# glm_mda_diffusion
## or *Globule-Linker-Model, Minimum-Dissipation-Approximation diffusion coefficient calculator*
Minimum dissipation approximation is a fast algorithm for predicting the diffusive properties of intrinsically disordered proteins.
# Installation
```bash
python3 -m pip install glm_mda_diffusion
```
# Usage as module
Basic usage:
```bash
python3 -m glm_mda_diffusion --sequence MGSS[HHHHHH]SSGLVPR
```
Sample output:
```
Computed GLM-MDA hydrodynamic radius [Ang]:
12.279165209438174
```
# Usage as package
Basic usage
```Python
import glm_mda_diffusion
glm_mda_diffusion.hydrodynamic_radius(sequence = "MGSS[HHHHHH]SSGLVPR")
```
Advanced usage (all options displayed with default values).
Options `steric_radius` and `hydrodynamic_radius` controll linker properties, while `effective_density` and `hydrdation_thickness` controll globular region properties.
```Python
import glm_mda_diffusion
glm_mda_diffusion.protein_hydrodynamic_radius(
sequence="MGSS[HHHHHH]SSGLVPR",
steric_radius=1.9025, # Ang
hydrodynamic_radius=4.2, # Ang
effective_density=0.52, # Da / Ang^3
hydration_thickness=3.0, # Ang
ensemble_size=30,
bootstrap_rounds=10,
aminoacid_masses={
"A": 71.08,
"C": 103.14,
"D": 115.09,
"E": 129.12,
"F": 147.18,
"G": 57.06,
"H": 137.15,
"I": 113.17,
"K": 128.18,
"L": 113.17,
"M": 131.21,
"N": 114.11,
"P": 97.12,
"Q": 128.41,
"R": 156.2,
"S": 87.08,
"T": 101.11,
"V": 99.14,
"W": 186.21,
"Y": 163.18,
"Z": 0,
"O": 0,
"U": 0,
"J": 0,
"X": 0,
"B": 0,
}, # Da,
)
```
# License
This software is licensed under GPLv3 License
Copyright (c) Radost Waszkiewicz (2023).
# How to cite
*Minimum dissipation approximation: A fast algorithm for the prediction of diffusive properties of intrinsically disordered proteins.* Radost Waszkiewicz, Agnieszka Michaś, Michał K. Białobrzewski, Barbara P. Klepka, Maja K. Cieplak-Rotowska, Zuzanna Staszałek, Bogdan Cichocki, Maciej Lisicki, Piotr Szymczak, and Anna Niedźwiecka; J. Phys. Chem. Lett. (submitted 2023)
# Bibliography
- *Diffusion coefficients of elastic macromolecules.* B. Cichocki, M. Rubin, A. Niedzwiecka, and P. Szymczak; J. Fluid Mech. (2019)
- *GRPY: An Accurate Bead Method for Calculation of Hydrodynamic Properties of Rigid Biomacromolecules.* P. Zuk, B. Cichocki, and P. Szymczak; Biophysical Journal (2018)
- *Pychastic: Precise Brownian dynamics using Taylor-Ito integrators in Python.* R. Waszkiewicz, M. Bartczak, K. Kolasa, and M. Lisicki; SciPost Phys. Codebases (2023)
Raw data
{
"_id": null,
"home_page": "https://github.com/RadostW/glm-mda-diffusion/",
"name": "glm-mda-diffusion",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "",
"author": "Radost Waszkiewicz",
"author_email": "radost.waszkiewicz@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/b5/56/03a3f16d34e92dedbb5044ff592cfb95e79454b759e86a3e8b3d95e64f64/glm_mda_diffusion-1.1.tar.gz",
"platform": null,
"description": "# glm_mda_diffusion\n\n## or *Globule-Linker-Model, Minimum-Dissipation-Approximation diffusion coefficient calculator*\n\n\n\nMinimum dissipation approximation is a fast algorithm for predicting the diffusive properties of intrinsically disordered proteins.\n\n# Installation\n\n```bash\npython3 -m pip install glm_mda_diffusion\n```\n\n# Usage as module\n\nBasic usage:\n\n```bash\npython3 -m glm_mda_diffusion --sequence MGSS[HHHHHH]SSGLVPR\n```\n\nSample output:\n```\nComputed GLM-MDA hydrodynamic radius [Ang]:\n12.279165209438174\n```\n\n# Usage as package\n\nBasic usage\n\n```Python\nimport glm_mda_diffusion\nglm_mda_diffusion.hydrodynamic_radius(sequence = \"MGSS[HHHHHH]SSGLVPR\")\n```\n\nAdvanced usage (all options displayed with default values).\n\nOptions `steric_radius` and `hydrodynamic_radius` controll linker properties, while `effective_density` and `hydrdation_thickness` controll globular region properties.