2022 Denovo tutorial 2 with PDBID 4ZUD
De Novo Design
De novo design refers to the process of generating novel ligands in an effort to identify molecules of physiological significance that can be further optimized to become approved drug molecules. The synthesis of thousands of potential drug molecules are done experimentally daily, but with computers, millions of molecules can be computationally modelled and pre-selected for possible synthesis in a fraction of the time it would take to test all possible molecules solely experimentally. With this, scientists are able to direct their attention towards molecules that have the highest probability of imparting a therapeutic effect upon binding to a respective receptor.
This tutorial is the second part of the 2022 DOCK tutorial 2 with PDBID 4ZUD tutorial. You will need the files created in that tutorial to continue with this one!
Make a new directory to organize the files generated in this tutorial:
Fragment Library Generation
To create new molecules, we need to begin with the building blocks. For the purposes of speed, we most often use pre-defined molecular fragments that can be arranged/attached in a variety of orientations to create unique structures. Since we have the structure of a ligand that is known to bind the 4ZUD protein, we can generate fragments from that molecule to increase the probability of creating molecules with similar properties to the known ligand.
In an input file:
Insert the following:
conformer_search_type flex write_fragment_libraries yes fragment_library_prefix fraglib fragment_library_freq_cutoff 1 fragment_library_sort_method freq fragment_library_trans_origin no use_internal_energy yes internal_energy_rep_exp 12 internal_energy_cutoff 100.0 ligand_atom_file ../001.structure/4ZUD_ligand_hydrogens.mol2 limit_max_ligands no skip_molecule no read_mol_solvation no calculate_rmsd no 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 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 ligand_outfile_prefix fragment.out write_orientations no num_scored_conformers 1 rank_ligands no
Run the fragment generation with the following command:
dock6 -i fragment.in -o fragment.out
DOCK should generate six files; three of those files should be mol2's of linker, scaffold, and side chain fragments. You can extract the number of fragments present in each file by running:
grep -wc MOLECULE *.mol2
Focused De Novo Growth
Now we can use the fragments generated in the previous step to allow DOCK to make new molecules that fall within certain rotamers, molecular weights, charges and chemical group compositions. DOCK will generate all possible molecules within the given parameters and that fall within reasonable internal energies based on the AMBER van der Waals equation.
In a new directory:
In an input file:
Insert the following:
conformer_search_type denovo dn_fraglib_scaffold_file ../fraglib_scaffold.mol2 dn_fraglib_linker_file ../fraglib_linker.mol2 dn_fraglib_sidechain_file ../fraglib_sidechain.mol2 dn_user_specified_anchor no dn_use_torenv_table yes dn_torenv_table ../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
Run this calculation with the following command:
dock6 -i dn_focus.in -o dn_focus.out
A few files will be generated, but the main file with all of the de novo molecules in a mol2 format is dn_focus.out.denovo_build.mol2. If you look at the dn_focus.out file you can see the number of molecules created which should be around 23,000.
Focused De Novo Rescored
Although we've generated a variety of new molecules, all composed of the 4ZUD native ligand fragments, they were created under the energetic constraints of an in silico environment, thus although these new molecules bare similarity to the crystal ligand, many will not be yield viable interaction energies with the 4ZUD receptor.
In order to shorten the list of potential molecules, and to ensure we only focus on ones that form similar interactions with the protein active site relative to the crystal ligand, we can apply a rescoring function to the new molecule library to extract those that are similar to the crystal ligand in both general composition, and energetic interactions with the protein.
In an new directory:
In a new input file:
Insert the following:
conformer_search_type rigid use_internal_energy yes internal_energy_rep_exp 12 internal_energy_cutoff 100.0 ligand_atom_file ../dn_focused_growth/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 ../../003.gridbox/4zud.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 ../../001.structure/4ZUD_protein_hydrogens.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 ../../003.gridbox/4zud.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 ../../003.gridbox/4zud.lig.min_scored.mol2 descriptor_hms_score_ref_filename ../../003.gridbox/4zud.lig.min_scored.mol2 descriptor_hms_score_matching_coeff -5 descriptor_hms_score_rmsd_coeff 1 descriptor_volume_score_reference_mol2_filename ../../003.gridbox/4zud.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
Run the command using:
dock6 -i rescore.in -o rescore.out
Several files should have been created. The descriptor.output_scored.mol2 file contains all of the molecules that would be deemed 'viable' in terms of mimicking the native crystal ligand interactions with the protein active site residues.
Footprint analysis plots can be generated for each molecule individually. Follow the steps provided in the previous tutorial: Editing 2022 Virtual Screening tutorial 2 with PDBID 4ZUD
Native crystal molecule for reference:
The molecule with the smallest footprint score: 7.127408
The molecule with the largest footprint score: 26.976864