Thang Nguyen Quoc

Here are a few options:

1. **PyRosetta**: PyRosetta is a Python-based framework that provides a wide range of tools for protein structure analysis, including the ability to analyze metal coordination geometries. You can use the `rosetta.protocols.metal_geometry` module to evaluate the geometry of metal centers in your protein structure.

2. **PyMOL**: PyMOL is a popular molecular visualization and analysis software that can be used with Python. You can use the `pymol.cmd` module to access the PyMOL API and analyze the geometry of metal centers in your protein structure.

3. **BioPython**: BioPython is a set of open-source Python modules that provide tools for computational biology, including the ability to analyze protein structures. You can use the `Bio.Metal` module to analyze the geometry of metal centers in your protein structure.

4. **OpenBabel**: OpenBabel is a chemical toolbox that can be used with Python. You can use the `openbabel` module to analyze the geometry of metal centers in your protein structure.

5. **NGLView**: NGLView is a Python package that provides a high-level interface to the NGL Viewer, a web-based molecular viewer. You can use NGLView to visualize your protein structure and analyze the geometry of metal centers.

6. **MOE (Molecular Operating Environment)**: MOE is a commercial software package that provides a range of tools for molecular modeling and analysis, including the ability to analyze metal coordination geometries. You can use the MOE Python API to integrate these tools into your Python workflow.

To use any of these tools, you will need to first load your MD simulation trajectory into a Python-compatible format, such as a Numpy array or a Pandas DataFrame. You can then use the appropriate functions and modules from the tools mentioned above to analyze the geometry of the metal center in your protein structure.

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Mohammad Imam Thank you a lot for your advice. I will try these ways.

Thang Nguyen Quoc There are alternative solutions to analyze ligand-protein complex after a molecular dynamics (MD) simulation. Many popular MD programs like LAMMPS, GROMACS, and NAMD have built-in analysis tools that can calculate geometric parameters for specific atom selections. These tools might include functions to calculate bond lengths, angles, and dihedral angles around the metal center. Consult your software's documentation to explore these functionalities. Additionally, powerful Python libraries like SciPy and MDAnalysis can be valuable. SciPy offers distance calculation functions, allowing you to define and analyze bond lengths and angles surrounding the metal center. MDAnalysis, specifically designed for MD data analysis, lets you select metal atoms and calculate geometric properties like distances, angles, and dihedrals.

Analyzing Interatomic Distances and Angles:

• This method involves calculating the distances and angles between the metal atom and its surrounding atoms throughout the simulation trajectory.
• Common metallic geometries include Face-Centered Cubic (FCC), Body-Centered Cubic (BCC), and Hexagonal Close Packed (HCP). You can compare the calculated distances and angles with known values for these structures to identify the dominant metallic geometry.
• Software like OVITO or VMD can be used to visualize the atomic positions and measure distances/angles.

2. Coordination Number Analysis:

• The coordination number refers to the average number of nearest neighbors a metal atom has.
• Different metallic geometries have characteristic coordination numbers (e.g., FCC = 12, BCC = 8, HCP = 12).
• By calculating the coordination number for the metal atom at each timestep in the trajectory and averaging over time, you can identify the prevalent metallic geometry.

Additional Tools:

• Python libraries: Libraries like PyRosetta or custom scripts using tools like NumPy can be used for advanced geometric analysis of metal centers in your MD data.

Remember: The choice of method depends on the specific details you're interested in. Analyzing interatomic distances provides a more detailed picture of the local atomic arrangement, while coordination number analysis offers a simpler, statistically robust approach.