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        "author": "Anton Goloborodko",
        "author_email": "goloborodko.anton@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "How to install pyteomics.biolccc?\r\n---------------------------------\r\n\r\nLinux (Debian/Ubuntu):\r\n\r\n::\r\n\r\n    sudo apt-get install python-setuptools python-dev\r\n    sudo easy_install pip\r\n    sudo pip install pyteomics.biolccc\r\n\r\nWindows:\r\n\r\n* Download pre-compiled binary packages from the list below \r\n\r\n  OR\r\n\r\n* If you have Enthought Python Distribution / ActivePython, execute in the\r\n  command line:\r\n\r\n  ::\r\n\r\n      easy_install pip\r\n      pip install pyteomics.biolccc\r\n\r\nWhat is BioLCCC?\r\n----------------\r\n\r\nBioLCCC (Liquid Chromatography of Biomacromolecules at Critical Conditions) is a\r\nmodel describing the adsorption of protein molecules on porous media. Its\r\nmain application is retention time prediction in liquid chromatography, although\r\nthe list of potential applications can be easily extended. Contrary to the other\r\nmodels of peptide/protein chromatography, BioLCCC starts from very basic\r\nassumptions regarding flexibility of a polypeptide chain, the shape of a pore,\r\nthe type of interactions neglected, etc. Given these assumptions, the coefficient of\r\ndistribution (Kd) of a peptide between the solid and mobile phases can be\r\nderived using the methods of statistical physics of macromolecules. Finally, the\r\nretention time of a peptide is calculated from Kd using the basic equation of\r\ngradient chromatography.\r\n\r\nOwing to the physical basis of the BioLCCC model, it contains very few free\r\nparameters. The retention properties of an amino acid are characterized by a\r\nsingle number, which is essentially the energy of interaction between the amino\r\nacid and the surface of solid phase in pure water+ion paring agent. Given this\r\nsmall number of phenomenological parameters, the BioLCCC model can be easily\r\nadapted for an arbitrary type of chromatography not limited by phase or solvent\r\ntypes. Moreover, its extension to peptides with post-translational modifications\r\nis straightforward as it was shown for the phosphorylated amino acids.\r\n\r\nSeveral papers regarding BioLCCC model were published:\r\n\r\n1. Liquid Chromatography at Critical Conditions:\u2009 Comprehensive Approach to\r\nSequence-Dependent Retention Time Prediction, Alexander V. Gorshkov, Irina A.\r\nTarasova, Victor V. Evreinov, Mikhail M. Savitski, Michael L. Nielsen, Roman A.\r\nZubarev, and Mikhail V. Gorshkov, Analytical Chemistry, 2006, 78 (22),\r\n7770-7777. Link: http://dx.doi.org/10.1021/ac060913x.\r\n\r\n2. Applicability of the critical chromatography concept to proteomics problems:\r\nDependence of retention time on the sequence of amino acids, Alexander V.\r\nGorshkov A., Victor V. Evreinov V., Irina A. Tarasova, Mikhail V. Gorshkov,\r\nPolymer Science B, 2007, 49 (3-4), 93-107. \r\nLink: http://dx.doi.org/10.1134/S1560090407030098.\r\n\r\n3. Applicability of the critical chromatography concept to proteomics problems:\r\nExperimental study of the dependence of peptide retention time on the sequence\r\nof amino acids in the chain, Irina A. Tarasova, Alexander V. Gorshkov, Victor V.\r\nEvreinov, Chris Adams, Roman A. Zubarev, and Mikhail V. Gorshkov, Polymer\r\nScience A, 2008, 50 (3), 309. \r\nLink: http://www.springerlink.com/content/gnh84v62w960747n/.\r\n\r\n4. Retention time prediction using the model of liquid chromatography of\r\nbiomacromolecules at critical conditions in LC-MS phosphopeptide analysis,\r\nTatiana Yu. Perlova, Anton A. Goloborodko, Yelena Margolin, Marina L.\r\nPridatchenko, Irina A. Tarasova, Alexander V. Gorshkov, Eugene Moskovets,\r\nAlexander R. Ivanov and Mikhail V. Gorshkov, Accepted to Proteomics.\r\nLink: http://dx.doi.org/10.1002/pmic.200900837.\r\n\r\nWhat is libBioLCCC?\r\n-------------------\r\n\r\nlibBioLCCC is an open source library, which implements the BioLCCC model in \r\nC++ programming language. It performs basic BioLCCC-related tasks, such as:\r\n\r\n* predicts the retention time of peptides and proteins in given \r\n  chromatographic conditions;\r\n* predicts the adsorption properties of protein molecules, namely coefficient of\r\n  distribution between mobile and solid phase;\r\n* manages elution conditions and physicochemical constants;\r\n* calculates masses of peptides and proteins.\r\n\r\nlibBioLCCC has a simple and well-documented API.\r\n\r\nWhat is pyteomics.biolccc?\r\n--------------------------\r\n\r\npyteomics.biolccc is a set of Python wrappings around libBioLCCC. It allows to \r\ninvoke libBioLCCC functions from Python programming language.\r\n\r\nThe main purpose of pyteomics.biolccc is to make libBioLCCC available in a\r\nprogramming language not so demanding as C++. The choice of Python is dictated\r\nby several points. Among them are simplicity, the great variety of libraries and\r\nthe extreme speed of development which could be very well appreciated in the\r\nmodern scientific world.\r\n\r\nWhere can I find more information?\r\n----------------------------------\r\n\r\nThe project documentation is hosted at http://packages.python.org/pyteomics.biolccc \r\n\r\nThe source code of libBioLCCC/pyteomics.biolccc is open and hosted at\r\nhttp://hg.theorchromo.ru.",
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