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<h1>Welcome to Graphein’s documentation!<a class="headerlink" href="#welcome-to-graphein-s-documentation" title="Permalink to this headline">#</a></h1>
<p>This package provides functionality for producing a number of types of graph-based representations of proteins. We provide compatibility with standard geometric deep learning library formats (currently: NetworkX nx.Graph, pytorch_geometric.data.Data and dgl.DGLGraph), as well as graph objects designed for ease of use with popular deep learning libraries.</p>
<p>The repository can be found at <a class="reference external" href="https://www.github.com/a-r-j/graphein">a-r-j/graphein</a></p>
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<p class="admonition-title">Note</p>
<p>This is an early-stage project and a lot more documentation and functionality is planned to be included. If you are a structural biologist or machine learning researcher in computational biology, my inbox is always open for suggestions and assistance!</p>
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<p><span class="raw-html-m2r"><a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"/></a></span></p>
<p align="center">
<a href="https://www.graphein.ai/">
<img src="https://github.com/a-r-j/graphein/blob/master/imgs/graphein.png?raw=true" width="100%">
</img></a>
</p><p><span class="raw-html-m2r"><br/></span></p>
<p><a class="reference external" href="http://www.graphein.ai">Documentation</a> | <a class="reference external" href="https://www.biorxiv.org/content/10.1101/2020.07.15.204701v1">Paper</a> | <a class="reference external" href="http://graphein.ai/notebooks_index.html">Tutorials</a> | <a class="reference external" href="#installation">Installation</a></p>
<p>Protein & Interactomic Graph Library</p>
<p>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.</p>
<div class="section" id="what-s-new">
<h2>What’s New?<a class="headerlink" href="#what-s-new" title="Permalink to this headline">#</a></h2>
</div>
<div class="section" id="example-usage">
<h2>Example usage<a class="headerlink" href="#example-usage" title="Permalink to this headline">#</a></h2>
<p>Graphein provides both a programmatic API and a command-line interface for constructing graphs.</p>
<div class="section" id="cli">
<h3>CLI<a class="headerlink" href="#cli" title="Permalink to this headline">#</a></h3>
<p>Graphein configs can be specified as <code class="docutils literal notranslate"><span class="pre">.yaml</span></code> files to batch process graphs from the commandline.</p>
<p><a class="reference external" href="http://graphein.ai/getting_started/usage.html">Docs</a></p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>graphein -c config.yaml -p path/to/pdbs -o path/to/output
</pre></div>
</div>
</div>
<div class="section" id="creating-a-protein-graph">
<h3>Creating a Protein Graph<a class="headerlink" href="#creating-a-protein-graph" title="Permalink to this headline">#</a></h3>
<p><a class="reference external" href="http://graphein.ai/notebooks/residue_graphs.html">Tutorial (Residue-level)</a> | <a class="reference external" href="http://graphein.ai/notebooks/atom_graph_tutorial.html">Tutorial (Atomic)</a> | <a class="reference external" href="http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs">Docs</a>
|
.. image:: <a class="reference external" href="https://colab.research.google.com/assets/colab-badge.svg">https://colab.research.google.com/assets/colab-badge.svg</a></p>
<blockquote>
<div><blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">target</dt>
<dd class="field-odd"><p><a class="reference external" href="https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb">https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb</a></p>
</dd>
<dt class="field-even">alt</dt>
<dd class="field-even"><p>Open In Colab</p>
</dd>
</dl>
</div></blockquote>
<div class="line-block">
<div class="line"><br/></div>
</div>
</div></blockquote>
<img alt="https://colab.research.google.com/assets/colab-badge.svg:target:https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/atom_graph_tutorial.ipynb:alt:OpenInColab(https://colab.research.google.com/assets/colab-badge.svg)||" src="https://colab.research.google.com/assets/colab-badge.svg:target:https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/atom_graph_tutorial.ipynb:alt:OpenInColab(https://colab.research.google.com/assets/colab-badge.svg)||"/>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.protein.config</span> <span class="kn">import</span> <span class="n">ProteinGraphConfig</span>
<span class="kn">from</span> <span class="nn">graphein.protein.graphs</span> <span class="kn">import</span> <span class="n">construct_graph</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">ProteinGraphConfig</span><span class="p">()</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">construct_graph</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">pdb_code</span><span class="o">=</span><span class="s2">"3eiy"</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-a-protein-graph-from-the-alphafold-protein-structure-database">
