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Paper: OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
- OpenFold: 重新训练 AlphaFold2 揭示对于学习机制和泛化能力的新见解
OpenFold 是可训练的开源实现用于模拟 AlphaFold2 的结构预测能力,主要特点如下:
- 训练和性能:从头开始训练 OpenFold,并且达到与 AlphaFold2 相当的预测精度。同时 OpenFold 比 AlphaFold2 更快、更节省内存,支持在 PyTorch 框架下运行。
- 学习机制:通过分析 OpenFold 在训练过程中预测的结构,发现一些有趣的现象,例如空间维度、二级结构元素和三级尺度的分阶段学习,以及低维 PCA 投影的近似性。
- 泛化能力:通过使用不同大小和多样性的训练集,以及在结构分类上剔除部分训练数据,来评估 OpenFold 对于未见蛋白质折叠空间的泛化能力。发现 OpenFold 即使在极端缩减的训练集上,也能表现出惊人的鲁棒性和准确性。
GitHub: aqlaboratory/openfold
1. 结构推理
准备模型文件 finetuning_ptm_2.pt
,参考 Huggingface - OpenFold:
pip install bypy
bypy info
mkdir openfold_params
cd openfold_params/
bypy downfile /huggingface/openfold/finetuning_ptm_2.pt finetuning_ptm_2.pt
测试的推理命令,如下:
python3 run_pretrained_openfold.py \
mydata/test \
af2-data-v230/pdb_mmcif/mmcif_files \
--uniref90_database_path af2-data-v230/uniref90/uniref90.fasta \
--mgnify_database_path af2-data-v230/mgnify/mgy_clusters_2022_05.fa \
--pdb70_database_path af2-data-v230/pdb70/pdb70 \
--uniclust30_database_path msa_databases/deepmsa2/uniclust30/uniclust30_2018_08 \
--output_dir mydata/output \
--bfd_database_path af2-data-v230/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
--model_device "cuda:0" \
--jackhmmer_binary_path /opt/openfold/hhsuite-speed/jackhmmer \
--hhblits_binary_path /opt/conda/envs/openfold/bin/hhblits \
--hhsearch_binary_path /opt/conda/envs/openfold/bin/hhsearch \
--kalign_binary_path /opt/conda/envs/openfold/bin/kalign \
--config_preset "model_1_ptm" \
--openfold_checkpoint_path openfold/resources/openfold_params/finetuning_ptm_2.pt
运行日志,如下:
INFO:openfold/openfold/utils/script_utils.py:Loaded OpenFold parameters at openfold/resources/openfold_params/finetuning_ptm_2.pt...
INFO:openfold/run_pretrained_openfold.py:Generating alignments for A...
INFO:openfold/openfold/utils/script_utils.py:Running inference for A...
INFO:openfold/openfold/utils/script_utils.py:Inference time: 10.128928968682885
INFO:openfold/run_pretrained_openfold.py:Output written to mydata/output/predictions/A_model_1_ptm_unrelaxed.pdb...
INFO:openfold/run_pretrained_openfold.py:Running relaxation on mydata/output/predictions/A_model_1_ptm_unrelaxed.pdb...
INFO:openfold/openfold/utils/script_utils.py:Relaxation time: 11.812019010074437
INFO:openfold/openfold/utils/script_utils.py:Relaxed output written to mydata/output/predictions/A_model_1_ptm_relaxed.pdb...
