The graphein from anton-bushuiev

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Protein & Interactomic Graph Library

This package provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks. We provide compatibility with standard PyData formats, as well as graph objects designed for ease of use with popular deep learning libraries.

What's New?


1.7.0	FoldComp Datasets
1.7.0	Creating Datasets from the PDB
1.6.0	Protein Tensor Module
1.5.0	Protein Graph Creation from AlphaFold2!
1.5.0	RNA Graph Construction from Dotbracket notation
1.4.0	Constructing molecular graphs
1.3.0	Ready-to-go Dataloaders for PyTorch Geometric
1.2.0	Extracting subgraphs from protein graphs
1.2.0	Protein Graph Analytics
1.2.0	Graphein CLI
1.2.0	Protein Graph Visualisation!
1.1.0	Protein - Protein Interaction Network Support & Structural Interactomics (Using AlphaFold2!)
1.0.0	High and Low-level API for massive flexibility - create your own bespoke workflows!

Example usage

Graphein provides both a programmatic API and a command-line interface for constructing graphs.

CLI

Graphein configs can be specified as .yaml files to batch process graphs from the commandline.

Docs

graphein -c config.yaml -p path/to/pdbs -o path/to/output

Creating a Protein Graph


Tutorial (Residue-level)	Tutorial (Atomic)	Docs

from graphein.protein.config import ProteinGraphConfig
from graphein.protein.graphs import construct_graph

config = ProteinGraphConfig()
g = construct_graph(config=config, pdb_code="3eiy")

Creating a Protein Graph from the AlphaFold Protein Structure Database


Tutorial	Docs

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)

Creating a Protein Mesh


Tutorial	Docs

from graphein.protein.config import ProteinMeshConfig
from graphein.protein.meshes import create_mesh

verts, faces, aux = create_mesh(pdb_code="3eiy", config=config)

Creating Molecular Graphs

Graphein can create molecular graphs from smiles strings as well as .sdf, .mol2, and .pdb files


Tutorial	Docs

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)

Creating an RNA Graph


Tutorial	Docs

from graphein.rna.graphs import construct_rna_graph
# Build the graph from a dotbracket & optional sequence
rna = construct_rna_graph(dotbracket='..(((((..(((...)))..)))))...',
                          sequence='UUGGAGUACACAACCUGUACACUCUUUC')

Creating a Protein-Protein Interaction Graph


Tutorial	Docs

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]
                     )

Creating a Gene Regulatory Network Graph


Tutorial	Docs

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),
    ],
)

Installation

Pip

The simplest install is via pip. N.B this does not install ML/DL libraries which are required for conversion to their data formats and for generating protein structure meshes with PyTorch 3D. Further details

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

However, there are a number of (optional) utilities (DSSP, PyMol, GetContacts) that are not available via PyPI:

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

Conda environment

The dev environment includes GPU Builds (CUDA 11.1) for each of the deep learning libraries integrated into graphein.

git clone https://www.github.com/a-r-j/graphein
cd graphein
conda env create -f environment-dev.yml
pip install -e .

A lighter install can be performed with:

git clone https://www.github.com/a-r-j/graphein
cd graphein
conda env create -f environment.yml
pip install -e .

Dockerfile

We provide two docker-compose files for CPU (docker-compose.cpu.yml) and GPU usage (docker-compose.yml) locally. For GPU usage please ensure that you have NVIDIA Container Toolkit installed. Ensure that you install the locally mounted volume after entering the container (pip install -e .). This will also setup the dev environment locally.

To build (GPU) run:

docker-compose up -d --build # start the container
docker-compose down # stop the container

Citing Graphein

Please consider citing graphein if it proves useful in your work.

@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}
}

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