Metadata-Version: 2.1
Name: proteinko
Version: 3.0.0
Summary: Proteinko is used for modeling distributions of psysicochemical properties of proteins
Home-page: https://github.com/stefs304/proteinko
Author: Stefan Stojanovic
Author-email: stefs304@gmail.com
License: UNKNOWN
Description: # Proteinko
        
        Proteinko is used for modeling distributions of psysicochemical properties of 
        proteins.
        
        * [About](#About)
        * [Installation](#Installation)
        * [Usage](#Usage)
        
        ---
        
        ### About
        
        Protein is a sequence of amino acid residues, each characterized by a set of 
        physical and chemical properties. 
        By modeling properties of individual amino acid residues, mapping them to 
        single vector representing a protein sequence and summing the overlapping 
        portions of modeled amino acid residues, proteinko yields a distribution
         of physicochemical properties of protein sequence.
         
        ![plot1](https://raw.githubusercontent.com/stefs304/proteinko/dev/resources/plot1.png)
        
        Proteinko has built-in schemas for following properties, although it allows 
        adding custom schemas for any real or theoretical property of amino acid 
        residues:
        
        * Hydropathy
        * Donor hydrogen bonds
        * Acceptor hydrogen bonds
        * Isoelectric point
        * Van der Waals volume
        
        ### Installation
        ```bash
        pip install proteinko
        ```
        
        ### Usage
        
        To start we are going to import class called **Proteinko** from `proteinko` 
        package and initialize the instance of the class.
         
        ```python
        from proteinko import Proteinko
        
        prt = Proteinko()
        
        ```
        
        To list available physicochemical properties we can use the built-in method 
        `get_schemas()`. This should produce the following output.
        ```python
        schemas = prt.get_schemas()
        print(schemas)
         
        >>> ['hydropathy', 'acceptors', 'donors', 'pI', 'volume']
        ```
        
        This looks fine, but let's add one of our own schemas. We are going to use 
        Kyte-Doolittle hydropathy schema which is stored in a CSV file located in local
        `resources/` directory.
        ```python
        prt.add_schema(
            'resources/kyte_doolittle.csv', 
            amino_col=0, 
            value_col=1, 
            key='kd', 
            header=1
        )
        ```
        To clarify what we did here, we passed the path to the csv file, specified 
        the columns which contain amino acid residues and corresponding values, 
        provided a key under which the data will be stored and let the parser know 
        the file has 1 header row. Now if we print schemas we should see following 
        output.
        ```python
        print(prt.get_schemas())
         
        >>> ['hydropathy', 'acceptors', 'donors', 'pI', 'volume', 'kd']
        ```
        
        Finally, in order to get a distribution of Kyte-Doolittle hydropathy across 
        protein sequence, let's first define our protein sequence and than call the 
        function `get_dist()` passing the sequence and schema as function arguments.
        ```python
        sequence = 'ILKEPVHGV'
        dist = prt.get_dist(sequence, 'kd')
        ```
        
        If we plot our modeled distribution it should look something like this.
        
        ![plot2](https://raw.githubusercontent.com/stefs304/proteinko/dev/resources/plot2.png)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 2
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Description-Content-Type: text/markdown
