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Imatinib_Equilibrium_Binding.py
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Imatinib_Equilibrium_Binding.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
# Read the .csv data file into Python and store the data in a DataFrame
data = pd.read_csv('example1_data.csv')
# Extract x and y data from the DataFrame
x = data.iloc[:, 0].values
y = data.iloc[:, 1].values
# Initial guess for KD (K_D) in nM
k0 = 100 # nM
# Define the fitting function
def binding_equation(x, k):
"""
Custom equation representing the fraction of bound kinase.
Parameters:
x (numpy.ndarray): Concentration of imatinib.
k (float): Equilibrium dissociation constant (KD).
Returns:
numpy.ndarray: Fraction of bound kinase.
"""
return x / (x + k)
# Fit the data to the custom equation
params, _ = curve_fit(binding_equation, x, y, p0=k0)
# Extract the fitted KD value
kFit = params[0]
# Plot the data and the fit
plt.figure()
plt.plot(x, y, 'bo', label='Data')
plt.plot(x, binding_equation(x, kFit), 'r-', label='Fit')
plt.title('Imatinib Binding Curve')
plt.xlabel('nanomolar [nM]')
plt.ylabel('fraction bound')
plt.legend()
plt.savefig('ImatinibBindingCurve')
plt.show()
# Display the equilibrium dissociation constant (KD)
print(f'Equilibrium Dissociation Constant (KD): {kFit:.4f} nM')