替换高性能的 Jackhmmer,位置如下:
cp backup/hhsuite-speed-3.3.2/jackhmmer /opt/openfold/hhsuite-speed/jackhmmer
模型推理的输出,如下:
alignments/ # MSA文件,与 AF2 相同
predictions/ # 预测结果
timings.json # 时间
tmp_2711.fasta # 缓存fasta
其中,在 timings.json
中,缓存推理耗时,即:
{"inference": 12.08716268837452}
其中,在 alignments/A
文件夹中,包括 MSA 文件,序列数量如下:
mgnify_hits.a3m # 56 行
pdb70_hits.hhr # 159 行
uniref90_hits.a3m # 58 行
bfd_uniref_hits.a3m
注意:与 AF2 不同的是,OpenFold 是 a3m 格式,而 AF2 是 sto 格式。
其中,在 predictions
文件夹中,默认只包括 1 个预测的结构,以及 Relax 的结构,如下:
A_model_1_ptm_relaxed.pdb
A_model_1_ptm_unrelaxed.pdb
timings.json
预测结果如下,其中黄色是 Reference 结构,深蓝色是 AF2 的单模型预测结果,浅蓝色是 OpenFold 的 finetuning_ptm_2.pt
模型预测结果
- AF2:
{'TMScore': 0.9036, 'RMSD(local)': 1.66, 'Align.Len.': 117, 'DockQ': 0.0}
- OpenFold:
{'TMScore': 0.8601, 'RMSD(local)': 1.7, 'Align.Len.': 115, 'DockQ': 0.0}
即:
2. 环境配置
构建 base docker 环境,基于 AF2 的 docker,即:
nvidia-docker run -it --name openfold-[your name] -v [nfs path]:[nfs path] af2:v1.02
2.1 配置 conda 与 pip 高速环境
在安装环境时,建议使用国内的 conda 与 pip 源,可以加速下载。
进入 docker 之后,首先修改 conda 与 pip 的环境配置。创建或修改 ~/.condarc
,即:
vim ~/.condarc
# 添加如下信息
channels:
- defaults
show_channel_urls: true
default_channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
channel_priority: disabled
allow_conda_downgrades: true
在 docker 中,存在默认的 pip 环境,而且优先级较高,即删除 pip 配置,再修改 pip 配置,避免失效或冲突,即:
rm /opt/conda/pip.conf
rm /root/.config/pip/pip.conf
再修改配置 ~/.pip/pip.conf
,建议使用 阿里云 的 pip 源,清华源缺少部分安装包,即:
vim ~/.pip/pip.conf
# 添加如下信息
# This file has been autogenerated or modified by NVIDIA PyIndex.
# In case you need to modify your PIP configuration, please be aware that
# some configuration files may have a priority order. Here are the following
# files that may exists in your machine by order of priority:
#
# [Priority 1] Site level configuration files
# 1. `/opt/conda/pip.conf`
#
# [Priority 2] User level configuration files
# 1. `/root/.config/pip/pip.conf`
# 2. `/root/.pip/pip.conf`
#
# [Priority 3] Global level configuration files
# 1. `/etc/pip.conf`
# 2. `/etc/xdg/pip/pip.conf`
[global]
no-cache-dir = true
index-url = http://mirrors.aliyun.com/pypi/simple/
extra-index-url = https://pypi.ngc.nvidia.com
trusted-host = mirrors.aliyun.com pypi.ngc.nvidia.com
2.2 配置 Docker 环境
建议 不要 使用默认命令配置 docker 镜像,即 docker build -t openfold .
,原因是下载速度较慢,而且有部分冲突,可以参考 Dockerfile 。
手动配置如下,配置 OpenFold 系统环境,即:
# 添加 apt 源
apt-key del 7fa2af80
apt-key del 3bf863cc
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
# 安装源
apt-get update && apt-get install -y wget libxml2 cuda-minimal-build-11-3 libcusparse-dev-11-3 libcublas-dev-11-3 libcusolver-dev-11-3 git
注意:如果网速很慢,wget 需要耐心等待,建议重试几次。
配置 OpenFold 的 conda 环境 openfold
,即:
# 复制环境文件
cd openfold
# 安装环境文件
conda env update -n openfold --file environment.yml && conda clean --all
如果中断,也可以重新更新,即:
# 更新安装环境文件
conda activate openfold
conda env update --file /opt/openfold/environment.yml --prune
注意:需要时间较长,请耐心等待,当安装 pip 包出现异常时,建议手动安装。
遇到安装失败,建议手动安装,日志清晰,推荐 安装方式,即:
