thoipapy 1.2.0
Machine-learning prediction of residues driving homotypic transmembrane interactions.
License: MIT
Requires Python: >=3
Keywords: bioinformatics protein transmembrane residue conservation coevolution covariance evolutionary couplings polarity hydrophobicity randomforest machinelearning interface LIPS evolution

.. image:: https://raw.githubusercontent.com/bojigu/thoipapy/develop/thoipapy/docs/THOIPA_banner.png

THOIPApy
========

The Transmembrane HOmodimer Interface Prediction Algorithm (THOIPA) is a machine learning method for the analysis of protein-protein-interactions.

THOIPA predicts transmembrane homodimer interface residues from evolutionary sequence information.

THOIPA helps predict potential homotypic transmembrane interface residues, which can then be verified experimentally.
THOIPA also aids in the energy-based modelling of transmembrane homodimers.

Important links:

* `THOIPA webserver <http://www.thoipa.org>`_
* `THOIPA FAQ <https://github.com/bojigu/thoipapy/wiki/What-is-THOIPA%3F>`_
* `THOIPA wiki main page <https://github.com/bojigu/thoipapy/wiki/THOIPA-wiki-main-page>`_


How does thoipapy work?
-----------------------

* downloads protein homologues with BLAST
* extracts residue properties (e.g. residue conservation and polarity)
* trains a machine learning classifier
* validates the prediction performance
* creates heatmaps of residue properties and THOIPA prediction


Installation
------------
.. code::

    pip install thoipapy

THOIPA has only been tested on Linux, due to reliance on external dependencies such as FreeContact, Phobius, CD-HIT and rate4site.
For predictions only, a dockerised version is available that runs on Windows or MacOS.
Please see the `THOIPA webserver <http://www.thoipa.org>`_ for the latest information.


Dependencies
------------

We recommend the `Anaconda python distribution <https://www.anaconda.com/products/individual>`_, which contains all the required python modules
(numpy, scipy, pandas,biopython and matplotlib). THOIPApy is currently tested for python 3.8.5. The requirements.txt contains a snapshot of compatible
dependencies.


Development status
------------------

The code has been extensively updated and annotated for public release. However is released "as is" with some known issues, limitations and legacy code.


Usage as a standalone predictor
-------------------------------

* first check if your needs are met by the `THOIPA webserver <http://www.thoipa.org>`_ or the latest version of dockerised software
* for local predictions on linux, first install phobius, NCBI_BLAST, biopython, freecontact, CD-HIT, and rate4site
* please see `thoipapy/test/functional/test_standalone_prediction.py <https://github.com/bojigu/thoipapy/tree/develop/thoipapy/test/functional/test_standalone_prediction.py>`_ for the latest run syntax, typically

.. code:: python

    from thoipapy.thoipa import get_md5_checksum, run_THOIPA_prediction
    from thoipapy.utils import make_sure_path_exists

    protein_name = "ERBB3"
    TMD_seq = "MALTVIAGLVVIFMMLGGTFL"
    full_seq = "MVQNECRPCHENCTQGCKGPELQDCLGQTLVLIGKTHLTMALTVIAGLVVIFMMLGGTFLYWRGRRIQNKRAMRRYLERGESIEPLDPSEKANKVLA"
    out_dir = "/path/to/your/desired/output/folder"
    make_sure_path_exists(out_dir)
    md5 = get_md5_checksum(TMD_seq, full_seq)
    run_THOIPA_prediction(protein_name, md5, TMD_seq, full_seq, out_dir)


**Example Output**

* the output includes a csv showing the THOIPA prediction for each residue, as well as a heatmap figure as a summary
* below is a heatmap showing the THOIPA prediction, and underlying conservation, relative polarity, and coevolution

.. image:: https://raw.githubusercontent.com/bojigu/thoipapy/master/thoipapy/docs/standalone_heatmap_example.png


Create your own machine learning predictor
------------------------------------------

* THOIPA can be retrained to any dataset of your choice
* the original set of training sequences and other resources are available via the `Open Science Foundation <https://osf.io/txjev/>`_
* the THOIPA feature extraction, feature selection, and training pipeline is fully automated
* contact us for an introduction to the THOIPA software pipeline and settings

.. code:: bash

    python path/to/thoipapy/run.py -s home/user/thoipa/THOIPA_settings.xlsx


License
-------

THOIPApy is free software distributed under the permissive MIT License.


Contribute
-------------

* Contributors are welcome.
* For feedback or troubleshooting, please email us directly and initiate an issue in Github.


Contact
-------

* Mark Teese, `TNG Technology Consulting GmbH <https://www.tngtech.com/en/index.html>`_, formerly of the `Langosch Lab <http://cbp.wzw.tum.de/index.php?id=10>`_ at the `Technical University of Munich <https://www.tum.de/en/>`_
* `Bo Zeng <http://frishman.wzw.tum.de/index.php?id=50>`_, `Chinese Academy of Sciences, Beijing <http://english.cas.cn/>`_ formerly of the `Frishman Lab <http://frishman.wzw.tum.de/index.php?id=2>`_ at the `Technical University of Munich <https://www.tum.de/en/>`_

.. image:: https://raw.githubusercontent.com/bojigu/thoipapy/develop/thoipapy/docs/signac_seine_bei_samois_mt.png
   :height: 150px
   :width: 250px

.. image:: https://raw.githubusercontent.com/bojigu/thoipapy/develop/thoipapy/docs/signac_notredame_bz.png
   :height: 120px
   :width: 250px


Citation
--------

`Yao Xiao, Bo Zeng, Nicola Berner, Dmitrij Frishman, Dieter Langosch, and Mark George Teese (2020)
Experimental determination and data-driven prediction of homotypic transmembrane domain interfaces,
Computational and Structural Biotechnology Journal <https://doi.org/10.1016/j.csbj.2020.09.035>`_