Hello,
I have performed MM-PBSA calculation of a protein-ligand complex. I utilised approx 750 frames (out of a total of 15000 frames) to compute the free enrgy change of binding. Then, I used a python code to compute ACF of the total delta G binding. But, the obtained ACF plot is not showing exponential decay feature exactly. I am not able to figure it out. I am attaching my plot here.
Any suggestions would be highly appreciated.
Kankana Bhattacharjee
When dealing with the auto-correlation function (ACF) of the free energy change of binding in a protein-ligand complex, achieving accurate results can be influenced by various factors. Here are some suggestions to help you address the issue:
By addressing these aspects, you can refine your approach to computing the ACF of the free energy change of binding and potentially resolve the discrepancies in your ACF plot. Additionally, considering the complex nature of protein-ligand interactions, it may be beneficial to explore alternative computational methods or conduct further experiments to validate the ACF results and enhance the overall accuracy of your analysis.
you need a dataset that contains the free energy values at different time points. Assuming you have such a dataset, here's an example Python code that calculates the auto-correlation function using the `numpy` library:
```python
import numpy as np
def auto_correlation(data):
"""
Calculates the auto-correlation function of a given dataset.
Parameters:
data (numpy array): 1D array of free energy values.
Returns:
acf (numpy array): Auto-correlation function.
"""
n = len(data)
mean = np.mean(data)
data_normalized = data - mean
acf = np.correlate(data_normalized, data_normalized, mode='full')[-n:]
acf /= (n * np.var(data))
return acf
# Example dataset (replace with your own data)
free_energy_data = np.array([1.2, 1.5, 2.1, 2.5, 2.8, 2.9, 2.7, 2.4, 2.0, 1.8])
# Calculate auto-correlation function
acf = auto_correlation(free_energy_data)
# Print the auto-correlation values
print(acf)
```
Replace as needed. Hope it helps
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