# 创建环境
conda create -n openfold python=3.9
# 安装 conda 包
conda install -y -c conda-forge python=3.9 setuptools=59.5.0 pip openmm=7.5.1 pdbfixer cudatoolkit==11.3.*
conda install -y -c bioconda hmmer==3.3.2 hhsuite==3.3.0 kalign2==2.04
conda install -y -c pytorch pytorch=1.12.*
# 安装 pip 包
pip install 'dllogger @ git+https://github.com/NVIDIA/dllogger.git'
pip install biopython==1.79 deepspeed==0.5.10 dm-tree==0.1.6 ml-collections==0.1.0 numpy==1.21.2 PyYAML==5.4.1 requests==2.26.0 scipy==1.7.1 tqdm==4.62.2 typing-extensions==3.10.0.2 pytorch_lightning==1.5.10 wandb==0.12.21 modelcif==0.7
# 解决 bug
conda install -c anaconda numpy-base==1.22.3 # 解决 np.object bug,同时避免与 scipy 冲突。
注意:
openmm
的 7.5.1 版本,位于simtk
中,即from simtk.openmm import app
,在sites-package
中,没有独立的文件夹。
其他关键安装包:
# jax
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
# modelcif 和 qc-procrustes
pip install modelcif==0.7 qc-procrustes
# 重新编译 openfold
pip uninstall openfold
python3 setup.py install
关于 DeepSpeed 的版本兼容问题,参考:
- GitHub - Upgrade DeepSpeed version? - deepspeed.utils.is_initialized()
- StackOverflow - TypeError: setup() got an unexpected keyword argument ‘stage’
即 deepspeed.utils.is_initialized()
的替换问题,与 setup()
的配置问题,修复文件 openfold/model/primitives.py
:
deepspeed_is_initialized = (
deepspeed_is_installed and
- deepspeed.utils.is_initialized()
+ # deepspeed.utils.is_initialized()
+ deepspeed.comm.comm.is_initialized()
)
修复文件 openfold/data/data_modules.py
,即可解决 DeepSpeed 版本兼容问题:
- def setup(self):
+ def setup(self, stage=None):
# Most of the arguments are the same for the three datasets
dataset_gen = partial(OpenFoldSingleMultimerDataset,
template_mmcif_dir=self.template_mmcif_dir,
2.3 修复文件与编译工程
下载资源 stereo_chemical_props.txt
与修复文件 simtk.openmm
,即:
cd openfold
# 注意位于 openfold/openfold/resources 中
# wget -q -P openfold/resources https://git.scicore.unibas.ch/schwede/openstructure/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt
cd openfold/resources
wget https://git.scicore.unibas.ch/schwede/openstructure/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt --no-check-certificate
# 注意 simtk.openmm 的安装位置需要选择
# conda show openmm
# import simtk
# print(simtk.__file__)
# /opt/conda/envs/openfold/lib/python3.9/site-packages/
patch -p0 -d /opt/conda/envs/openfold/lib/python3.9/site-packages/ < lib/openmm.patch
# 输出日志
patching file simtk/openmm/app/topology.py
Hunk #1 succeeded at 353 (offset -3 lines).
注意:openmm 的 7.5.1 版本需要修复一些 bug,高版本不需要,参考 关于 AlphaFold2 的 openmm.patch 补丁
编译工程,即 conda 环境中包括 openfold 的包,即
cd openfold
python3 setup.py install
2.4 相关文件
配置 conda 环境需要参考 environment.yml
文件,即:
name: openfold_venv
channels:
- conda-forge
- bioconda
- pytorch
dependencies:
- conda-forge::python=3.9
- conda-forge::setuptools=59.5.0
- conda-forge::pip
- conda-forge::openmm=7.5.1
- conda-forge::pdbfixer
- conda-forge::cudatoolkit==11.3.*
- bioconda::hmmer==3.3.2
- bioconda::hhsuite==3.3.0
- bioconda::kalign2==2.04
- pytorch::pytorch=1.12.*
