tensorflow-cpu==2.9.0
      - tensorflow-estimator==2.9.0
      - tensorflow-io-gcs-filesystem==0.34.0
      - termcolor==2.3.0
      - terminado==0.17.1
      - tinycss2==1.2.1
      - tomli==2.0.1
      - toolz==0.12.0
      - tornado==6.3.3
      - tqdm==4.66.1
      - traitlets==5.11.2
      - types-python-dateutil==2.8.19.14
      - typing-extensions==4.8.0
      - uri-template==1.3.0
      - urllib3==2.0.6
      - wcwidth==0.2.8
      - webcolors==1.13
      - webencodings==0.5.1
      - websocket-client==1.6.4
      - werkzeug==3.0.0
      - widgetsnbextension==4.0.9
      - wrapt==1.15.0
      - zipp==3.17.0
prefix: /data/chaofan/software/miniconda3/envs/alphafold2

2.测试

scp alphafold_non_docker/run_alphafold.sh alphafold-2.3.2/

# 修改下相关文件地址
vi alphafold-2.3.2/run_alphafold.sh

# 
bash alphafold-2.3.2/run_alphafold.sh -c reduced_dbs -d ./database/ -o ./dummy_test/ -f t2.fa -t 2024-10-17 -m multimer -l 1 -n 40
t2.fa
>Pip1
VTEVPNSMDWRKRGSVTGVKDQGVCGCCWAFSAAAAIEGAYQIANNELISLSEQQLLDCSTQNKGCEGGLMTVAYDFLLQNNGGGITTETNYPYEEAQNVCKTEQPAAVTINGYEVVPSDESSLLKAVVNQPISVGIAANDEFHMYGSGIYDGSCNSRLNHAVTVIGYGTSEEDGTKYWIVKNSWGSDWGEEGYMRIARDVGVDGGHCGIAKVASFPTA
>EpiC2B
QLNGYSKKEVTPEDTELLQKAQSNVSAYNSDVTSRICYLKVDSLETQVVSGENYKFHVSGCSVNSDKELGGCANQNCESSKYDIVIYSQSWTNTLKVTSITPAN

结果与我们之前在CUDA11.6版本上的一致，也与原文章结果一致。

result
{
    "iptm+ptm": {
        "model_1_multimer_v3_pred_0": 0.913495827158252,
        "model_2_multimer_v3_pred_0": 0.9082750066457126,
        "model_3_multimer_v3_pred_0": 0.9185924126209433,
        "model_4_multimer_v3_pred_0": 0.9199507589621323,
        "model_5_multimer_v3_pred_0": 0.9214059557649581
    },
    "order": [
        "model_5_multimer_v3_pred_0",
        "model_4_multimer_v3_pred_0",
        "model_3_multimer_v3_pred_0",
        "model_1_multimer_v3_pred_0",
        "model_2_multimer_v3_pred_0"
    ]
}

3.安装过程中遇到的问题

1.

Warning: importing ‘simtk.openmm’ is deprecated. Import ‘openmm’ instead.
Traceback (most recent call last):
File “/data/chaofan/software/alphafold2/alphafold-2.3.2/run_alphafold.py”, line 40, in
from alphafold.relax import relax
File “/data/chaofan/software/alphafold2/alphafold-2.3.2/alphafold/relax/relax.py”, line 18, in
from alphafold.relax import amber_minimize
File “/data/chaofan/software/alphafold2/alphafold-2.3.2/alphafold/relax/amber_minimize.py”, line 33, in
from simtk.openmm.app.internal.pdbstructure import PdbStructure
ModuleNotFoundError: No module named ‘simtk.openmm.app.internal’

https://github.com/google-deepmind/alphafold/issues/410

2.

Warning: importing ‘simtk.openmm’ is deprecated. Import ‘openmm’ instead.
FATAL Flags parsing error: Unknown command line flag ‘run_relax’
Pass –helpshort or –helpfull to see help on flags.

删掉run_alphafold.sh文件中的--run_relax=$run_relax

3.

AttributeError: module ‘jax’ has no attribute ‘xla’

pip install dm-haiku -U

还有一种安装方法，自行配置CUDA驱动，参考非root用户安装cuda与cudnn,可以装个和alphafold_non_docker一样的CUDA的版本，配置conda启动时自动替换一些环境变量，可能就没那么麻烦了，这里只提供一种可能的方式，我没有试过。

文章作者: zhangchaofan
文章链接: https://zhangchaofan01.gitee.io/2024/10/18/alphafold2-install/
版权声明: 本博客所有文章除特別声明外，均采用 CC BY 4.0 许可协议。转载请注明来源 zhangchaofan !
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