The scripts and input files that accompany this demo can be found in the demos/public directory of the Rosetta weekly releases.

KEYWORDS: DOCKING LIGANDS

Docking Approach using Ray Casting (DARC) is structure-based computational method for carrying out virtual screening by docking small-molecules into protein surface pockets. In this demo, DARC is used to dock a small molecule in a pocket centered around residue 61 of the protein, E3 ubiquitin-protein ligase Mdm2 (PDB: 4ERF).

Generating input files

  • Protein files:

    4ERF.pdb was downloaded from The Protein Databank. Remove any water or ligand or other HETATM present in the protein and remove redundant chains. Although Rosetta will build any missing atoms including hydrogens in input protein PDB files on the fly defore it uses them, we need to prepare the protein to generate the electrostatic potential grid. This is one way to dump the protein after building missing atom and added hydrogens. You can use the PDB provided in the input file to continue:

    NOTE ($ROSETTA3= path-to-Rosetta/main/source)

    $> cp input/4ERF.pdb .
    $> $ROSETTA3/bin/score.default.linuxgccrelease -in:file:s 4ERF.pdb -out:output -no_optH false
which gives the output protein 4ERF_0001.pdb

  • Ligand files:

    Input files are included in the folder input, but the following explains how they were obtained or generated. For more information, you can check the prepare ligand tutorial for more information.

    4ERF.pdb was downloaded from The Protein Databank. The small molecule used for docking was found in ZINC, the database of commercially available compounds at http://zinc.docking.org/. We download ZINC13989607 in mol2 format. The file name is zinc_13989607.mol2.

    Since the charges assigned to the atom is same for all conformers, it is fast to assign charges before generating conformers. The charges were added to the compound using the molcharge program from OpenEye (http://www.eyesopen.com/) using the following command:

    $ OpenEye/bin/molcharge -in zinc_13989607.mol2 -out zinc_13989607_charged.mol2 -method am1bccsym

    If you have the compound in 2D SMILES format, then first convert to 3D format and then add charges. You can use openbabel or OpenEye program for converting from SMILES format to mol2 format

    $ OpenEye/bin/omega2 -in zinc_13989607_charged.mol2 -out zinc_13989607_conformers.mol2 -maxconfs 100

    then we need to generate parameter files for the compound to use in Rosetta. save the names of the compounds in a text file for generating parameters. You can use other softwares to perform this steps.

    Next, parameter files for the compounds are generated using batch_molfile_to_params.py which is a python app in rosetta. This command is used:

    $ echo zinc_13989607_conformers.mol2 > molfile_list.txt

$ ROSETTA3/scripts/python/public/batch_molfile_to_params.py --script_path=<path-to-Rosetta>/main/source/scripts/python/public/molfile_to_params.py molfile_list.txt

    The expected output files are:

    params/zinc_13989607_conformers/000_conformers.pdb
params/zinc_13989607_conformers/000.params

    You can copy them to your directory: 

    $> cp input/000_conformers.pdb .
$> cp input/000.params .

  • Other input files:

    To run DARC we need to generate a RAY file for the input protein. To generate this ray-file we need to input the protein in PDB format and specify a target residue at the interface to generate the vectors. For more information about the ray file and how DARC works please refer to the provided citations. We can specify more than one residue at the interface. The command to run DARC is based on shape only is as follows: NOTE For your special purposes, you will need to change the residue numbers and also provide params file for your ligands.

    $> $ROSETTA3/bin/make_ray_files.default.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61 -darc_shape_only

    Expected output from this command will be a ray-file named ray_4ERF_0001_61.txt.

    To use a center of mass of any residue as origin points for casting rays: 

        $> $ROSETTA3/bin/make_ray_files.default.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61 -darc_shape_only -set_origin 5 -origin_res_num 85:A
    Expected output: ray_4ERF_0001_61.txt

    To use multiple origin points for casting rays: 

        $> $ROSETTA3/bin/make_ray_files.default.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61,54 -darc_shape_only -set_origin 5 -origin_res_num 85:A -multiple_origin
    Expected output: ray_4ERF_0001_61,54.txt

    To use a bound ligand to center the grid: 

        $> $ROSETTA3/bin/make_ray_files.default.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61 -darc_shape_only -set_origin 5 -origin_res_num 85:A -bound_ligand input/4ERF_XTL_0001.pdb -extra_res_fa input/4ERF_XTL.params -lig_grid
    Expected output: ray_4ERF_0001_61.txt

    To include electrostatics calculations:

    For electrostatics calculations, we first need to generate the electrostatic potential grids. To generate the electrostatic potential grid, we can use the examples Listings provided in the zap toolkit manual.

    $ OpenEye/bin/Listing_2 -in 4ERF_0001.pdb -out 4ERF.agd -buffer 2 -grid_spacing 0.5 -epsout 80 -epsin 1

    Expected output: 4ERF.agd

    You can also copy this file from the input directory: 

      $> cp input/4ERF.agd .

    Now we need to resize the electrostatic potential grid (generated from openeye '4ERF.agd') to match the size of the interface pocket grid. This step can be carried out while generating the ray file. 

        $> $ROSETTA3/bin/make_ray_files.linuxgccrelease -database Rosetta/main/database/ -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61,54 -set_origin 5 -origin_res_num 85:A -multiple_origin -bound_ligand input/4ERF_XTL_0001.pdb -extra_res_fa input/4ERF_XTL.params -lig_grid -espGrid_file 4ERF.agd
    The output from this command will be a ray-file named ray_4ERF_0001_61,54.txt and an electrostatic potential grid file named DARC_4ERF.agd which we use as input for running docking using DARC.