<h3>Creating a Protein Graph from the AlphaFold Protein Structure Database<a class="headerlink" href="#creating-a-protein-graph-from-the-alphafold-protein-structure-database" title="Permalink to this headline">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.protein.config</span> <span class="kn">import</span> <span class="n">ProteinGraphConfig</span>
<span class="kn">from</span> <span class="nn">graphein.protein.graphs</span> <span class="kn">import</span> <span class="n">construct_graph</span>
<span class="kn">from</span> <span class="nn">graphein.protein.utils</span> <span class="kn">import</span> <span class="n">download_alphafold_structure</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">ProteinGraphConfig</span><span class="p">()</span>
<span class="n">fp</span> <span class="o">=</span> <span class="n">download_alphafold_structure</span><span class="p">(</span><span class="s2">"Q5VSL9"</span><span class="p">,</span> <span class="n">aligned_score</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">construct_graph</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> <span class="n">pdb_path</span><span class="o">=</span><span class="n">fp</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-a-protein-mesh">
<h3>Creating a Protein Mesh<a class="headerlink" href="#creating-a-protein-mesh" title="Permalink to this headline">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.protein.config</span> <span class="kn">import</span> <span class="n">ProteinMeshConfig</span>
<span class="kn">from</span> <span class="nn">graphein.protein.meshes</span> <span class="kn">import</span> <span class="n">create_mesh</span>
<span class="n">verts</span><span class="p">,</span> <span class="n">faces</span><span class="p">,</span> <span class="n">aux</span> <span class="o">=</span> <span class="n">create_mesh</span><span class="p">(</span><span class="n">pdb_code</span><span class="o">=</span><span class="s2">"3eiy"</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-molecular-graphs">
<h3>Creating Molecular Graphs<a class="headerlink" href="#creating-molecular-graphs" title="Permalink to this headline">#</a></h3>
<p>Graphein can create molecular graphs from smiles strings as well as <code class="docutils literal notranslate"><span class="pre">.sdf</span></code>, <code class="docutils literal notranslate"><span class="pre">.mol2</span></code>, and <code class="docutils literal notranslate"><span class="pre">.pdb</span></code> files</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.molecule.config</span> <span class="kn">import</span> <span class="n">MoleculeGraphConfig</span>
<span class="kn">from</span> <span class="nn">graphein.molecule.graphs</span> <span class="kn">import</span> <span class="n">construct_graph</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">create_graph</span><span class="p">(</span><span class="n">smiles</span><span class="o">=</span><span class="s2">"CC(=O)OC1=CC=CC=C1C(=O)O"</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-an-rna-graph">
<h3>Creating an RNA Graph<a class="headerlink" href="#creating-an-rna-graph" title="Permalink to this headline">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.rna.graphs</span> <span class="kn">import</span> <span class="n">construct_rna_graph</span>
<span class="c1"># Build the graph from a dotbracket & optional sequence</span>
<span class="n">rna</span> <span class="o">=</span> <span class="n">construct_rna_graph</span><span class="p">(</span><span class="n">dotbracket</span><span class="o">=</span><span class="s1">'..(((((..(((...)))..)))))...'</span><span class="p">,</span>
<span class="n">sequence</span><span class="o">=</span><span class="s1">'UUGGAGUACACAACCUGUACACUCUUUC'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-a-protein-protein-interaction-graph">
<h3>Creating a Protein-Protein Interaction Graph<a class="headerlink" href="#creating-a-protein-protein-interaction-graph" title="Permalink to this headline">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.ppi.config</span> <span class="kn">import</span> <span class="n">PPIGraphConfig</span>
<span class="kn">from</span> <span class="nn">graphein.ppi.graphs</span> <span class="kn">import</span> <span class="n">compute_ppi_graph</span>
<span class="kn">from</span> <span class="nn">graphein.ppi.edges</span> <span class="kn">import</span> <span class="n">add_string_edges</span><span class="p">,</span> <span class="n">add_biogrid_edges</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">PPIGraphConfig</span><span class="p">()</span>
<span class="n">protein_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CDC42"</span><span class="p">,</span> <span class="s2">"CDK1"</span><span class="p">,</span> <span class="s2">"KIF23"</span><span class="p">,</span> <span class="s2">"PLK1"</span><span class="p">,</span> <span class="s2">"RAC2"</span><span class="p">,</span> <span class="s2">"RACGAP1"</span><span class="p">,</span> <span class="s2">"RHOA"</span><span class="p">,</span> <span class="s2">"RHOB"</span><span class="p">]</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">compute_ppi_graph</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
