anaconda3和python哪个好用_下了anaconda还要下python吗

apt-get update && apt-get -y install aria2
nohup ~/alphafold/scripts/download_pdb_mmcif.sh /root/alphafold/data/pdb_mmcif/ &  #这部分需要rsync和aria，是很多分散的文件
wget other datasets

cd ~/

git clone https://github.com/deepmind/alphafold.git
cd alphafold


pip install dm-haiku  #jax之上最流行的NN库
pip install simtk

pip install openmm
pip install absl-py==0.13.0

#下面部分为DeepMind的docker版本中在docker内安装的软件
curl -fsSL https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/7fa2af80.pub | apt-key add -
echo "deb https://mirrors.aliyun.com/nvidia-cuda/ubuntu1804/x86_64/ /" > /etc/apt/sources.list.d/cuda.list
apt update
DEBIAN_FRONTEND=noninteractive apt-get install -y build-essential cmake git hmmer kalign tzdata wget cuda-command-line-tools-10-1 
rm -rf /var/lib/apt/lists/*

git clone --branch v3.3.0 http://github.com/soedinglab/hh-suite.git /tmp/hh-suite
mkdir /tmp/hh-suite/build && cd /tmp/hh-suite/build
cmake -DCMAKE_INSTALL_PREFIX=/opt/hhsuite ..
make -j 4 && make install
ln -s /opt/hhsuite/bin/* /usr/bin
#rm -rf /tmp/hh-suite

wget -q -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


#注意一定要使用Python3.8，后面openmm的patch也是针对该版本，这是个坑
#DeepMind的在Ubuntu20+CUDA11.0，使用Ubuntu18.04+CUDA10.1也可以，需要注意修改脚本中所有10.1、101，10-1的地方。
export PATH="/opt/conda/bin:$PATH"
conda update -qy conda
conda install -y -c conda-forge openmm=7.5.1 cudatoolkit==10.1.243 pdbfixer pip

wget -q -P ~/alphafold/alphafold/common/ https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt

pip3 install --upgrade pip
pip3 install -r ~/alphafold/requirements.txt
pip3 install --upgrade jax jaxlib==0.1.69+cuda101 -f  https://storage.googleapis.com/jax-releases/jax_releases.html

cd /opt/conda/lib/python3.8/site-packages
patch -p0 < ~/alphafold/docker/openmm.patch
cd ~/alphafold
wget https://storage.googleapis.com/alphafold/alphafold_params_2021-07-14.tar && mkdir && tar params -xvf alphafold_params_2021-07-14.tar -C params

wget -o /dev/null -O T1050.fasta 'https://www.predictioncenter.org/casp14/target.cgi?target=T1050&view=sequence'

export DATA_DIR='/root/alphafold/data/' && export OUTPUT_DIR='/root/alphafold/output/' 

python run_alphafold.py --fasta_paths=${DATA_DIR}/fasta_path_0/T1050.fasta --uniref90_database_path=${DATA_DIR}/uniref90_database_path/uniref90.fasta --mgnify_database_path=${DATA_DIR}/mgnify_database_path/mgy_clusters.fa --uniclust30_database_path=${DATA_DIR}/uniclust30_database_path/uniclust30_2018_08 --bfd_database_path=${DATA_DIR}/bfd_database_path/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt --pdb70_database_path=${DATA_DIR}/pdb70_database_path/pdb70 --data_dir=${DATA_DIR}/data_dir/data --template_mmcif_dir=${DATA_DIR}/template_mmcif_dir/mmcif_files --obsolete_pdbs_path=${DATA_DIR}/obsolete_pdbs_path/obsolete.dat --output_dir=${OUTPUT_DIR}/output --model_names=model_1,model_2,model_3,model_4,model_5 --max_template_date=2020-05-14 --preset=full_dbs --benchmark=False --logtostderr

###pip  
#dgl:  https://www.dgl.ai/pages/start.html
cat /usr/local/cuda/version.txt
python --version
pip install dgl-cu101

pip install torch_geometric torch_sparse torch_scatter

#hh-suite
wget https://github.com/soedinglab/hh-suite/releases/download/v3.2.0/hhsuite-3.2.0-AVX2-Linux.tar.gz --no-check-certificate
tar xvfz hhsuite-3.2.0-AVX2-Linux.tar.gz
mkdir hhsuite
mv bin hhsuite
mv data hhsuite
mv scripts hhsuite
mv LICENSE hhsuite
mv README hhsuite
# Backer版本，使用hhsuite3.2.0不会crash

#nibi-blash (legacy)
wget ftp://ftp.ncbi.nih.gov/blast/executables/legacy.NOTSUPPORTED/2.2.26/blast-2.2.26-x64-linux.tar.gz
tar -xzvf blast-2.2.26-x64-linux.tar.gz

#gcc5.4/g++5.4
apt install gcc-5 g++-5
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 40
update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 40

#git clone https://github.com/sparsehash/sparsehash
apt-get install libsparsehash-dev  

### official: http://wwwuser.gwdg.de/~compbiol/data/csblast/releases/csblast-2.2.3_${platform}.tar.gz
git clone https://github.com/soedinglab/csblast
cd csblast/src
make csblast
make csbuild
make csclust
make cssgd
make cstrainset
make cstranslate

wget https://files.ipd.uw.edu/pub/RoseTTAFold/weights.tar.gz
tar xfz weights.tar.gz

git clone https://github.com/RosettaCommons/RoseTTAFold
cd RoseTTAFold

###modify script，主要是环境配置的修改
修改run_e2e_ver.sh脚本
#set -e
#mkdir -p $WDIR/log
#conda activate RoseTTAFold
#Backer版本，有端到端版本，还有多步的版本；对步的版本，会先预测出多组候选距离与朝向，然后做精化，最终评估选择出最好的结果
修改run_pyrosetta_ver.sh脚本

#set -e
#mkdir -p $WDIR/log
#conda activate RoseTTAFold
#conda deactivate
#conda activate folding

修改input_prep/make_msa.sh
#-d $MYDB    #just use one db for test

修改input_prep/make_ss.sh中的修改相关路径，参考如下：
DATADIR="/root/data/psipred/soft/psipred/data"
/root/data/psipred/soft/blast-2.2.26/bin/makemat -P $ID

cd <workspace>
##/root/RoseTTAFold/bfd/
ln -s /root/data/hh/data/UniRef30_2020_06/ .
ln -s /root/data/baker/pdb100_2021Mar03/ .

ln -s /root/data/psipred/soft/csblast/ .
mv https://zhuanlan.zhihu.com/p/csblast https://zhuanlan.zhihu.com/p/csblast-2.2.3

ln -s /root/data/baker/weights/ .

#端到端预测，效果略差
run_e2e_ver.sh

#pipeline多步预测
run_pyrosetta_ver.sh