Metadata-Version: 2.1
Name: thoipapy
Version: 1.1.2
Summary: Machine-learning prediction of residues driving homotypic transmembrane interactions.
Home-page: https://github.com/bojigu/thoipapy
Author: Bo Zeng
Author-email: zeng@checkmytumhomepage.de
License: MIT
Download-URL: https://github.com/bojigu/thoipapy/archive/1.1.2.tar.gz
Project-URL: BoZeng, http://frishman.wzw.tum.de/index.php?id=50
Project-URL: FrishmanLab, http://frishman.wzw.tum.de/index.php?id=2
Project-URL: LangoschLab, http://cbp.wzw.tum.de/index.php?id=10
Project-URL: TU_Munich, https://www.tum.de
Description: .. 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, accepted manuscript.
Keywords: bioinformatics protein transmembrane residue conservation coevolution covariance evolutionary couplings polarity hydrophobicity randomforest machinelearning interface LIPS evolution
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3
Description-Content-Type: text/x-rst
