Metadata-Version: 1.0
Name: pyteomics.biolccc
Version: 1.5.0
Summary: Bindings for the libBioLCCC
Home-page: http://theorchromo.ru
Author: Anton Goloborodko
Author-email: goloborodko.anton@gmail.com
License: License :: Free for non-commercial use
Description: How to install pyteomics.biolccc?
        ---------------------------------
        
        Linux (Debian/Ubuntu):
        
        ::
        
            sudo apt-get install python-setuptools python-dev
            sudo easy_install pip
            sudo pip install pyteomics.biolccc
        
        Windows:
        
        * Download pre-compiled binary packages from the list below 
        
          OR
        
        * If you have Enthought Python Distribution / ActivePython, execute in the
          command line:
        
          ::
        
              easy_install pip
              pip install pyteomics.biolccc
        
        What is BioLCCC?
        ----------------
        
        BioLCCC (Liquid Chromatography of Biomacromolecules at Critical Conditions) is a
        model describing the adsorption of protein molecules on porous media. Its
        main application is retention time prediction in liquid chromatography, although
        the list of potential applications can be easily extended. Contrary to the other
        models of peptide/protein chromatography, BioLCCC starts from very basic
        assumptions regarding flexibility of a polypeptide chain, the shape of a pore,
        the type of interactions neglected, etc. Given these assumptions, the coefficient of
        distribution (Kd) of a peptide between the solid and mobile phases can be
        derived using the methods of statistical physics of macromolecules. Finally, the
        retention time of a peptide is calculated from Kd using the basic equation of
        gradient chromatography.
        
        Owing to the physical basis of the BioLCCC model, it contains very few free
        parameters. The retention properties of an amino acid are characterized by a
        single number, which is essentially the energy of interaction between the amino
        acid and the surface of solid phase in pure water+ion paring agent. Given this
        small number of phenomenological parameters, the BioLCCC model can be easily
        adapted for an arbitrary type of chromatography not limited by phase or solvent
        types. Moreover, its extension to peptides with post-translational modifications
        is straightforward as it was shown for the phosphorylated amino acids.
        
        Several papers regarding BioLCCC model were published:
        
        1. Liquid Chromatography at Critical Conditions:  Comprehensive Approach to
        Sequence-Dependent Retention Time Prediction, Alexander V. Gorshkov, Irina A.
        Tarasova, Victor V. Evreinov, Mikhail M. Savitski, Michael L. Nielsen, Roman A.
        Zubarev, and Mikhail V. Gorshkov, Analytical Chemistry, 2006, 78 (22),
        7770-7777. Link: http://dx.doi.org/10.1021/ac060913x.
        
        2. Applicability of the critical chromatography concept to proteomics problems:
        Dependence of retention time on the sequence of amino acids, Alexander V.
        Gorshkov A., Victor V. Evreinov V., Irina A. Tarasova, Mikhail V. Gorshkov,
        Polymer Science B, 2007, 49 (3-4), 93-107. 
        Link: http://dx.doi.org/10.1134/S1560090407030098.
        
        3. Applicability of the critical chromatography concept to proteomics problems:
        Experimental study of the dependence of peptide retention time on the sequence
        of amino acids in the chain, Irina A. Tarasova, Alexander V. Gorshkov, Victor V.
        Evreinov, Chris Adams, Roman A. Zubarev, and Mikhail V. Gorshkov, Polymer
        Science A, 2008, 50 (3), 309. 
        Link: http://www.springerlink.com/content/gnh84v62w960747n/.
        
        4. Retention time prediction using the model of liquid chromatography of
        biomacromolecules at critical conditions in LC-MS phosphopeptide analysis,
        Tatiana Yu. Perlova, Anton A. Goloborodko, Yelena Margolin, Marina L.
        Pridatchenko, Irina A. Tarasova, Alexander V. Gorshkov, Eugene Moskovets,
        Alexander R. Ivanov and Mikhail V. Gorshkov, Accepted to Proteomics.
        Link: http://dx.doi.org/10.1002/pmic.200900837.
        
        What is pyteomics.biolccc?
        --------------------------
        
        pyteomics.biolccc is an open source library, which implements the BioLCCC model
        in the combination of Python and C++ programming languages. 
        It performs most BioLCCC-related tasks, such as:
        
        * predicts the retention time of peptides and proteins in given 
          chromatographic conditions;
        * predicts the adsorption properties of protein molecules, namely coefficient of
          distribution between mobile and solid phase;
        * manages elution conditions and physicochemical constants;
        * calculates masses of peptides and proteins.
        
        What is libBioLCCC?
        -------------------
        
        libBioLCCC is the C++ layer of pyteomics.biolccc. libBioLCCC can be used 
        separately from the Python wrappings and has a clean and well-documented API.
        
        Where can I find more information?
        ----------------------------------
        
        The project documentation is hosted at 
        http://packages.python.org/pyteomics.biolccc/
        
        The source code of pyteomics.biolccc and underlying libBioLCCC C++ library is 
        open and hosted at http://hg.theorchromo.ru.
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
