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    "info": {
        "author": "Stefan Stojanovic",
        "author_email": "stefs304@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Healthcare Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry"
        ],
        "description": "# Proteinko\n\nProteinko is used for modeling distributions of psysicochemical properties of \nproteins.\n\n* [About](#About)\n* [Installation](#Installation)\n* [Usage](#Usage)\n\n---\n\n### About\n\nProtein is a sequence of amino acid residues, each characterized by a set of \nphysical and chemical properties. \nBy modeling properties of individual amino acid residues, mapping them to \nsingle vector representing a protein sequence and summing the overlapping \nportions of modeled amino acid residues, proteinko yields a distribution\n of physicochemical properties of protein sequence.\n\n![plot1](https://raw.githubusercontent.com/stefs304/proteinko/dev/resources/plot1.png)\n\nProteinko has built-in schemas for following properties, although it allows \nadding custom schemas for any real or theoretical property of amino acid \nresidues:\n\n* Hydropathy\n* Donor hydrogen bonds\n* Acceptor hydrogen bonds\n* Isoelectric point\n* Van der Waals volume\n\n### Installation\n```bash\npip install proteinko\n```\n\n### Usage\n\nTo start we are going to import class called **Proteinko** from `proteinko` \npackage and initialize the instance of the class.\n\n```python\nfrom proteinko import Proteinko\n\nprt = Proteinko()\n\n```\n\nTo list available physicochemical properties we can use the built-in method \n`get_schemas()`. This should produce the following output.\n```python\nschemas = prt.get_schemas()\nprint(schemas)\n\n>>> ['hydropathy', 'acceptors', 'donors', 'pI', 'volume']\n```\n\nThis looks fine, but let's add one of our own schemas. We are going to use \nKyte-Doolittle hydropathy schema which is stored in a CSV file located in local\n`resources/` directory.\n```python\nprt.add_schema(\n    'resources/kyte_doolittle.csv', \n    amino_col=0, \n    value_col=1, \n    key='kd', \n    header=1\n)\n```\nTo clarify what we did here, we passed the path to the csv file, specified \nthe columns which contain amino acid residues and corresponding values, \nprovided a key under which the data will be stored and let the parser know \nthe file has 1 header row. Now if we print schemas we should see following \noutput.\n```python\nprint(prt.get_schemas())\n\n>>> ['hydropathy', 'acceptors', 'donors', 'pI', 'volume', 'kd']\n```\n\nFinally, in order to get a distribution of Kyte-Doolittle hydropathy across \nprotein sequence, let's first define our protein sequence and than call the \nfunction `get_dist()` passing the sequence and schema as function arguments.\n```python\nsequence = 'ILKEPVHGV'\ndist = prt.get_dist(sequence, 'kd')\n```\n\nIf we plot our modeled distribution it should look something like this.\n\n![plot2](https://raw.githubusercontent.com/stefs304/proteinko/dev/resources/plot2.png)\n\n\n\n",
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        "name": "proteinko",
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