.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery_output/plot_protein_interactions.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_output_plot_protein_interactions.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_output_plot_protein_interactions.py:


Identify protein interactions that may be impacted by splicing of PTMs
=============================================================================================================

Post translational modifications (PTMs) often facilitate protein interactions, either through direct binding of domains specific to that particular modification (e.g. SH2 domains binding to phosphorylated tyrosines) or through allosteric effects that change the conformation of the protein to either enhance or disrupt interactions. We provide functions to annotate spliced PTMs with relevant protein interactions and to identify key PTMs that may disrupt protein interaction networks.

Currently, we provide functions to process and analyze protein interaction data from PhosphoSitePlus, PTMInt, and PTMcode. We can also include enzyme-specific interactions (such as kinase substrate interactions through PhosphoSitePlus and RegPhos). First, we need to annotate the spliced PTMs with protein interactions (see rest of documentation for how to do this). Then, we can process the interactions across the different databases using the protein_interactions class to identify key PTMs that may disrupt protein interaction networks.

.. GENERATED FROM PYTHON SOURCE LINES 9-21

.. code-block:: Python


    from ptm_pose import analyze
    import pandas as pd

    # Load spliced ptm and altered flank data
    spliced_ptms = pd.read_csv('spliced_ptms.csv')

    interactions = analyze.protein_interactions(spliced_ptms)
    interactions.get_interaction_network()

    interactions.network_data.head()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    PhosphoSitePlus regulatory site data found and added
    Combined kinase-substrate data found and added
    PTMcode data found and added
    PTMInt data found and added
    ELM data found and added


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Modified Gene</th>
          <th>Interacting Gene</th>
          <th>Residue</th>
          <th>Type</th>
          <th>Source</th>
          <th>dPSI</th>
          <th>Regulation Change</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>ABI1</td>
          <td>ABL1</td>
          <td>S361</td>
          <td>INDUCES</td>
          <td>PTMcode</td>
          <td>0.213</td>
          <td>+</td>
        </tr>
        <tr>
          <th>1</th>
          <td>ABI1</td>
          <td>BAIAP2</td>
          <td>S361</td>
          <td>INDUCES</td>
          <td>PTMcode</td>
          <td>0.213</td>
          <td>+</td>
        </tr>
        <tr>
          <th>2</th>
          <td>ABI1</td>
          <td>CYFIP2</td>
          <td>S361</td>
          <td>INDUCES</td>
          <td>PTMcode</td>
          <td>0.213</td>
          <td>+</td>
        </tr>
        <tr>
          <th>3</th>
          <td>ABI1</td>
          <td>EPS8</td>
          <td>S361</td>
          <td>INDUCES</td>
          <td>PTMcode</td>
          <td>0.213</td>
          <td>+</td>
        </tr>
        <tr>
          <th>4</th>
          <td>ABI1</td>
          <td>EPS8L1</td>
          <td>S361</td>
          <td>INDUCES</td>
          <td>PTMcode</td>
          <td>0.213</td>
          <td>+</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 22-23

We can also calculate interaction stats to identify proteins that are most impacted or relevant to spliced PTMs and the protein interaction network

.. GENERATED FROM PYTHON SOURCE LINES 23-27

.. code-block:: Python

    interactions.get_interaction_stats()

    interactions.network_stats.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Degree</th>
          <th>Degree Centrality</th>
          <th>Closeness</th>
          <th>Betweenness</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>ABI1</th>
          <td>18</td>
          <td>0.111111</td>
          <td>0.214493</td>
          <td>0.237466</td>
        </tr>
        <tr>
          <th>ABL1</th>
          <td>3</td>
          <td>0.018519</td>
          <td>0.176786</td>
          <td>0.105207</td>
        </tr>
        <tr>
          <th>BAIAP2</th>
          <td>2</td>
          <td>0.012346</td>
          <td>0.166163</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>CYFIP2</th>
          <td>1</td>
          <td>0.006173</td>
          <td>0.164051</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>EPS8</th>
          <td>1</td>
          <td>0.006173</td>
          <td>0.164051</td>
          <td>0.000000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 28-29

If we want to focus on a specific protein, we can summarize information about a single protein in the network. In this case, let's look at TSC2, which loses pS981 upon ESRP1 knockdown

.. GENERATED FROM PYTHON SOURCE LINES 29-32

.. code-block:: Python


    interactions.summarize_protein_network(protein = 'TSC2')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Decreased interaction likelihoods: AKT1, SGK1, YWHAE, YWHAZ
    Number of interactions: 4 (Rank: 14)
    Centrality measures -    Degree = 0.024691358024691357 (Rank: 14)
                             Betweenness = 0.0004600874166091557 (Rank: 28)
                             Closeness = 0.024691358024691357 (Rank: 126)




.. GENERATED FROM PYTHON SOURCE LINES 33-34

We can also visualize the network...

.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: Python


    interactions.plot_interaction_network(interacting_node_size = 10)




.. image-sg:: /gallery_output/images/sphx_glr_plot_protein_interactions_001.png
   :alt: plot protein interactions
   :srcset: /gallery_output/images/sphx_glr_plot_protein_interactions_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 38-39

...and the centrality of proteins in the network

.. GENERATED FROM PYTHON SOURCE LINES 39-40

.. code-block:: Python


    interactions.plot_network_centrality(centrality_measure='Degree')


.. image-sg:: /gallery_output/images/sphx_glr_plot_protein_interactions_002.png
   :alt: plot protein interactions
   :srcset: /gallery_output/images/sphx_glr_plot_protein_interactions_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.096 seconds)


.. _sphx_glr_download_gallery_output_plot_protein_interactions.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_protein_interactions.ipynb <plot_protein_interactions.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_protein_interactions.py <plot_protein_interactions.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_protein_interactions.zip <plot_protein_interactions.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_