Protein ligand binding simulation in Python
Project description
PyBindingCurve
PyBindingCurve is a Python package for simulation, plotting and fitting of experimental parameters to protein-ligand binding systems at equilibrium. In simple terms, the most basic functionality allows simulation of a two species binding to each other as a function of their concentrations and the dissociation constant (KD) between the two species. A number of systems are built in and can be solved using direct analytical, kinetic, or Langrange multiplier based techniques. User-defined custom systems can also be specified using a simple syntax.
*There is a bug in Numpy 1.19.4 when running the latest Windows 10 update - see https://tinyurl.com/y3dm3h86, and https://github.com/numpy/numpy/issues/16744). To workaround on Windows 10, use conda/miniconda, or downgrade to Numpy 1.19.3 with:*
> pip install numpy==1.19.4
Installation
PyBindingCurve may be installed from source present in the GitHub repository https://github.com/stevenshave/pybindingcurve via git pull, or from the Python Package Index (https://pypi.org/project/pybindingcurve/) using the command :
pip install pybindingcurve
Requirements
PyBindingCurve was developed using python 3.7.1 but should work with any Python version 3.6 or greater. The following packages are also required
- Matplotlib (2.x)
- Numpy (1.15.x)
- lm_fit (1.0.0)
- mpmath (1.1.0)
Licence
Usage
A tutorial and API documentation can be found here
A quickstart example for simulation of protein-ligand binding is as follows:
import numpy as np
import pybindingcurve as pbc
my_system = pbc.BindingCurve("1:1")
system_parameters = {"p": np.linspace(0, 20), "l": 10, "kdpl": 1}
my_system.add_curve(system_parameters)
my_system.show_plot()
Authors
PyBindingCurve was written by Steven Shave
Please get in contact for custom solutions, integration to existing workflows and training.
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