2022 Denovo tutorial 1 with PDBID 6ME2
This article is a continuation of PDBID 6ME2, with the previous one detailing how to run a virtual screen on this protein. This article will detail how to run a focused de novo design experiment on 6ME2.
Contents
I. De novo design
- Define and describe difference between focused and generic de novo design*
Generating a fragment library
Focused de novo growth
Now, it is time to create ligands that should fit well into the appropriate region of the receptor. This is done by combining the fragments generated in the previous step, taking into account charge, molecular weight, and other constraints.
Create a directory called 011_dn_focus and cd into it:
cd 011_dn_focus
Now create the following input file:
vi dn_focus.in
And write the following into the dn_focus.in file:
conformer_search_type denovo dn_fraglib_scaffold_file ../010_dn_fraglib/fraglib_scaffold.mol2 dn_fraglib_linker_file ../010_dn_fraglib/fraglib_linker.mol2 dn_fraglib_sidechain_file ../010_dn_fraglib/fraglib_sidechain.mol2 dn_user_specified_anchor no dn_use_torenv_table yes dn_torenv_table ../010_dn_fraglib/fraglib_torenv.dat dn_sampling_method graph dn_graph_max_picks 30 dn_graph_breadth 3 dn_graph_depth 2 dn_graph_temperature 100.0 dn_pruning_conformer_score_cutoff 100.0 dn_pruning_conformer_score_scaling_factor 1.0 dn_pruning_clustering_cutoff 100.0 dn_constraint_mol_wt 550.0 dn_constraint_rot_bon 15 dn_constraint_formal_charge 2.0 dn_heur_unmatched_num 1 dn_heur_matched_rmsd 2.0 dn_unique_anchors 1 dn_max_grow_layers 9 dn_max_root_size 25 dn_max_layer_size 25 dn_max_current_aps 5 dn_max_scaffolds_per_layer 1 dn_write_checkpoints yes dn_write_prune_dump no dn_write_orients no dn_write_growth_trees no dn_output_prefix dn_focus.out use_internal_energy yes internal_energy_rep_exp 12 internal_energy_cutoff 100.0 use_database_filter no orient_ligand yes automated_matching yes receptor_site_file ../002.surface_spheres/selected_spheres.sph max_orientations 1000 critical_points no chemical_matching no use_ligand_spheres no bump_filter no score_molecules yes contact_score_primary no contact_score_secondary no grid_score_primary yes grid_score_secondary no grid_score_rep_rad_scale 1 grid_score_vdw_scale 1 grid_score_es_scale 1 grid_score_grid_prefix ../003_gridbox/grid multigrid_score_secondary no dock3.5_score_secondary no continuous_score_secondary no footprint_similarity_score_secondary no pharmacophore_score_secondary no descriptor_score_secondary no gbsa_zou_score_secondary no gbsa_hawkins_score_secondary no SASA_score_secondary no amber_score_secondary no minimize_ligand yes minimize_anchor yes minimize_flexible_growth yes use_advanced_simplex_parameters no simplex_max_cycles 1 simplex_score_converge 0.1 simplex_cycle_converge 1.0 simplex_trans_step 1.0 simplex_rot_step 0.1 simplex_tors_step 10.0 simplex_anchor_max_iterations 500 simplex_grow_max_iterations 500 simplex_grow_tors_premin_iterations 0 simplex_random_seed 0 simplex_restraint_min no atom_model all vdw_defn_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/vdw_AMBER_parm99.defn flex_defn_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/flex.defn flex_drive_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/flex_drive.tbl
To start the de novo growth process, run dock6 using this input file by typing:
dock6 -i dn_focus.in -o dn_focus.out
Focused de novo rescore
Now that we have generated our molecules from the de novo growth, we can rescore them based on different characteristics of the molecules in order to get a better idea of which ones are better for in vitro binding. In order to do this, make a directory called 012_dn_rescore and cd into it. We now need to generate an input file and a submit file. For the input file, vi into dn_rescore.in and write the following to that file:
