AMECovDock - Automated Modeling Engine for COVelent DOCKing

This pipeline contains all the components needed to automate covalent docking of specified electrophilic warheads to a receptor CYS sidechain of the protein target. Given a protein target and a list of SMILES, the pipeline forms a covalent bond between the thiol of the CYS sidechain and the pertinent functional group(s) of a ligand, and then determines the energetically favorable poses of the ligand!

In a covalently bound ligand, there are three atoms shared between the receptor and the ligand. It includes a common anchor atom in the receptor that connects with the ligand’s atoms. You can see both connected atoms in the PDBQT files of both the receptor and the ligand as well. This package holds everything needed to automate AutoDockFR (ADFR) [1-2]. For receceptor preparation follow the tutorial https://ccsb.scripps.edu/adfr/tutorial-covalent/. ] We have curated covalent antiviral data sets with desired scaffold using 3D-Scaffld deep learning framework https://github.com/PNNL-CompBio/3D_Scaffold [3].

Currently Supported Warhead Reactions

acrylamides

alkynes*

chloroacetamides

epoxyKetone_alphaSub

epoxyKetone_betaSub

esters

haloKetone_SN2

vinylmethyl ethers

vinylsulfones

OUTPUT

./Res_Dock/ for ease of comparison and visualization of results receptorName_ligandName_reactionName_counter_jobName_out.pdbqt - the docked ligand scores.txt - list of docked ligands sorted by score

./output/ additional information about docked ligands ./output/ligandName_reactionName_counter/ .dro file - from ADFR summary.dlg file - from ADFR atoms.txt - atom numbers chosen for docking, for additional validation

./pdb/ for ease of re-docking and input validation .pdb - result of AMECovDock_Reactions.py .pdbqt - result of adfr prepare_ligand

ENVIRONMENT - Constance

module load python/anaconda3.2019.3 source /share/apps/python/anaconda3.2019.3/etc/profile.d/conda.sh conda activate my-rdkit-env module load gcc/6.1.0 module load openbabel/2.4.1

DEPENDENCIES

ADFR - http://adfr.scripps.edu/AutoDockFR/downloads.html anaconda rdkit - https://www.rdkit.org/docs/Install.html gcc - used on Constance for compatibility with slurm

NOTES - SETUP

only 2 files need to be provided {ligand_file}.smi 2 columns, no header: SMILES ID {receptor}_cov.trg for example run this command on your receptor.pdbqt (6wqf in this case) agfr -r 6wqf.pdbqt -b user -17.00 -5.00 15.00 40.00 40.00 40.00 -c 1379 1382 -t 1377 -x A:CYS145 -o 6wqf_cov

in AMECovDock_ADFR.sh modify lines 14- 16 - receptor code, {ligand_file} base name, /path/to/ADFR/Installation/bin/ modify lines 42-45 - Residue Number in AMECovDock_Reactions.py modify line 99 - {ligand_file}.smi goes in supplier modify line 223 - SetResidueNumber

resulting files show receptorName - for keeping track of what receptorthe ligand was docked to ligandName - for referencing ligand with original input reactionName - for easily identifying which warhead reacted with the receptor, may also be used to filter results according to interest counter - when a reaction produces multiple unique products, the counter prevents them from overwriting

TO RUN

./AMECovDock_ADFR.sh

References

1. Zhao, Y., Stoffler, D., & Sanner, M. (2006). Hierarchical and multi-resolution representation of protein flexibility. Bioinformatics, 22(22), 2768-2774.
2. Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J., & Sanner, M. F. (2015). AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS computational biology, 11(12), e1004586.
3. Joshi R, Gebauer N, Bontha M, Khazaieli M, James RM, Brown JB, Kumar N*. 3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Antiviral Candidates with Desired Scaffolds. Phys Chem B. 2021. doi: 10.1021/acs.jpcb.1c06437.