\n\n```Python\nimport glm_mda_diffusion\n\nglm_mda_diffusion.protein_hydrodynamic_radius(\n sequence=\"MGSS[HHHHHH]SSGLVPR\",\n steric_radius=1.9025, # Ang\n hydrodynamic_radius=4.2, # Ang\n effective_density=0.52, # Da / Ang^3\n hydration_thickness=3.0, # Ang\n ensemble_size=30,\n bootstrap_rounds=10,\n aminoacid_masses={\n \"A\": 71.08,\n \"C\": 103.14,\n \"D\": 115.09,\n \"E\": 129.12,\n \"F\": 147.18,\n \"G\": 57.06,\n \"H\": 137.15,\n \"I\": 113.17,\n \"K\": 128.18,\n \"L\": 113.17,\n \"M\": 131.21,\n \"N\": 114.11,\n \"P\": 97.12,\n \"Q\": 128.41,\n \"R\": 156.2,\n \"S\": 87.08,\n \"T\": 101.11,\n \"V\": 99.14,\n \"W\": 186.21,\n \"Y\": 163.18,\n \"Z\": 0,\n \"O\": 0,\n \"U\": 0,\n \"J\": 0,\n \"X\": 0,\n \"B\": 0,\n }, # Da,\n)\n```\n\n\n# License\n\nThis software is licensed under GPLv3 License\n\nCopyright (c) Radost Waszkiewicz (2023).\n\n# How to cite\n\n*Minimum dissipation approximation: A fast algorithm for the prediction of diffusive properties of intrinsically disordered proteins.* Radost Waszkiewicz, Agnieszka Micha\u015b, Micha\u0142 K. Bia\u0142obrzewski, Barbara P. Klepka, Maja K. Cieplak-Rotowska, Zuzanna Stasza\u0142ek, Bogdan Cichocki, Maciej Lisicki, Piotr Szymczak, and Anna Nied\u017awiecka; J. Phys. Chem. Lett. (submitted 2023)\n\n# Bibliography\n\n- *Diffusion coefficients of elastic macromolecules.* B. Cichocki, M. Rubin, A. Niedzwiecka, and P. Szymczak; J. Fluid Mech. (2019)\n\n- *GRPY: An Accurate Bead Method for Calculation of Hydrodynamic Properties of Rigid Biomacromolecules.* P. Zuk, B. Cichocki, and P. Szymczak; Biophysical Journal (2018)\n\n - *Pychastic: Precise Brownian dynamics using Taylor-Ito integrators in Python.* R. Waszkiewicz, M. Bartczak, K. Kolasa, and M. Lisicki; SciPost Phys. Codebases (2023)\n\n\n",
"bugtrack_url": null,
"license": "GNU GPLv3",
"summary": "Minimum dissipation approximation: A fast algorithm for the prediction of diffusive properties of intrinsically disordered proteins",
"version": "1.1",
"project_urls": {
"Documentation": "https://github.com/RadostW/glm_mda_diffusion",
"Homepage": "https://github.com/RadostW/glm-mda-diffusion/",
"Source": "https://github.com/RadostW/glm_mda_diffusion"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "c154922115dfe270c4b74c8514e74000b159386754be2d8c9cfc2bad1703d070",
"md5": "ee46d35629cb562dd208e67e2cdcdba4",
"sha256": "d7b9b485bee17f7065b240015b8a81bba3cc0281fe052bba6b5a9a098f89df71"
},
"downloads": -1,
"filename": "glm_mda_diffusion-1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "ee46d35629cb562dd208e67e2cdcdba4",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 18175,
"upload_time": "2024-01-05T09:48:34",
"upload_time_iso_8601": "2024-01-05T09:48:34.941219Z",
"url": "https://files.pythonhosted.org/packages/c1/54/922115dfe270c4b74c8514e74000b159386754be2d8c9cfc2bad1703d070/glm_mda_diffusion-1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b55603a3f16d34e92dedbb5044ff592cfb95e79454b759e86a3e8b3d95e64f64",
"md5": "466ed951d2a91b34496e20f6d09f5967",
"sha256": "d283b967256aec6fb3f9f5a6ff2eae72fc09e0089a7ef2f357c20d7c8a832f3d"
},
"downloads": -1,
"filename": "glm_mda_diffusion-1.1.tar.gz",
"has_sig": false,
"md5_digest": "466ed951d2a91b34496e20f6d09f5967",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 17116,
"upload_time": "2024-01-05T09:48:36",
"upload_time_iso_8601": "2024-01-05T09:48:36.781534Z",
"url": "https://files.pythonhosted.org/packages/b5/56/03a3f16d34e92dedbb5044ff592cfb95e79454b759e86a3e8b3d95e64f64/glm_mda_diffusion-1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-05 09:48:36",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "RadostW",
"github_project": "glm-mda-diffusion",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "numpy",
"specs": []
},
{
"name": "pygrpy",
"specs": []
},
{
"name": "sarw_spheres",
"specs": []
},
{
"name": "scipy",
"specs": []
}
],
"lcname": "glm-mda-diffusion"
}
JSON
