Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach
June 23rd, 2026
Here, we present a protocol for the structure-guided design of protein-ligand binding interaction between Sanglifehrin A and Cyclophilin A/Ganoderiol-F, essential in discovering new drugs. The stability of the ligand-receptor complex was evaluated using molecular dynamics simulation.
This research focus on designing novel Cyclophilin A inhibitors and gan-it-role antiproliferative to target the Cyclophilin DCDK axis and inhibit cell cancer proliferation. Key challenge include achieving optimal ligand-receptor geometric interaction and maintaining a stable ligand-protein complexes under dynamic experimental and physiological condition. To begin, open the 1NMK"file.
Locate the amino acid arginine"at position number 55, and record the X, Y, and Z coordinates in the AutoDock"interface. Open the File"menu, select Read Molecules. Choose the file 1NMK"and click Open.
Add hydrogen atoms by selecting Edit, choosing Hydrogen, and clicking Add. Then choose Polar Only"and confirm with OK.Then go to the Edit"menu again, choose Atoms, and select Assign AD4 Atom Type. Next, choose Charges"and select Kollman Charges.
Save the prepared receptor as 1NMK. pdbqt"by selecting File, choosing Save, setting the file type, and confirming the save location. Next, open the Ligand"menu, select Input.
Click Open, choose the PDB file, and click Open"again. When the information window appears, review the ligand details. To detect the ligand root, select Ligand, then choose Torsion Tree"and click Detect Root.
Save the ligand by selecting Ligand, choosing Output, and clicking Save as PDBQT. Prepare the receptor grid by selecting Grid, choosing Macromolecule, selecting 1NMK, and clicking Molecule. Confirm the warning and save as 1NMK.pdbqt.
For setting the map types, select Grid, followed by Set Map Types, Ligand. Choose Ligand"and finally select Ligand"again. Then define the grid box center by selecting Grid, choosing Grid Box, and clicking Center.
Then select an atom and enter the grid box coordinates. Save the grid parameter as a GPF file. Configure docking by selecting Docking, followed by Macromolecule"and Set Rigid File Name.
Navigate to the required folder and click on 1NMK.pdbqt. to open it, select Docking, choose Output, click Lamarckian GA, and save the docking parameter file as 1NMK.dpf. In the Cygwin"terminal, type cd C:and press enter;then type cd project"and press enter again.
Next, type cd molecules"and press enter. Then type cd 3"and press enter. Type the AutoGrid"command to generate grid maps and press enter to execute AutoGrid.
After the execution, type tail f 1NMK. glg"and press enter to view the log progress, type the command for molecular docking simulation. To generate the docking log file, and press enter to start AutoDock.
Then, type tail f 1NMK. dlg"and press enter to monitor the docking process. After creating all DLG files, go to folder 1 and open the DLG file in WordPad, press Ctrl+F.
Type the search keyword and press enter three times to reach the RMSD table. Copy the binding energy and corresponding run information for each molecule from the RMSD table, compile the top 20 molecules in a spreadsheet with columns created for serial number, minimum binding energy, and run. In the Cygwin"terminal, navigate to the specific molecules folder within the project directory.
Type the command to extract docked ligand coordinates from DLG and trim the data. Press enter once more to finalize, confirming the creation of the 1NMK run PDBQT"file. Verify that the 1NMK run PDB"file is created in the same folder.
Next, open the PyMOL 2.5"software and the AutoDock"from the desktop, and open the receptor file to select the receptor structure for visualization. Open the corresponding ligand configuration file for the chosen molecule and display the loaded ligand in the 3D workspace. Select Hide Everything"from the menu, import the file to Maestro"to visualize the structure, and then click on Ligand Interaction"to get the ligand interaction diagram.
Open the UCSF Chimera"software from the desktop. Select File, followed by Open. Navigate to drive C, project, and molecules.
Select the file 1NMK. pdb"and click Open. From the menu, select Residue"and choose UNL Zoom"to focus on the UNL residue region.
Select Zone. Then press OK"to confirm. Next, select Action.
Choose Label, then select Residue"and choose Name Plus Specifier"to label the residues. Open Tools, select Structure Analysis, and then click FindHBond. In the default FindHBond"settings window, check the boxes for Color H-bonds That Do Not Meet Precise Criteria"and Only Find H-bonds with Selected.
