Documentation | Paper | Tutorials | Installation

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

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

Creating a Protein Graph

| | | | |---|---|---| Tutorial (Residue-level) | Tutorial (Atomic) | Docs | | | |

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

config = ProteinGraphConfig() g = constructgraph(config=config, pdbcode="3eiy") ```

Creating a Protein Graph from the AlphaFold Protein Structure Database

| | | |---|---| | Tutorial | Docs | | |

```python from graphein.protein.config import ProteinGraphConfig from graphein.protein.graphs import constructgraph from graphein.protein.utils import downloadalphafold_structure

config = ProteinGraphConfig() fp = downloadalphafoldstructure("Q5VSL9", alignedscore=False) g = constructgraph(config=config, path=fp) ```

Creating a Protein Mesh

| | | |---|---| | Tutorial | Docs | | | |

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

verts, faces, aux = createmesh(pdbcode="3eiy", config=config) ```

Creating Molecular Graphs

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

| | | |---|---| | Tutorial | Docs | | | |

```python 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 | | | |

```python from graphein.rna.graphs import constructrnagraph

Build the graph from a dotbracket & optional sequence

rna = constructrnagraph(dotbracket='..(((((..(((...)))..)))))...', sequence='UUGGAGUACACAACCUGUACACUCUUUC') ```

Creating a Protein-Protein Interaction Graph

| | | |---|---| | Tutorial | Docs | | |

```python from graphein.ppi.config import PPIGraphConfig from graphein.ppi.graphs import computeppigraph from graphein.ppi.edges import addstringedges, addbiogridedges

config = PPIGraphConfig() protein_list = ["CDC42", "CDK1", "KIF23", "PLK1", "RAC2", "RACGAP1", "RHOA", "RHOB"]

g = computeppigraph(config=config, proteinlist=proteinlist, edgeconstructionfuncs=[addstringedges, addbiogridedges] ) ```

Creating a Gene Regulatory Network Graph

| | | |---|---| |Tutorial | Docs | | |

```python from graphein.grn.config import GRNGraphConfig from graphein.grn.graphs import computegrngraph from graphein.grn.edges import addregnetworkedges, addtrrustedges

config = GRNGraphConfig() gene_list = ["AATF", "MYC", "USF1", "SP1", "TP53", "DUSP1"]

g = computegrngraph( genelist=genelist, edgeconstructionfuncs=[ partial(addtrrustedges, trrustfilteringfuncs=config.trrustconfig.filteringfunctions), partial(addregnetworkedges, regnetworkfilteringfuncs=config.regnetworkconfig.filteringfunctions), ], ) ```

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

bash 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 getdynamiccontacts.py --topology 5xndtopology.pdb \ --trajectory 5xndtrajectory.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.

bash 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:

bash 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.

```bibtex @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} }

```