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The final step consists of rescoring the poses produced by the docking algorithms using AB DockSorter.
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For each target in our test sets, we have the raw list of top N decoys as ordered by the docking algorithm and our rescored version thereof.
It uses a combination of energy, conservation, and accessibility scores to rank the models that are generated by the docking algorithm(s).
The input to AB DockSorter consists of a single antibody antigen pose supplied by the docking algorithm, a set of Chothia CDR residues and a set of residues for one of the predicted epitopes.
The virtual screening step presents also some data management issues: although the size of the output is not huge, the number of compounds in analysis, the number of simulations performed, and the number of different conformations between the proteins and the ligands provided by the docking algorithm usually require the use of a relational database to be managed.
The docking algorithms of Glide SP and XP, together with the Lamarckian genetic algorithm were employed for consensus docking.
For comparison and validation of the docking algorithms, docking simulations were performed also with palmitate.
Docking parameter file on the other hand contains information about the docking algorithm to be used.
Using the DUD benchmark, the docking algorithm achieved a median AUC value of 0.8717.
For each ligand, we tested how well the docking algorithm was able to rank drugs when docked into their known targets.
For Auto Dock, Lamarckian Genetic Algorithm was employed as the docking algorithm.
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