Skip to main content

Bindings for the libBioLCCC

Project description

How to install pyteomics.biolccc?

Install from PyPI:

python -m pip install pyteomics.biolccc

Build from a source checkout:

git clone https://github.com/levitsky/biolccc
cd biolccc
python -m pip install .

For development, use an editable install instead:

python -m pip install -e .

To build from source you need a C++17 compiler toolchain and Python headers. On Debian/Ubuntu the typical system dependency set is:

sudo apt-get install build-essential python3-dev

Windows:

Use the standard Python packaging workflow:

py -m pip install pyteomics.biolccc

If no wheel is available for your interpreter, install Visual Studio Build Tools and then rerun the same command.

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://theorchromo.ru/docs

The source code of pyteomics.biolccc and underlying libBioLCCC C++ library is open and hosted at https://github.com/levitsky/biolccc.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyteomics_biolccc-1.6.1.tar.gz (43.6 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pyteomics_biolccc-1.6.1-cp314-cp314-win_amd64.whl (213.4 kB view details)

Uploaded CPython 3.14Windows x86-64

pyteomics_biolccc-1.6.1-cp314-cp314-musllinux_1_2_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.14musllinux: musl 1.2+ x86-64

pyteomics_biolccc-1.6.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (745.0 kB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

pyteomics_biolccc-1.6.1-cp314-cp314-macosx_11_0_arm64.whl (563.6 kB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

pyteomics_biolccc-1.6.1-cp313-cp313-win_amd64.whl (208.0 kB view details)

Uploaded CPython 3.13Windows x86-64

pyteomics_biolccc-1.6.1-cp313-cp313-musllinux_1_2_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ x86-64

pyteomics_biolccc-1.6.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (745.1 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

pyteomics_biolccc-1.6.1-cp313-cp313-macosx_11_0_arm64.whl (563.5 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

pyteomics_biolccc-1.6.1-cp312-cp312-win_amd64.whl (208.0 kB view details)

Uploaded CPython 3.12Windows x86-64

pyteomics_biolccc-1.6.1-cp312-cp312-musllinux_1_2_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ x86-64

pyteomics_biolccc-1.6.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (744.9 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

pyteomics_biolccc-1.6.1-cp312-cp312-macosx_11_0_arm64.whl (563.4 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

pyteomics_biolccc-1.6.1-cp311-cp311-win_amd64.whl (205.1 kB view details)

Uploaded CPython 3.11Windows x86-64

pyteomics_biolccc-1.6.1-cp311-cp311-musllinux_1_2_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ x86-64

pyteomics_biolccc-1.6.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (727.9 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

pyteomics_biolccc-1.6.1-cp311-cp311-macosx_11_0_arm64.whl (553.7 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pyteomics_biolccc-1.6.1-cp310-cp310-win_amd64.whl (204.7 kB view details)

Uploaded CPython 3.10Windows x86-64

pyteomics_biolccc-1.6.1-cp310-cp310-musllinux_1_2_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.10musllinux: musl 1.2+ x86-64

pyteomics_biolccc-1.6.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (727.5 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

pyteomics_biolccc-1.6.1-cp310-cp310-macosx_11_0_arm64.whl (553.1 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file pyteomics_biolccc-1.6.1.tar.gz.

File metadata

  • Download URL: pyteomics_biolccc-1.6.1.tar.gz
  • Upload date:
  • Size: 43.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for pyteomics_biolccc-1.6.1.tar.gz
Algorithm Hash digest
SHA256 a89cc66f68233ffb28d90a5cfbc0bf85c9562ea273be532841d32e5c7d1d4056
MD5 7fda542d8d17eca67cf9e7dac68122a8
BLAKE2b-256 86b12305ad4c4f90e65da8c67322f9d98b68d4b780dffbf71031a5df09e5526a

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp314-cp314-win_amd64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 95f43bffbc6e0e44d2f95562a396dff7b33ea4b9c57d35963468ecb3d5b0e03c
MD5 3adac559bb9c33a1ecf18f81c5f63a15
BLAKE2b-256 23a69268e3a5b085d01ff615da5abeadb9d34601abfe836f4791cc608227823f

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp314-cp314-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp314-cp314-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 89f7151c9489567741d79de94bc7b6cb798526b471b2f7437f3c1aa95c1ddd28
MD5 6bf458c728b62b4d81fe1fe9041c1dc3
BLAKE2b-256 83cbda7ee8b474166229c0345e012bee8f69d7486163e21b2e8faf37321c3864

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 87765bc0de85692ddcccb8ada0e93dc8e544d4cd344bcaf1c785d6f70cd28a2f
MD5 22171fd55ba30d8a036a5a4b3702be4e
BLAKE2b-256 5d9bc246c1ebd036387be04d6b52c8b9a0ceb74d54317e351a565ec089234bdd

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 79514980d5fe6342e39ab5cab50467c9d36504bce3b20da15c01f5ad8d4b9a84
MD5 4f341a36e9834d02a559f142641a87b7
BLAKE2b-256 34f03bb76889f89556f2d578040ab909220c69151fed336eab19ce011b9c2136

