Structure-Based
Virtual Screening Approach for Discovery
of Covalently Bound Ligands
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Abstract
We present a fast and effective covalent
docking approach suitable
for large-scale virtual screening (VS). We applied this method to
four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with
known crystal structures and known covalent inhibitors. We implemented
a customized “VS mode” of the Schrödinger Covalent
Docking algorithm (CovDock), which we refer to as CovDock-VS. Known
actives and target-specific sets of decoys were docked to selected
X-ray structures, and poses were filtered based on noncovalent protein–ligand
interactions known to be important for activity. We were able to retrieve
71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease,
and EGFR within 5% of the decoy library, respectively. With the more
challenging XPO1 target, where no specific interactions with the protein
could be used for postprocessing of the docking results, we were able
to retrieve 95% of the actives within 30% of the decoy library and
achieved an early enrichment factor (EF1%) of 33. The poses of the
known actives bound to existing crystal structures of 4 targets were
predicted with an average RMSD of 1.9 Å. To the best of our knowledge,
CovDock-VS is the first fully automated tool for efficient virtual
screening of covalent inhibitors. Importantly, CovDock-VS can handle
multiple chemical reactions within the same library, only requiring
a generic SMARTS-based predefinition of the reaction. CovDock-VS provides
a fast and accurate way of differentiating actives from decoys without
significantly deteriorating the accuracy of the predicted poses for
covalent protein–ligand complexes. Therefore, we propose CovDock-VS
as an efficient structure-based virtual screening method for discovery
of novel and diverse covalent ligands