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Bindings for the libBioLCCC

Project 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


  • Download pre-compiled binary packages from the list below


  • 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:

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:

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:

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:

What is libBioLCCC?

libBioLCCC is an open source library, which implements the BioLCCC model in C++ programming language. It performs basic 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.

libBioLCCC has a simple and well-documented API.

What is pyteomics.biolccc?

pyteomics.biolccc is a set of Python wrappings around libBioLCCC. It allows to invoke libBioLCCC functions from Python programming language.

The main purpose of pyteomics.biolccc is to make libBioLCCC available in a programming language not so demanding as C++. The choice of Python is dictated by several points. Among them are simplicity, the great variety of libraries and the extreme speed of development which could be very well appreciated in the modern scientific world.

Where can I find more information?

The project documentation is hosted at

The source code of libBioLCCC/pyteomics.biolccc is open and hosted at

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