Though many existing covalent docking tools are successful, they have several limitations. They require time-consuming tasks such as pre-processing of ligands (standardization, filtering based on warheads type), setting up of reactions, protein preparation, complex formation, etc... They can only be used to dock a small library of compounds due to the high CPU time needed for performing covalent computations and scoring, rendering them unsuitable for performing large-scale virtual screening. We developed a toolkit for performing automated covalent docking in a fast and effective manner by combining GOLD, MOE, KNIME, and Python programs. In the first step, a KNIME workflow was developed to perform ligand preparation in a manner compatible with the covalent docking protocol of GOLD. The protein structures were prepared using the MOE QuickPrep module. Subsequently, a Python program was written to perform the covalent docking of ligands, by invoking the GOLD docking engine, in a parallelized fashion. Finally, the protein-ligand complexes were generated for the docking poses by calling a submodule of the Python program. We applied this toolkit retrospectively on six targets (NUDT7, OTUB2, EGFR, Cathepsin K, XPO1, and HCV NS3 protease) with known crystal structures and sets of active and inactive covalent inhibitors and assessed the potential of four GOLD docking scoring functions (GoldScore, ASP, ChemScore, and PLP). For most of the targets, the virtual screening metrics were satisfactory for the different scoring functions, demonstrating the program's usefulness in performing prospective virtual screening.
The KNIME workflow “Evotec_Covalent_Processing_forGOLD.knwf” for the preparation of the ligands is available in the KNIME Hub https://hub.knime.com/emilie_pihan/spaces. The Python program is available at https://gitlab.com/seb-buch/covalent_docking_helper.