- pip:
- biopython==1.79
- deepspeed==0.5.10
- dm-tree==0.1.6
- ml-collections==0.1.0
- numpy==1.21.2
- PyYAML==5.4.1
- requests==2.26.0
- scipy==1.7.1
- tqdm==4.62.2
- typing-extensions==3.10.0.2
- pytorch_lightning==1.5.10
- wandb==0.12.21
- modelcif==0.7
- git+https://github.com/NVIDIA/dllogger.git
配置环境需要参考 Dockerfile
文件,即:
FROM nvidia/cuda:11.3.1-cudnn8-runtime-ubuntu18.04
# metainformation
LABEL org.opencontainers.image.version = "1.0.0"
LABEL org.opencontainers.image.authors = "Gustaf Ahdritz"
LABEL org.opencontainers.image.source = "https://github.com/aqlaboratory/openfold"
LABEL org.opencontainers.image.licenses = "Apache License 2.0"
LABEL org.opencontainers.image.base.name="docker.io/nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04"
RUN apt-key del 7fa2af80
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
RUN apt-get update && apt-get install -y wget libxml2 cuda-minimal-build-11-3 libcusparse-dev-11-3 libcublas-dev-11-3 libcusolver-dev-11-3 git
RUN wget -P /tmp \
"https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" \
&& bash /tmp/Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda \
&& rm /tmp/Miniconda3-latest-Linux-x86_64.sh
ENV PATH /opt/conda/bin:$PATH
COPY environment.yml /opt/openfold/environment.yml
# installing into the base environment since the docker container wont do anything other than run openfold
RUN conda env update -n base --file /opt/openfold/environment.yml && conda clean --all
COPY openfold /opt/openfold/openfold
COPY scripts /opt/openfold/scripts
COPY run_pretrained_openfold.py /opt/openfold/run_pretrained_openfold.py
COPY train_openfold.py /opt/openfold/train_openfold.py
COPY setup.py /opt/openfold/setup.py
COPY lib/openmm.patch /opt/openfold/lib/openmm.patch
RUN wget -q -P /opt/openfold/openfold/resources \
https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt
RUN patch -p0 -d /opt/conda/lib/python3.9/site-packages/ < /opt/openfold/lib/openmm.patch
WORKDIR /opt/openfold
RUN python3 setup.py install
2.5 提交 Docker Image
登录 docker 服务器,即:
docker login harbor.[ip address].com
注意:如果无法登录,则需要管理员配置,或切换可登录的服务器。
设置 BOS 命令:
alias bos='bcecmd/bcecmd --conf-path bcecmd/bceconf/ bos'
提交 docker image,设置标签 (tag),以及上传 docker,即:
# 提交 Tag
docker ps -l
docker commit [container id] openfold:v1.0
# 准备远程 Tag
docker tag openfold:v1.0 openfold:v1.0
docker images | grep "openfold"
# 推送至远程
docker push openfold:v1.0
# 从远程拉取
docker pull openfold:v1.0
# 或者保存至本地
docker save openfold:v1.0 | gzip > openfold_v1_0.tar.gz
# 加载已保存的 docker image
docker image load -i openfold_v1_01.tar.gz
docker images | grep "openfold"
进入 Harbor 页面查看,发现已上传的 docker image,以及不同版本,即:
3. Bugfix
3.1 Numpy 版本不兼容
Bug 日志:
openfold/openfold/data/templates.py:88: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.
"template_domain_names": np.object,
Traceback (most recent call last):
File "openfold/run_pretrained_openfold.py", line 47, in <module>
from openfold.data import templates, feature_pipeline, data_pipeline
