graphein: A Jupyter Notebook repository from Seanny123

Protein Graph Library

This package provides functionality for producing a number of types of graph-based representations of proteins. We provide compatibility with standard formats, as well as graph objects designed for ease of use with popular deep learning libraries.

What's New?

Protein Graph Visualisation!
RNA Graph Construction from Dotbracket notation

Example usage

Creating a Protein Graph

from graphein.construct_graphs import  ProteinGraph

# Initialise ProteinGraph class
pg = ProteinGraph(granularity='CA', insertions=False, keep_hets=True,
                  node_featuriser='meiler', get_contacts_path='/Users/arianjamasb/github/getcontacts',
                  pdb_dir='examples/pdbs/',
                  contacts_dir='examples/contacts/',
                  exclude_waters=True, covalent_bonds=False, include_ss=True)

# Create residue-level graphs. Chain selection is either 'all' or a list e.g. ['A', 'B', 'D'] specifying the polypeptide chains to capture

# DGLGraph From PDB Accession Number
graph = pg.dgl_graph_from_pdb_code('3eiy', chain_selection='all')
# DGLGraph From PDB file
graph = pg.dgl_graph_from_pdb_file(file_path='examples/pdbs/pdb3eiy.pdb', contact_file='examples/contacts/3eiy_contacts.tsv', chain_selection='all')

# Create atom-level graphs
graph = pg._make_atom_graph(pdb_code='3eiy', graph_type='bigraph')

Creating a Protein Mesh

from graphein.construct_meshes import  ProteinMesh
# Initialise ProteinMesh class
pm = ProteinMesh()

# Pytorch3D Mesh Object from PDB Code
verts, faces, aux = pm.create_mesh(pdb_code='3eiy', out_dir='examples/meshes/')
# Pytorch3D Mesh Object from PDB File
verts, faces, aux = pm.create_mesh(pdb_file='examples/pdbs/pdb3eiy.pdb')

Creating an RNA Graph

from graphein.construct_graphs import RNAGraph
# Initialise RNAGraph Constructor
rg = RNAGraph()
# Build the graph from a dotbracket & optional sequence
rna = rg.dgl_graph_from_dotbracket('..(((((..(((...)))..)))))...', sequence='UUGGAGUACACAACCUGUACACUCUUUC')

Parameters

Graphs can be constructed according to walks through the graph in the figure below.

granularity: {'CA', 'CB', 'atom'} - specifies node-level granularity of graph
insertions: bool - keep atoms with multiple insertion positions
keep_hets: bool - keep hetatoms
node_featuriser: {'meiler', 'kidera'} low-dimensional embeddings of AA physico-chemical properties
pdb_dir: path to pdb files
contacts_dir: path to contact files generated by get_contacts
get_contacts_path: path to GetContacts installation
exclude_waters: bool - retain structural waters
covalent_bonds: bool - maintain covalent bond edges or just use intramolecular interactions
include_ss: bool - calculate protein SS and surface features using DSSP and assign them as node features

Installation

Create env:

conda create --name graphein python=3.7
conda activate graphein

Install GetContacts

Installation Instructions

MacOS

 # Install get_contact_ticc.py dependencies
 $ conda install scipy numpy scikit-learn matplotlib pandas cython seaborn
 $ pip install ticc==0.1.4
  
 # Install vmd-python dependencies
 $ conda install netcdf4 numpy pandas seaborn expat tk=8.5  # Alternatively use pip
 $ brew install netcdf pyqt # Assumes https://brew.sh/ is installed

 # Install vmd-python library
 $ conda install -c conda-forge vmd-python

 # Set up getcontacts library
 $ git clone https://github.com/getcontacts/getcontacts.git
 $ echo "export PATH=`pwd`/getcontacts:\$PATH" >> ~/.bash_profile
 $ source ~/.bash_profile

 # Test installation
 $ cd getcontacts/example/5xnd
 $ get_dynamic_contacts.py --topology 5xnd_topology.pdb \
                           --trajectory 5xnd_trajectory.dcd \
                           --itypes hb \
                           --output 5xnd_hbonds.tsv

Linux

   
  # Make sure you have git and conda installed and then run

  # Install get_contact_ticc.py dependencies
  conda install scipy numpy scikit-learn matplotlib pandas cython
  pip install ticc==0.1.4
  
  # Set up vmd-python library
  conda install -c https://conda.anaconda.org/rbetz vmd-python
  
  # Set up getcontacts library
  git clone https://github.com/getcontacts/getcontacts.git
  echo "export PATH=`pwd`/getcontacts:\$PATH" >> ~/.bashrc
  source ~/.bashrc

Install Biopython & RDKit:

N.B. DGLLife requires rdkit==2018.09.3

conda install biopython
conda install -c conda-forge rdkit==2018.09.3

Install DSSP:

We use DSSP for computing some protein features
```
$ conda install -c salilab dssp
```

Install PyTorch, DGL and DGL LifeSci:

N.B. Make sure to install appropriate version for your CUDA version

# Install PyTorch: MacOS
$ conda install pytorch torchvision -c pytorch                      # Only CPU Build

# Install PyTorch: Linux
$ conda install pytorch torchvision cpuonly -c pytorch              # For CPU Build
$ conda install pytorch torchvision cudatoolkit=9.2 -c pytorch      # For CUDA 9.2 Build
$ conda install pytorch torchvision cudatoolkit=10.1 -c pytorch     # For CUDA 10.1 Build
$ conda install pytorch torchvision cudatoolkit=10.2 -c pytorch     # For CUDA 10.2 Build

# Install DGL. N.B. We require 0.4.3 until compatibility with DGL 0.5.0+ is implemented
$ pip install dgl==0.4.3

# Install DGL LifeSci
$ conda install -c dglteam dgllife

Install PyTorch Geometric:

$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-${TORCH}.html
$ pip install torch-geometric

Where ${CUDA} and ${TORCH} should be replaced by your specific CUDA version (cpu, cu92, cu101, cu102) and PyTorch version (1.4.0, 1.5.0, 1.6.0), respectively

N.B. Follow the instructions in the Torch-Geometric Docs to install the versions appropriate to your CUDA version.

Install PyMol and IPyMol
```
$ conda install -c schrodinger pymol
$ git clone https://github.com/cxhernandez/ipymol
$ cd ipymol
$ pip install . 
```
N.B. The PyPi package seems to be behind the github repo. We require functionality that is not present in the PyPi package in order to construct meshes.

Install graphein:

$ git clone https://www.github.com/a-r-j/graphein
$ cd graphein
$ pip install -e .

Citing Graphein

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

@article{Jamasb2020,
  doi = {10.1101/2020.07.15.204701},
  url = {https://doi.org/10.1101/2020.07.15.204701},
  year = {2020},
  month = jul,
  publisher = {Cold Spring Harbor Laboratory},
  author = {Arian Rokkum Jamasb and Pietro Lio and Tom Blundell},
  title = {Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures}
}

Seanny123/graphein