<span class="n">protein_list</span><span class="o">=</span><span class="n">protein_list</span><span class="p">,</span>
<span class="n">edge_construction_funcs</span><span class="o">=</span><span class="p">[</span><span class="n">add_string_edges</span><span class="p">,</span> <span class="n">add_biogrid_edges</span><span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-a-gene-regulatory-network-graph">
<h3>Creating a Gene Regulatory Network Graph<a class="headerlink" href="#creating-a-gene-regulatory-network-graph" title="Permalink to this headline">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphein.grn.config</span> <span class="kn">import</span> <span class="n">GRNGraphConfig</span>
<span class="kn">from</span> <span class="nn">graphein.grn.graphs</span> <span class="kn">import</span> <span class="n">compute_grn_graph</span>
<span class="kn">from</span> <span class="nn">graphein.grn.edges</span> <span class="kn">import</span> <span class="n">add_regnetwork_edges</span><span class="p">,</span> <span class="n">add_trrust_edges</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">GRNGraphConfig</span><span class="p">()</span>
<span class="n">gene_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"AATF"</span><span class="p">,</span> <span class="s2">"MYC"</span><span class="p">,</span> <span class="s2">"USF1"</span><span class="p">,</span> <span class="s2">"SP1"</span><span class="p">,</span> <span class="s2">"TP53"</span><span class="p">,</span> <span class="s2">"DUSP1"</span><span class="p">]</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">compute_grn_graph</span><span class="p">(</span>
<span class="n">gene_list</span><span class="o">=</span><span class="n">gene_list</span><span class="p">,</span>
<span class="n">edge_construction_funcs</span><span class="o">=</span><span class="p">[</span>
<span class="n">partial</span><span class="p">(</span><span class="n">add_trrust_edges</span><span class="p">,</span> <span class="n">trrust_filtering_funcs</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">trrust_config</span><span class="o">.</span><span class="n">filtering_functions</span><span class="p">),</span>
<span class="n">partial</span><span class="p">(</span><span class="n">add_regnetwork_edges</span><span class="p">,</span> <span class="n">regnetwork_filtering_funcs</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">regnetwork_config</span><span class="o">.</span><span class="n">filtering_functions</span><span class="p">),</span>
<span class="p">],</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id13">
<h2>Installation<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
<div class="section" id="pip">
<h3>Pip<a class="headerlink" href="#pip" title="Permalink to this headline">#</a></h3>
<p>The simplest install is via pip. <em>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.</em> <a class="reference external" href="http://graphein.ai//getting_started/installation.html">Further details</a></p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install graphein <span class="c1"># For base install</span>
pip install graphein<span class="o">[</span>extras<span class="o">]</span> <span class="c1"># For additional featurisation dependencies</span>
pip install graphein<span class="o">[</span>dev<span class="o">]</span> <span class="c1"># For dev dependencies</span>
pip install graphein<span class="o">[</span>all<span class="o">]</span> <span class="c1"># To get the lot</span>
</pre></div>
</div>
<p>However, there are a number of (optional) utilities (<a class="reference external" href="https://anaconda.org/salilab/dssp">DSSP</a>, <a class="reference external" href="https://pymol.org/2/">PyMol</a>, <a class="reference external" href="https://getcontacts.github.io/">GetContacts</a>) that are not available via PyPI:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">salilab</span> <span class="n">dssp</span> <span class="c1"># Required for computing secondary structural features</span>
<span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">schrodinger</span> <span class="n">pymol</span> <span class="c1"># Required for PyMol visualisations & mesh generation</span>
<span class="c1"># GetContacts - used as an alternative way to compute intramolecular interactions</span>
<span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">vmd</span><span class="o">-</span><span class="n">python</span>
<span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">getcontacts</span><span class="o">/</span><span class="n">getcontacts</span>
<span class="c1"># Add folder to PATH</span>
<span class="n">echo</span> <span class="s2">"export PATH=\$PATH:`pwd`/getcontacts"</span> <span class="o">>></span> <span class="o">~/.</span><span class="n">bashrc</span>
<span class="n">source</span> <span class="o">~/.</span><span class="n">bashrc</span>
<span class="n">To</span> <span class="n">test</span> <span class="n">the</span> <span class="n">installation</span><span class="p">,</span> <span class="n">run</span><span class="p">:</span>