File "openfold/openfold/data/templates.py", line 88, in <module>
"template_domain_names": np.object,
File "/opt/conda/envs/openfold/lib/python3.9/site-packages/numpy/__init__.py", line 319, in __getattr__
raise AttributeError(__former_attrs__[attr])
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
即 Numpy 版本过高,没有 np.object
属性,建议降低至 1.23.4
版本,即:
conda list numpy
# 当前 numpy-base 的版本是 1.25.2
# conda list numpy
# packages in environment at /opt/conda/envs/openfold:
#
# Name Version Build Channel
numpy 1.21.2 pypi_0 pypi
numpy-base 1.25.2 py39hb5e798b_0 defaults
# 降低版本至 1.23.4
conda install -c anaconda numpy-base==1.22.3 # 解决 np.object bug,同时避免与 scipy 冲突。
也可以,修改源码文件 openfold/data/templates.py
与 openfold/data/data_pipeline.py
,将 np.object 替换为 object,注意,全局搜索,需要修改 2 处,即:
TEMPLATE_FEATURES = {
"template_aatype": np.int64,
"template_all_atom_mask": np.float32,
"template_all_atom_positions": np.float32,
"template_domain_names": np.object, # 需要修改
"template_sequence": np.object, # 需要修改
"template_sum_probs": np.float32,
}
Bug 参考:
- StackOverflow - module ‘numpy’ has no attribute ‘object’ closed
- 关于 scipy 与 numpy 的兼容性,参考: Toolchain Roadmap
完整的 conda 环境 yaml 文件如下:文章来源:https://www.toymoban.com/news/detail-655641.html
name: openfold
channels:
- theochem
- pytorch
- anaconda
- bioconda
- defaults
- conda-forge
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_kmp_llvm
- alabaster=0.7.13=pyhd8ed1ab_0
- babel=2.12.1=pyhd8ed1ab_1
- blas=1.0=mkl
- brotli-python=1.0.9=py39h5a03fae_9
- bzip2=1.0.8=h7f98852_4
- ca-certificates=2023.7.22=hbcca054_0
- certifi=2023.7.22=py39h06a4308_0
- colorama=0.4.6=py39h06a4308_0
- cudatoolkit=11.3.1=hb98b00a_12
- docutils=0.20.1=py39hf3d152e_0
- exceptiongroup=1.1.3=pyhd8ed1ab_0
- fftw=3.3.10=nompi_hc118613_108
- hhsuite=3.3.0=py39pl5321he10ea66_9
- hmmer=3.3.2=hdbdd923_4
- icu=72.1=hcb278e6_0
- idna=3.4=pyhd8ed1ab_0
- imagesize=1.4.1=py39h06a4308_0
- importlib-metadata=6.8.0=pyha770c72_0
- iniconfig=2.0.0=pyhd8ed1ab_0
- intel-openmp=2021.4.0=h06a4308_3561
- jax=0.3.25=py39h06a4308_0
- jaxlib=0.3.25=py39h6a678d5_1
- jinja2=3.1.2=py39h06a4308_0
- kalign2=2.04=h031d066_5
- ld_impl_linux-64=2.40=h41732ed_0
- libblas=3.9.0=12_linux64_mkl
- libcblas=3.9.0=12_linux64_mkl
- libffi=3.4.4=h6a678d5_0
- libgcc-ng=13.1.0=he5830b7_0
- libgfortran-ng=13.1.0=h69a702a_0
- libgfortran5=13.1.0=h15d22d2_0
- libhwloc=2.9.2=nocuda_h7313eea_1008
- libiconv=1.17=h166bdaf_0
- liblapack=3.9.0=12_linux64_mkl
- libnsl=2.0.0=h7f98852_0
- libsqlite=3.42.0=h2797004_0
- libstdcxx-ng=13.1.0=hfd8a6a1_0
- libuuid=2.38.1=h0b41bf4_0
- libxml2=2.11.5=h0d562d8_0
- libzlib=1.2.13=hd590300_5
- llvm-openmp=16.0.6=h4dfa4b3_0
- lz4-c=1.9.4=hcb278e6_0
- markupsafe=2.1.3=py39hd1e30aa_0
- mkl=2021.4.0=h06a4308_640
- mkl-service=2.4.0=py39h7f8727e_0
- mkl_fft=1.3.1=py39hd3c417c_0
- mkl_random=1.2.2=py39h51133e4_0
- ncurses=6.4=hcb278e6_0
- numpy-base=1.22.3=py39hf524024_0
- ocl-icd=2.3.1=h7f98852_0