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 6638d6bc9331c7605cff20a8ddcf2127e2285e0b94a02b1160c4d14a7b95a52c
MD5 eb21eba3d8e804791aeb429fbaa10464
BLAKE2b-256 c7985692fc132a108a9d24d12063bf46392b6aa916fe62d545f7a06ebff1daf8

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp313-cp313-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp313-cp313-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 dc2bd4cf584ad6852eeb5e9b33c04b68142fb69d43f80b546c2b6975534d6e7d
MD5 37d98299ce01ee398d074d8f62075681
BLAKE2b-256 4eecff0acfaa30559913a819e4f9d04b2e8549f2f0437459f6ca54b81b2c58fb

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 7e55881aa85c76f3f46167110d5a81c4e3bff14f5f5c3b2a9f2358ceca9958b0
MD5 bf7679f24bff29a7c7ef15133792dc49
BLAKE2b-256 2c3a392a3344c9dbaeeb2834430ad1e565c87e9648f754a964963849b3a07d73

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 def113526c49c6e5d4de254532d2f183acdf88d8896ef34042b73f1794c64744
MD5 6ca78562275e2b19c59498ff75457e08
BLAKE2b-256 6537f224faf855af8e333a335d3ff9d6f9b0c515f9d14242567ebad9e81ccafd

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 25afb6c848848e2a44f7126be159f7a151751cddf05f3905b8e3f1f5f199c351
MD5 5db42e445986d7aa4b0f2a5e64cb75f4
BLAKE2b-256 37360ccec5d340c19cebb3d1b449ceadd7e9031e3332f983170af6dd4b63c264

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp312-cp312-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp312-cp312-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 dd31795dc02b8b9bcfc8b2bbf0f1f9fe8d6646077ab077cc428731ca4c166380
MD5 f471b44d4135757fa766d3e225a01884
BLAKE2b-256 6b276e6fbe79b89eaaa200e6c33d09c2dea29269f6bca71919bc699983bdae51

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 f6ff953a5c9d47c29edb69f87c14757b039e3f44be2b22670ed134a3b8cb766b
MD5 3c19f0400d55440c76e00029fc1072f5
BLAKE2b-256 6e4fdf850f1f76be534bf4c87ba15c52c01f13eb85529685a74d0edf42fc87bf

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cf267d99b732e64c99d09b60acc114c2ff077ba0676e2efb0b5e0c2254ed6603
MD5 b8fb7291dc6aab76a8877aabeebd0d77
BLAKE2b-256 c495c1b97c55eca9a14374d5d562b9dd5216188f1518612709cc78dc2eb7e8ac

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 2cfe7d7b03ec44428d4a495bdc77955438ceaeb1bd8f34a3e225d0bafffee8b5
MD5 0f6a82dd8baca0d4ca6ff04089221ec0
BLAKE2b-256 b006f3705286aaa7bd67baf702a9a31d49081a49276d179ab12b20b529fae465

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp311-cp311-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 078f8ce20c9c8037aec2df4029ff11d7d4739a6b4bd02dcd04b69a2e22517313
MD5 db81bbd9fafdc2eddef5b6c622b323c7
BLAKE2b-256 1a1e337b2022d6933dabbe0c1ac6fbad3b003b8b96cd65f853e275ce53086007

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 6e4c87a0750f2421f655214e01413d5d7046c949c31f97019b77c25b19a8e9a7
MD5 fd85a2de690cebd46eb08ef94f76b5c1
BLAKE2b-256 56c7b714ef37257f8e9a95ef516accf322e1a91be0f67494da59bc53a6365341

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d57122889551ab458b4284b4527dbf0c365e904e215603b69d4dde5648bee617
MD5 74ccb199c26e6c2bc9f4b309f1023837
BLAKE2b-256 93a272b2bd02664b55f8a01c741c78768de17e41b31057a79ebbfa8a9eda9c2b

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 528c12d969eee4ec6549f15fb7cdcdc4debe4f3a46e0fb4f62204900c3270a30
MD5 b1835875ff0fe1f5b82135f19c76cf32
BLAKE2b-256 5232cd51c839bbe3ed3bf75bbfb267291da7f2b81849567a746709bfc706d8cb

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp310-cp310-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp310-cp310-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 adb1374159652863c37aa1a0423ac32b5964d6b030a298dee030b89dc193e226
MD5 694dafadfcabd75275edd5291ab9fd87
BLAKE2b-256 c53817d5e969672cec21bd2a362542256c8de1627c70a10e906cdf054837d86e

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 1230cc7f445022b81c7689afbab2ce0ebc5501928b0736982a2467a78dc40013
MD5 9238d252ad25835c7d8e8f3b576ac71f
BLAKE2b-256 be57fbd3aa88c05fc61b7caf4c403de2dee0e2b9d7621d27607756204d2b2a91

See more details on using hashes here.

File details

Details for the file pyteomics_biolccc-1.6.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyteomics_biolccc-1.6.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3e03146f9bf0c836918abe225ad53f10fd25ff6105b2ff0ea9cf96dd861aaf05
MD5 84f3213e6d2a2e0b0dd2f26c245e8642
BLAKE2b-256 7637ffd0bff0bbd684585a5de2be35e2e8707dabc632f51cdf37ff34d0c32aa5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page