Clear the zone selection to begin a new survey. Then select the zone parameters and zoom into the molecular display, and observe the blue and orange lines representing hydrogen bonds. Finally, examine the visualized structure and count the total number of visible hydrogen bonds.
The docking confirmation of the ligand-receptor complex was visualized using PyMOL 2.5, showing 109 hydroxy six hydroxyethyl bound within the active pocket of the 1NMK"receptor. The 4-hydroxymandelic acid ligand formed hydrogen bonds with the 1NMK"receptor, particularly with arginine 55 at a distance of 1.53 angstroms. Two-dimensional structural analysis illustrated the amino acids with which the ligands interacted through hydrogen bonding.
Molecular dynamics simulations confirm the stability of the ligand-receptor complex through the analysis of the radius of gyration over 1000 picoseconds. The RMSD graph showed consistent stabilization of the complex after one nanosecond of simulation, indicating steady protein-ligand binding. The average density of the system was approximately 1019 kilograms per cubic meter, confirming equilibrium under NVT conditions, and the pressure of the system remains stable during NPT equilibration for 100 picoseconds.
The temperature stabilized around 300 kelvin during the NVT phase, confirming successful thermal equilibration. The ligand-protein complex trajectory box was visualized after the simulation, showing the distribution of water molecules surrounding the docked complex. Few studies investigate gan-it-role antiproliferative as an oncogenic therapeutic target.
This protocol addresses that gap by designing and validating inhibitor using molecular docking and dynamics analysis. Our protocol interrogate multiple computational tool into a unified workflow, ensuring comprehensive ligand-protein or receptor analysis. Our future research will focus on validating these inhibitor in vitro and in vivo by exploring border application of Cyclophilin A inhibitors across multiple cancer cell type.
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Overview
This study focuses on the design and validation of novel Cyclophilin A inhibitors targeting the Cyclophilin D-CDK axis to suppress cancer cell proliferation. Using a structure-guided approach, researchers developed 117 ligand molecules and evaluated their interactions with the Sanglifehrin A enzyme receptor (1NMK) through molecular docking and dynamics simulations. The workflow integrated multiple computational tools to optimize ligand-receptor binding, assess stability, and identify key interacting residues for potential anticancer drug development.
Key Study Components
Area of Science
- Cancer pharmacology
- Computational drug design
- Molecular docking and simulation
Background
- Cyclophilin A (Cyp A) is implicated in cancer progression through regulation of proliferation and cell survival.
- The Cyclophilin D-CDK axis represents a promising therapeutic target in oncology.
- Ganoderiol F derivatives have shown potential as oncogenic targets across multiple cancer types.
- Accurate prediction of ligand-receptor interactions requires robust computational modeling.
Purpose of Study
- To design novel Cyclophilin A inhibitors using structure-guided methods.
- To evaluate ligand-receptor binding affinity and geometric compatibility via docking.
- To confirm complex stability through molecular dynamics simulations.
- To identify key amino acid residues involved in hydrogen bonding with lead ligands.
Methods Used
- Chemsketch for constructing 117 novel cyclophilin inhibitor ligand molecules.
- AutoDock 4.2.6 for ligand-protein docking to determine optimal binding geometries.
- PyMOL 2.5 for visualizing docking poses and ligand-receptor interactions.
- Chimera and Maestro for detecting and analyzing hydrogen bonds.
- Gromacs with CHARMM 36 force field for molecular dynamics simulation of the ligand-receptor complex.
Main Results
- Arginine 55 in the 1NMK receptor formed a hydrogen bond with the ligand at a distance of 1.53 Å.
- Key interacting residues included Asn, Gly, Thr, Lys, Ile, His, Phe, and Cys.
- The ligand formed 45 hydrogen bonds with the receptor, indicating strong binding potential.
- The docking box was centered at X=38.86, Y=10.987, Z=40.734 in the 1NMK receptor.
- MD simulation in Gromacs showed stabilization of the complex after 1 ns, with consistent RMSD and radius of gyration.
- The system maintained equilibrium under NVT/NPT conditions, with average density ~1019 kg/m³ and stable pressure and temperature (~300 K).
Conclusions
- The designed ligand-receptor complex demonstrates sufficient stability for further development as a cancer therapeutic.
- Arginine 55 plays a critical role in ligand binding within the active site of Cyclophilin A.
- The integrated computational workflow successfully identifies promising Cyp A inhibitors.
- Future work will focus on in vitro and in vivo validation across multiple cancer cell types.
