{
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        "author": "Bo Zeng",
        "author_email": "zeng@checkmytumhomepage.de",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": ".. image:: https://raw.githubusercontent.com/bojigu/thoipapy/master/thoipapy/docs/THOIPApy_logo.png\n\nTHOIPApy\n========\n\nThe Transmembrane HOmodimer Interface Prediction Algorithm (THOIPA) is a machine learning method for the analysis of protein-protein-interactions.\n\nTHOIPA predicts TM homodimer interface residues from evolutionary sequence information alone.\n\nTHOIPA was designed to complement experimental approaches, and also energy-based modelling of TM homodimers.\n\nSee the `FAQ`__ in the THOIPA wiki for more information.\n\n.. _FAQ: https://github.com/bojigu/thoipapy/wiki/What-is-THOIPA%3F\n__ FAQ_\n\nWhat does thoipapy do?\n----------------------\n\n* download protein homologues with BLAST\n* extract residue properties (e.g. residue conservation and polarity)\n* trains a machine learning classifier\n* validates the prediction performance\n* creates heatmaps of residue properties and THOIPA prediction\n\n\nInstallation\n------------\n.. code:: python\n\n    pip install thoipapy\n\nTHOIPA has external dependencies such as FreeContact and Phobius that are only working on Linux.\n\nUse the \"Docker\" implementation detailed in the `Wiki`__ to:\n* run THOIPA standalone on platforms such as Windows or MacOS\n* run THOIPA standalone on Linux without installing the dependencies on your system\n\n.. _Wiki: https://github.com/bojigu/thoipapy/wiki\n__ Wiki_\n\n\nDependencies\n------------\n\nWe recommend the `Anaconda python distribution`__, which contains all the required python modules (numpy, scipy, pandas,biopython and matplotlib). THOIPApy is currently tested for python 3.6.\n\nPip should automatically install the pytoxr package of Mark Teese.\n\n.. _AnacondaLink: https://www.continuum.io/downloads\n__ AnacondaLink_\n\nTHOIPApy depends on the command-line programs phobius and freecontact.\nBoth of these are only available for Linux. THOIPApy itself has been tested on several different systems running Windows and Linux.\n\nDevelopment status\n------------------\n\nThe code has been extensively updated and annotated for public release. However is released \"as is\" with some known issues, limitations and legacy code.\nThe THOIPA standalone predictor is currently available to use. The settings file and databases used for THOIPA training are not yet released.\n\nUsage as a standalone predictor\n-------------------------------\n\nFor TMD interface residue predictions of a protein of interest, we recommend running THOIPA as a standalone program via Docker, as described in the `Wiki`__ .\n\n.. _Wiki: https://github.com/bojigu/thoipapy/wiki\n__ Wiki_\n\nTHOIPA can also be installed in Linux and used as a standalone predictor:\n* The operating system needs to have freecontact, phobius, and NCBI_BLAST installed.\n* The biopython wrapper for NCBIblast should be installed.\n\n.. code:: python\n\n    from thoipapy.thoipa import get_md5_checksum, run_THOIPA_prediction\n    from thoipapy.utils import make_sure_path_exists\n\n    protein_name = \"ERBB3\"\n    TMD_seq = \"MALTVIAGLVVIFMMLGGTFL\"\n    full_seq = \"MVQNECRPCHENCTQGCKGPELQDCLGQTLVLIGKTHLTMALTVIAGLVVIFMMLGGTFLYWRGRRIQNKRAMRRYLERGESIEPLDPSEKANKVLA\"\n    out_dir = \"/path/to/your/desired/output/folder\"\n    make_sure_path_exists(out_dir)\n    md5 = get_md5_checksum(TMD_seq, full_seq)\n    run_THOIPA_prediction(protein_name, md5, TMD_seq, full_seq, out_dir)\n\n**Example Output**\n\n.. image:: https://raw.githubusercontent.com/bojigu/thoipapy/master/thoipapy/docs/standalone_heatmap_example.png\n\nCreate your own machine learning predictor\n------------------------------------------\n\nDetails on how to train THOIPA on your own datasets will be released after publication.\n\n.. code:: python\n\n    import THOIPApy\n    settings = r\"D:\\data\\THOIPApy_settings.xlsx\"\n    THOIPApy.run(settings)\n\nLicense\n-------\n\nTHOIPApy is free software distributed under the permissive MIT License.\n\n\nContribute\n-------------\n\nTHOIPApy is not yet officially published. However, feedback regarding the installation and usage of the standalone version is appreciated. Simply email us directly, or initiate an issue in Github.\n\n\nContact\n-------\n\nFor contact details, see the relevant TU-Munich websites:\n\nAuthor: `Bo Zeng`__  of the `Frishman lab`__, TU-Munich, Weihenstephan Campus\n\nFurther coding and supervision: `Mark Teese`__ of the `Langosch lab`__, TU-Munich, Weihenstephan Campus\n\n.. _BoWebsite: http://frishman.wzw.tum.de/index.php?id=50\n.. _FrishmanWebsite: http://frishman.wzw.tum.de/index.php?id=2\n.. _MarkWebsite: http://cbp.wzw.tum.de/index.php?id=49&L=1\n.. _LangoschWebsite: http://cbp.wzw.tum.de/index.php?id=10\n__ BoWebsite_\n__ FrishmanWebsite_\n__ MarkWebsite_\n__ LangoschWebsite_\n\n\nCitation\n--------\n\nCitation to be added.\nFull Credits: Bo Zeng, Yao Xiao, Dmitrij Frishman, Dieter Langosch, Mark Teese\n\n\n\n",
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        "keywords": "bioinformatics protein transmembrane residue conservation coevolution covariance evolutionary couplings polarity hydrophobicity randomforest machinelearning interface LIPS evolution",
        "license": "MIT",
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        "name": "thoipapy",
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