TDL-BP/RI-Score/GOLD/AUTODOCK-VINA
PQB2PQR, Javaplex, R-TDA, Scikit-learn
NA
Machine learning based method using the selected top poses.
No
GOLD/AutoDock Vina/Schrodinger
GOLD/AutoDock Vina/Schrodinger
(prepwizard) -propka_pH 7.5
(prepwizard) -fillsidechains -s # fillsidechains for target protein
(ligprep) -adjust_itc -ph
Maestro's prepwizard was used to optimize the protein with pH value and fillsidechains option.
Maestro's ligprep was used to generate optimized 3d structure of ligands from 2d structure. Sample ligand was manually filled into the binding pocket as the reference ligand. Position of the reference ligand was set as binding site.
autoscale = 1.5
floodfill_center = cavity_from_ligand 10 atoms
gold_fitfunc_path = plp # use plp as scoring function to generate poses
num_poses = 200 # generate 200 poses as pose_pool for each complex
Target ligands were docked to all CatS proteins available in RCSB and two protein structures provided by organizer.
Use consensus scores between plp scores (GOLD) and Vina scores (Autodock Vina) to select the best poses.
No
Yes