<span class="n">cd</span> <span class="n">getcontacts</span><span class="o">/</span><span class="n">example</span><span class="o">/</span><span class="mi">5</span><span class="n">xnd</span>
<span class="n">get_dynamic_contacts</span><span class="o">.</span><span class="n">py</span> <span class="o">--</span><span class="n">topology</span> <span class="mi">5</span><span class="n">xnd_topology</span><span class="o">.</span><span class="n">pdb</span> \
<span class="o">--</span><span class="n">trajectory</span> <span class="mi">5</span><span class="n">xnd_trajectory</span><span class="o">.</span><span class="n">dcd</span> \
<span class="o">--</span><span class="n">itypes</span> <span class="n">hb</span> \
<span class="o">--</span><span class="n">output</span> <span class="mi">5</span><span class="n">xnd_hbonds</span><span class="o">.</span><span class="n">tsv</span>
</pre></div>
</div>
</div>
<div class="section" id="conda-environment">
<h3>Conda environment<a class="headerlink" href="#conda-environment" title="Permalink to this headline">#</a></h3>
<p>The dev environment includes GPU Builds (CUDA 11.1) for each of the deep learning libraries integrated into graphein.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://www.github.com/a-r-j/graphein
<span class="nb">cd</span> graphein
conda env create -f environment-dev.yml
pip install -e .
</pre></div>
</div>
<p>A lighter install can be performed with:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://www.github.com/a-r-j/graphein
<span class="nb">cd</span> graphein
conda env create -f environment.yml
pip install -e .
</pre></div>
</div>
</div>
<div class="section" id="dockerfile">
<h3>Dockerfile<a class="headerlink" href="#dockerfile" title="Permalink to this headline">#</a></h3>
<p>We provide two <code class="docutils literal notranslate"><span class="pre">docker-compose</span></code> files for CPU (<code class="docutils literal notranslate"><span class="pre">docker-compose.cpu.yml</span></code>) and GPU usage (<code class="docutils literal notranslate"><span class="pre">docker-compose.yml</span></code>) locally. For GPU usage please ensure that you have <a class="reference external" href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html">NVIDIA Container Toolkit</a> installed. Ensure that you install the locally mounted volume after entering the container (<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">-e</span> <span class="pre">.</span></code>). This will also setup the dev environment locally.</p>
<p>To build (GPU) run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">docker</span><span class="o">-</span><span class="n">compose</span> <span class="n">up</span> <span class="o">-</span><span class="n">d</span> <span class="o">--</span><span class="n">build</span> <span class="c1"># start the container</span>
<span class="n">docker</span><span class="o">-</span><span class="n">compose</span> <span class="n">down</span> <span class="c1"># stop the container</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="citing-graphein">
<h2>Citing Graphein<a class="headerlink" href="#citing-graphein" title="Permalink to this headline">#</a></h2>
<p>Please consider citing graphein if it proves useful in your work.</p>
<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span> <span class="p">{</span><span class="nl">Jamasb2020.07.15.204701</span><span class="p">,</span>
<span class="na">author</span> <span class="p">=</span> <span class="s">{Jamasb, Arian R. and Vi{\~n}as, Ramon and Ma, Eric J. and Harris, Charlie and Huang, Kexin and Hall, Dominic and Li{\'o}, Pietro and Blundell, Tom L.}</span><span class="p">,</span>
<span class="na">title</span> <span class="p">=</span> <span class="s">{Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks}</span><span class="p">,</span>
<span class="na">elocation-id</span> <span class="p">=</span> <span class="s">{2020.07.15.204701}</span><span class="p">,</span>
<span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span>
<span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1101/2020.07.15.204701}</span><span class="p">,</span>
<span class="na">publisher</span> <span class="p">=</span> <span class="s">{Cold Spring Harbor Laboratory}</span><span class="p">,</span>
<span class="na">abstract</span> <span class="p">=</span> <span class="s">{Geometric deep learning has well-motivated applications in the context of biology, a domain where relational structure in datasets can be meaningfully leveraged. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. However, there has been little exploration of how to best to integrate and construct geometric representations of these datatypes. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of protein structures and biological interaction networks for computational analysis. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the recently-released AlphaFold Structure Database, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery.Availability and implementation Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: graphein.aiCompeting Interest StatementThe authors have declared no competing interest.}</span><span class="p">,</span>
