graphein
v1.7.7
文檔|紙|教程|安裝
蛋白質與互動圖庫
該軟件包提供了產生蛋白質和RNA結構以及生物相互作用網絡的幾何表示的功能。我們提供與標準PYDATA格式的兼容性，以及旨在易於使用的圖形對象，可與流行的深度學習庫易用。
| 1.7.0 | foldcomp數據集 | |
| 1.7.0 | 從PDB創建數據集 | |
| 1.6.0 | 蛋白張量模塊 | |
| 1.5.0 | alphafold2創建蛋白質圖！ | |
| 1.5.0 | Dotbracket符號的RNA圖構造法 | |
| 1.4.0 | 構造分子圖 | |
| 1.3.0 | Pytorch幾何的即時數據加載器 | |
| 1.2.0 | 從蛋白質圖中提取子圖 | |
| 1.2.0 | 蛋白質圖分析 | |
| 1.2.0 | Gruphein CLI | |
| 1.2.0 | 蛋白質圖可視化！ | |
| 1.1.0 | 蛋白質 - 蛋白質相互作用網絡支持和結構相互作用組學（使用alphafold2！） | |
| 1.0.0 | 高級和低水平的API，以實現巨大的靈活性 - 創建自己的定制工作流！ |
Chaphein提供了一個程序化API和用於構造圖形的命令行界面。
Chaphein Configs可以指定為.yaml文件，以從命令行批處理過程圖。
文件
graphein -c config.yaml -p path/to/pdbs -o path/to/output| 教程（殘留級） | 教程（原子） | 文件 |
from graphein . protein . config import ProteinGraphConfig
from graphein . protein . graphs import construct_graph
config = ProteinGraphConfig ()
g = construct_graph ( config = config , pdb_code = "3eiy" )| 教程 | 文件 |
from graphein . protein . config import ProteinGraphConfig
from graphein . protein . graphs import construct_graph
from graphein . protein . utils import download_alphafold_structure
config = ProteinGraphConfig ()
fp = download_alphafold_structure ( "Q5VSL9" , aligned_score = False )
g = construct_graph ( config = config , path = fp )| 教程 | 文件 |
from graphein . protein . config import ProteinMeshConfig
from graphein . protein . meshes import create_mesh
verts , faces , aux = create_mesh ( pdb_code = "3eiy" , config = config )Chaphein可以從微笑字符串以及.sdf ， .mol2和.pdb文件中創建分子圖
| 教程 | 文件 |
from graphein . molecule . config import MoleculeGraphConfig
from graphein . molecule . graphs import construct_graph
g = create_graph ( smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" , config = config )| 教程 | 文件 |
from graphein . rna . graphs import construct_rna_graph
# Build the graph from a dotbracket & optional sequence
rna = construct_rna_graph ( dotbracket = '..(((((..(((...)))..)))))...' ,
sequence = 'UUGGAGUACACAACCUGUACACUCUUUC' )| 教程 | 文件 |
from graphein . ppi . config import PPIGraphConfig
from graphein . ppi . graphs import compute_ppi_graph
from graphein . ppi . edges import add_string_edges , add_biogrid_edges
config = PPIGraphConfig ()
protein_list = [ "CDC42" , "CDK1" , "KIF23" , "PLK1" , "RAC2" , "RACGAP1" , "RHOA" , "RHOB" ]
g = compute_ppi_graph ( config = config ,
protein_list = protein_list ,
edge_construction_funcs = [ add_string_edges , add_biogrid_edges ]
)| 教程 | 文件 |
from graphein . grn . config import GRNGraphConfig
from graphein . grn . graphs import compute_grn_graph
from graphein . grn . edges import add_regnetwork_edges , add_trrust_edges
config = GRNGraphConfig ()
gene_list = [ "AATF" , "MYC" , "USF1" , "SP1" , "TP53" , "DUSP1" ]
g = compute_grn_graph (
gene_list = gene_list ,
edge_construction_funcs = [
partial ( add_trrust_edges , trrust_filtering_funcs = config . trrust_config . filtering_functions ),
partial ( add_regnetwork_edges , regnetwork_filtering_funcs = config . regnetwork_config . filtering_functions ),
],
)最簡單的安裝是通過PIP。 nb這不安裝將轉換為數據格式的ML/DL庫以及與Pytorch 3D生成蛋白質結構網格所需的。更多細節
pip install graphein # For base install
pip install graphein[extras] # For additional featurisation dependencies
pip install graphein[dev] # For dev dependencies
pip install graphein[all] # To get the lot但是，有許多（可選的）實用程序（DSSP，Pymol，GetContacts）無法通過PYPI提供：
conda install -c salilab dssp # Required for computing secondary structural features
conda install -c schrodinger pymol # Required for PyMol visualisations & mesh generation
# GetContacts - used as an alternative way to compute intramolecular interactions
conda install -c conda-forge vmd-python
git clone https://github.com/getcontacts/getcontacts
# Add folder to PATH
echo "export PATH=$PATH:`pwd`/getcontacts" >> ~/.bashrc
source ~/.bashrc
To test the installation, run:
cd getcontacts/example/5xnd
get_dynamic_contacts.py --topology 5xnd_topology.pdb
--trajectory 5xnd_trajectory.dcd
--itypes hb
--output 5xnd_hbonds.tsv
開發環境包括集成到Chaphein中的每個深度學習庫中的GPU構建（CUDA 11.1）。
git clone https://www.github.com/a-r-j/graphein
cd graphein
conda env create -f environment-dev.yml
pip install -e .可以通過以下方式執行較輕的安裝：
git clone https://www.github.com/a-r-j/graphein
cd graphein
conda env create -f environment.yml
pip install -e .我們在本地提供兩個CPU（ docker-compose.cpu.yml ）和GPU用法（ docker-compose.yml ）的docker-compose文件。對於GPU使用率，請確保安裝了NVIDIA容器工具包。進入容器後，請確保您安裝本地安裝的音量（ pip install -e . ）。這也將在本地設置開發環境。
構建（GPU）運行：
docker-compose up -d --build # start the container
docker-compose down # stop the container
如果證明對您的工作有用，請考慮引用Chaphein。
@inproceedings { jamasb2022graphein ,
title = { Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks } ,
author = { Arian Rokkum Jamasb and Ramon Vi{~n}as Torn{'e} and Eric J Ma and Yuanqi Du and Charles Harris and Kexin Huang and Dominic Hall and Pietro Lio and Tom Leon Blundell } ,
booktitle = { Advances in Neural Information Processing Systems } ,
editor = { Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho } ,
year = { 2022 } ,
url = { https://openreview.net/forum?id=9xRZlV6GfOX }
}