conformer_search_type rigid use_internal_energy yes internal_energy_rep_exp 12 internal_energy_cutoff 100.0 ligand_atom_file ../58.dn_focus/dn_focus.out.denovo_build.mol2 limit_max_ligands no skip_molecule no read_mol_solvation no calculate_rmsd no use_database_filter no orient_ligand no bump_filter no score_molecules yes contact_score_primary no contact_score_secondary no grid_score_primary no grid_score_secondary no multigrid_score_primary no multigrid_score_secondary no dock3.5_score_primary no dock3.5_score_secondary no continuous_score_primary no continuous_score_secondary no footprint_similarity_score_primary no footprint_similarity_score_secondary no pharmacophore_score_primary no pharmacophore_score_secondary no descriptor_score_primary yes descriptor_score_secondary no descriptor_use_grid_score no descriptor_use_multigrid_score no descriptor_use_continuous_score no descriptor_use_footprint_similarity yes descriptor_use_pharmacophore_score yes descriptor_use_tanimoto yes descriptor_use_hungarian yes descriptor_use_volume_overlap yes descriptor_fps_score_use_footprint_reference_mol2 yes descriptor_fps_score_footprint_reference_mol2_filename ../53.dock/6ME2.lig.min_scored.mol2 descriptor_fps_score_foot_compare_type Euclidean descriptor_fps_score_normalize_foot no descriptor_fps_score_foot_comp_all_residue yes descriptor_fps_score_receptor_filename ../50.structure/6ME2.receptor_wH.mol2 descriptor_fps_score_vdw_att_exp 6 descriptor_fps_score_vdw_rep_exp 12 descriptor_fps_score_vdw_rep_rad_scale 1 descriptor_fps_score_use_distance_dependent_dielectric yes descriptor_fps_score_dielectric 4.0 descriptor_fps_score_vdw_fp_scale 1 descriptor_fps_score_es_fp_scale 1 descriptor_fps_score_hb_fp_scale 0 descriptor_fms_score_use_ref_mol2 yes descriptor_fms_score_ref_mol2_filename ../53.dock/6ME2.lig.min_scored.mol2 descriptor_fms_score_write_reference_pharmacophore_mol2 no descriptor_fms_score_write_reference_pharmacophore_txt no descriptor_fms_score_write_candidate_pharmacophore no descriptor_fms_score_write_matched_pharmacophore no descriptor_fms_score_compare_type overlap descriptor_fms_score_full_match yes descriptor_fms_score_match_rate_weight 5.0 descriptor_fms_score_match_dist_cutoff 1.0 descriptor_fms_score_match_proj_cutoff 0.7071 descriptor_fms_score_max_score 20 descriptor_fingerprint_ref_filename ../53.dock/6ME2.lig.min_scored.mol2 descriptor_hms_score_ref_filename ../53.dock/6ME2.lig.min_scored.mol2 descriptor_hms_score_matching_coeff -5 descriptor_hms_score_rmsd_coeff 1 descriptor_volume_score_reference_mol2_filename ../53.dock/6ME2.lig.min_scored.mol2 descriptor_volume_score_overlap_compute_method analytical descriptor_weight_fps_score 1 descriptor_weight_pharmacophore_score 1 descriptor_weight_fingerprint_tanimoto -1 descriptor_weight_hms_score 1 descriptor_weight_volume_overlap_score -1 gbsa_zou_score_secondary no gbsa_hawkins_score_secondary no SASA_score_secondary no amber_score_secondary no minimize_ligand no atom_model all vdw_defn_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/vdw_AMBER_parm99.defn flex_defn_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/flex.defn flex_drive_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/flex_drive.tbl chem_defn_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/chem.defn pharmacophore_defn_file /gpfs/projects/AMS536/zzz.programs/dock6.9_release/parameters/ph4.defn ligand_outfile_prefix descriptor.output write_footprints yes write_hbonds yes write_orientations no num_scored_conformers 1 rank_ligands no
Once you have finished, vi into dn_rescore.sh and write the following to that file:
#!/bin/bash #SBATCH --time=48:00:00 #SBATCH --nodes=1 #SBATCH --ntasks=28 #SBATCH --job-name=dn_rescore #SBATCH --output=dn_rescore.out #SBATCH -p long-28core
cd $SLURM_SUBMIT_DIR echo "starting Dock6.9 simulation" /gpfs/projects/AMS536/zzz.programs/dock6.9_release/bin/dock6.mpi -i dn_rescore.in -o dn_rescore.out
To submit this job, type the following into the terminal:
dock6 -i rescore.in -o rescore.out
When this finishes, you will generate a couple of files, the most important of which is the descriptor.output_scored.mol2 file. Move that to the comupter you are using Chimera on and load that file into Chimera using ViewDock as described in the virtual screen tutorial. It is also a good idea to load in the native substrate to see how these new ligands compare to the original.
The first two images show the top scored and 2nd highest scored ligands with the receptor and the third image shows all the rescored ligands at the same time. There's a lot of variability in where any of these ligands can be docked, but the top two ligands seem to have been docked in the same part of the binding pocket.