- ocl-icd-system=1.0.0=1
- openmm=7.5.1=py39h71eca04_1
- openssl=3.1.2=hd590300_0
- opt_einsum=3.3.0=pyhd8ed1ab_1
- packaging=23.1=pyhd8ed1ab_0
- pdbfixer=1.7=pyhd3deb0d_0
- perl=5.32.1=4_hd590300_perl5
- pip=23.2.1=pyhd8ed1ab_0
- platformdirs=3.10.0=pyhd8ed1ab_0
- pluggy=1.2.0=pyhd8ed1ab_0
- pooch=1.7.0=pyha770c72_3
- pygments=2.16.1=pyhd8ed1ab_0
- pysocks=1.7.1=pyha2e5f31_6
- pytest=7.4.0=py39h06a4308_0
- python=3.9.17=h0755675_0_cpython
- python_abi=3.9=3_cp39
- pytorch=1.12.1=py3.9_cuda11.3_cudnn8.3.2_0
- pytorch-mutex=1.0=cuda
- pytz=2023.3=pyhd8ed1ab_0
- qc-procrustes=1.0.0=py_0
- readline=8.2=h8228510_1
- rocm-smi=5.6.0=h59595ed_1
- setuptools=59.5.0=py39hf3d152e_0
- six=1.16.0=pyhd3eb1b0_1
- snowballstemmer=2.2.0=pyhd3eb1b0_0
- sphinx=7.2.2=pyhd8ed1ab_0
- sphinxcontrib-applehelp=1.0.7=pyhd8ed1ab_0
- sphinxcontrib-devhelp=1.0.5=pyhd8ed1ab_0
- sphinxcontrib-htmlhelp=2.0.4=pyhd8ed1ab_0
- sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0
- sphinxcontrib-qthelp=1.0.6=pyhd8ed1ab_0
- sphinxcontrib-serializinghtml=1.1.8=pyhd8ed1ab_0
- tbb=2021.10.0=h00ab1b0_0
- tk=8.6.12=h27826a3_0
- tomli=2.0.1=py39h06a4308_0
- typing_extensions=4.7.1=pyha770c72_0
- wheel=0.41.1=pyhd8ed1ab_0
- xz=5.4.2=h5eee18b_0
- zipp=3.16.2=pyhd8ed1ab_0
- zlib=1.2.13=hd590300_5
- zstd=1.5.5=hc292b87_0
- pip:
- absl-py==1.4.0
- aiohttp==3.8.5
- aiosignal==1.3.1
- async-timeout==4.0.3
- attrs==23.1.0
- biopython==1.79
- bypy==1.8.2
- cachetools==5.3.1
- charset-normalizer==2.0.12
- click==8.1.6
- contextlib2==21.6.0
- deepspeed==0.5.10
- dill==0.3.7
- dllogger==1.0.0
- dm-tree==0.1.6
- docker-pycreds==0.4.0
- frozenlist==1.4.0
- fsspec==2023.6.0
- future==0.18.3
- gitdb==4.0.10
- gitpython==3.1.32
- google-auth==2.22.0
- google-auth-oauthlib==1.0.0
- grpcio==1.57.0
- hjson==3.1.0
- ihm==0.39
- lightning-utilities==0.9.0
- markdown==3.4.4
- ml-collections==0.1.0
- modelcif==0.7
- msgpack==1.0.5
- multidict==6.0.4
- multiprocess==0.70.15
- ninja==1.11.1
- numpy==1.21.2
- oauthlib==3.2.2
- openfold==1.0.1
- pandas==2.0.3
- pathtools==0.1.2
- promise==2.3
- protobuf==3.20.3
- psutil==5.9.5
- py-cpuinfo==9.0.0
- pyasn1==0.5.0
- pyasn1-modules==0.3.0
- pydeprecate==0.3.1
- python-dateutil==2.8.2
- pytorch-lightning==1.5.10
- pyyaml==5.4.1
- requests==2.26.0
- requests-oauthlib==1.3.1
- requests-toolbelt==1.0.0
- rsa==4.9
- scipy==1.7.1
- sentry-sdk==1.29.2
- setproctitle==1.3.2
- shortuuid==1.0.11
- smmap==5.0.0
- tensorboard==2.14.0
- tensorboard-data-server==0.7.1
- torchmetrics==1.0.3
- tqdm==4.62.2
- triton==1.0.0
- typing-extensions==3.10.0.2
- tzdata==2023.3
- urllib3==1.26.16
- wandb==0.12.21
- werkzeug==2.3.7
- yarl==1.9.2
prefix: /opt/conda/envs/openfold
参考
参考:文章来源地址https://www.toymoban.com/news/detail-655641.html
- ENV 设置环境变量
- StackOverflow - How to update an existing Conda environment with a .yml file
- CSDN - 配置 AlphaFold2 的高效 Tensorflow 运行环境
- CSDN - 蛋白质结构预测 ESMFold 算法的工程配置
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