<span class="na">URL</span> <span class="p">=</span> <span class="s">{https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701}</span><span class="p">,</span>
<span class="na">eprint</span> <span class="p">=</span> <span class="s">{https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701.full.pdf}</span><span class="p">,</span>
<span class="na">journal</span> <span class="p">=</span> <span class="s">{bioRxiv}</span>
<span class="p">}</span>
</pre></div>
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<div class="toctree-wrapper compound">
</div>
<div class="toctree-wrapper compound">
</div>
<div class="toctree-wrapper compound">
</div>
<div class="toctree-wrapper compound">
</div>
<p>If Graphein proves useful to your work, please consider citing:</p>
<div class="highlight-latex notranslate"><div class="highlight"><pre><span></span>@article <span class="nb">{</span>Jamasb2020.07.15.204701,
author = <span class="nb">{</span>Jamasb, Arian R. and Vi<span class="nb">{</span><span class="k">\~</span>n<span class="nb">}</span>as, Ramon and Ma, Eric J. and Harris, Charlie and Huang, Kexin and Hall, Dominic and Li<span class="nb">{</span><span class="k">\'</span>o<span class="nb">}</span>, Pietro and Blundell, Tom L.<span class="nb">}</span>,
title = <span class="nb">{</span>Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks<span class="nb">}</span>,
elocation-id = <span class="nb">{</span>2020.07.15.204701<span class="nb">}</span>,
year = <span class="nb">{</span>2021<span class="nb">}</span>,
doi = <span class="nb">{</span>10.1101/2020.07.15.204701<span class="nb">}</span>,
publisher = <span class="nb">{</span>Cold Spring Harbor Laboratory<span class="nb">}</span>,
abstract = <span class="nb">{</span>Geometric deep learning has well-motivated applications in the context of biology, a domain where relational structure in datasets can be meaningfully leveraged. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. However, there has been little exploration of how to best to integrate and construct geometric representations of these datatypes. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of protein structures and biological interaction networks for computational analysis. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the recently-released AlphaFold Structure Database, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery.Availability and implementation Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: graphein.aiCompeting Interest StatementThe authors have declared no competing interest.<span class="nb">}</span>,
URL = <span class="nb">{</span>https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701<span class="nb">}</span>,
eprint = <span class="nb">{</span>https://www.biorxiv.org/content/early/2021/10/12/2020.07.15.204701.full.pdf<span class="nb">}</span>,
journal = <span class="nb">{</span>bioRxiv<span class="nb">}</span>
<span class="nb">}</span>
</pre></div>
</div>
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<div class="section" id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">#</a></h1>
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<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
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Contents
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<div class="toc-tree">
<ul>
<li><a class="reference internal" href="#">Welcome to Graphein’s documentation!</a><ul>
<li><a class="reference internal" href="#what-s-new">What’s New?</a></li>
<li><a class="reference internal" href="#example-usage">Example usage</a><ul>
<li><a class="reference internal" href="#cli">CLI</a></li>
<li><a class="reference internal" href="#creating-a-protein-graph">Creating a Protein Graph</a></li>
<li><a class="reference internal" href="#creating-a-protein-graph-from-the-alphafold-protein-structure-database">Creating a Protein Graph from the AlphaFold Protein Structure Database</a></li>
<li><a class="reference internal" href="#creating-a-protein-mesh">Creating a Protein Mesh</a></li>
<li><a class="reference internal" href="#creating-molecular-graphs">Creating Molecular Graphs</a></li>
<li><a class="reference internal" href="#creating-an-rna-graph">Creating an RNA Graph</a></li>
<li><a class="reference internal" href="#creating-a-protein-protein-interaction-graph">Creating a Protein-Protein Interaction Graph</a></li>
<li><a class="reference internal" href="#creating-a-gene-regulatory-network-graph">Creating a Gene Regulatory Network Graph</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id13">Installation</a><ul>
<li><a class="reference internal" href="#pip">Pip</a></li>
<li><a class="reference internal" href="#conda-environment">Conda environment</a></li>
<li><a class="reference internal" href="#dockerfile">Dockerfile</a></li>
</ul>
</li>
<li><a class="reference internal" href="#citing-graphein">Citing Graphein</a><ul>
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<li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
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