Preparation of target and small molecules library
The 3D structure of FimA (PDB ID: 6JZK) was downloaded from Protein Data Bank (PDB) (www.rcsb.org). The structure was obtained from the X-Ray diffraction method with 2.10 resolution. The downloaded structure was prepared using Prepare Protein module of BIOVIA Discovery Studio Client v20.1.0.19295. In this module the alternate conformations were deleted, missing residues were fixed, terminal residues were adjusted and the bond orders were corrected. To generate a library of small molecules, anticancer natural compounds were retrieved from the Naturally occurring Plant based Anticancerous Compound-Activity-Target Database (NPACT) (https://webs.iiitd.edu.in/raghava/npact/index.html). The retrieved compounds were filtered based on Lipinski’s rule of five. Further, the filtered compounds were used to generate small molecules library using prepare ligand module from BIOVIA Discovery Studio Client v20.1.0.19295. Using this module tautomers and isomers were generated, bad valencies were fixed and finally, 3D coordinates of the compounds were generated.
High-Throughput Virtual Screening
To perform screening of the selected compounds, the epithelial binding domain of the FimA protein was selected as the docking site. Furthermore, the binding sphere was generated around the docking site. The Libdock module was used to perform the virtual screening. Libdock uses the hotspots method in which a grid is placed at the binding site of the protein and the hotspots map was calculated using non-polar and polar groups of residues in the active site. Furthermore, these hot spots were then applied to align the ligands rigidly to form favorable interactions with binding site residues. Using fast conformation methods ten conformations were generated for each ligand. High-quality docking with a tolerance of 0.25 was selected to dock the ligands at the binding site. And the best-docked poses were selected based on the Libdock score.
Molecular Docking
The CDOCKER module was used for the precise docking of the topmost ligands of the virtual screening. COCKER uses a charmm36 force field to perform grid-based docking. The docking was performed by keeping the receptor rigid and the ligand flexible. During the docking process, random conformations of the ligands were generated using molecular dynamics, and then the conformations were refined using the simulated annealing method. Ten random conformations followed by ten refined orientations were generated. The best docking poses were selected based on the best docking score i.e CDOCKER score (COCKER Energy, COCKER Interaction Energy). COCKER Energy scores include ligand strain energy and receptor-ligand interaction energy whereas CDOCKER Interaction Energy scores include only receptor ligand interaction energy.
Molecular Dynamic Simulation
The molecular Dynamic Simulation method was used to evaluate the stability of the protein-ligand complex. WebGro web server (https: //simlab. uams.edu/) was used to perform the MD simulation of the best-docked complex. The ligand topology file was generated from the GlycoBioChem PRODRG2 Server (http://davapc1.bioch.dundee.ac.uk/cgi-bin/ prodrg). A total of 100 ns simulation was run using GROMOS9643a1 force field and a cubic box with an SPC water model was used to solvate the system. The whole system was neutralized using 0.15 M salt of NaCl. Steepest descent energy minimization with steps of 5,000 has been chosen to minimize the complex structure. The equilibration step was performed at a temperature of 300 K and pressure bar of 1, MD integrator of leap-frog. A total of 1,000 frames have been generated during the whole simulation process.