Running DARC

In the second step, we are actually running the docking calculations using the pre-generated ray-file. Call DARC for running the docking calculations using the pre-generated ray-file and the matched electrostatic potential grid. use the flag -num_particles to increase or decrease the sampling. high number of particles will take a longer time to finish. For best results, We suggest alleast 100 particles and 100 runs for docking single conformer or iterative docking (one-by-one) of ligand conformers. For on-the-fly sampling of ligand conformers we suggest at least 500 particles and 500 runs.

Here we give theinput ligands for screening against the ray-file, as follows. Note that the options.short files are provided for faster test runs in case needed.

Docking single ligand conformer: 

  $>  $ROSETTA3/bin/DARC.default.linuxgccrelease @darc_single.options
Expected output: DARC_4ERF_0001_LG1.pdb

Docking multiple ligand conformers: 

  $>  $ROSETTA3/bin/DARC.linuxgccrelease @darc_conformers.options
Expected output: DARC_4ERF_0001_000.pdb

To run DARC with shape only (without including electrostatics score):

$>  $ROSETTA3/bin/DARC.linuxgccrelease @darc_shape.options

Expected output: DARC_4ERF_0001_000.pdb

To search conformers on-the-fly:

$>  $ROSETTA3/bin/DARC.linuxgccrelease @darc_fly.options -protein 4ERF_0001.pdb -ligand 000_conformers.pdb -extra_res_fa 000.params -ray_file ray_4ERF_0001_61.txt -espGrid_file DARC_4ERF.agd -search_conformers true

Expected output: DARC_4ERF_0001_000.pdb

The output of the DARC run is a docked model of the protein-ligand complex named DARC_4ERF_0001_000.pdb. To minimize the docked model: We can do the fullatom minimization of the DARC models separately in Rosetta or as an additional option while running DARC itself. To minimize the DARC models immediately after docking we add the flag -minimize_output_complex:

$>  $ROSETTA3/bin/DARC.linuxgccrelease @darc_shape.options -minimize_output_complex

Expected output files are:

  • DARC docked model complex: DARC_4ERF_0001_000.pdb
  • DARC docked and minimized model complex: mini_4ERF_0001_000.pdb

Other options for DARC:

  • -use_connolly_surface: use this flag to make ray files with connolly surface insted of pocketshell generated by pocketgrid
  • -num_particles: set the number of particles used durin particle swarm optimization
  • -num_runs: set the number of particles used durin particle swarm optimization
  • -missing_point_weight: weight for rays misses the pocket
  • -steric_weight: weight for rays missesthe ligand
  • -extra_point_weight: weight for ligand moves away from the pocket
  • -esp_weight: electrostatics weight
  • -pocket_static_grid: no autoexpansion of the pocket grid (set ON for DARC)

Example: Commands for sample DARC run for protein MDM2 [PDB:4ERF]

$ cd run_dir

Copy 4ERF.pdb, 000_conformers.pdb, and 000.params, 4ERF.agd, DARC_4ERF.agd, 4ERF_0001.pdb, 4ERF_XTL_0001.pdb, 4ERF_XTL.params to run_dir. Run DARC with the following command:

  • DARC shape only:

    $ Rosetta/main/source/bin/score.linuxgccrelease -in:file:s 4ERF.pdb -out:output -no_optH false
$ Rosetta/main/source/bin/make_ray_files.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61 -darc_shape_only
$ Rosetta/main/source/bin/DARC.linuxgccrelease -protein 4ERF_0001.pdb -ligand 4ERF_XTL_0001.pdb -extra_res_fa 4ERF_XTL.params -ray_file ray_4ERF_0001_61.txt -darc_shape_only -num_particles 50 -num_runs 50

    Expected output: DARC_4ERF_0001_LG1.pdb

  • DARC shape and electrostatics:

    $ Rosetta/main/source/bin/make_ray_files.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61 -espGrid_file 4ERF.agd
$ Rosetta/main/source/bin/DARC.linuxgccrelease -protein 4ERF_0001.pdb -ligand 4ERF_XTL_0001.pdb -extra_res_fa 4ERF_XTL.params -ray_file ray_4ERF_0001_61.txt -espGrid_file DARC_4ERF.agd -num_particles 50 -num_runs 50

    Expected output: DARC_4ERF_0001_LG1.pdb

  • DARC sampling conformers on the fly

    $ Rosetta/main/source/bin/DARC.linuxgccrelease -protein 4ERF_0001.pdb -ligand 000_conformers.pdb -extra_res_fa 000.params -ray_file ray_4ERF_0001_61.txt -espGrid_file DARC_4ERF.agd -num_particles 500 -num_runs 500 -search conformers true

    Expected output: DARC_4ERF_0001_000.pdb

  • DARC sampling conformers iteratively one-by-one

    $ Rosetta/main/source/bin/DARC.linuxgccrelease -protein 4ERF_0001.pdb -ligand 000_conformers.pdb -extra_res_fa 000.params -ray_file ray_4ERF_0001_61.txt -espGrid_file DARC_4ERF.agd -num_particles 500 -num_runs 500 -search conformers false

    Expected output: DARC_4ERF_0001_000.pdb

Running DARC in GPU systems

DARC is adapted to run on GPU systems. On average, Running DARC on GPU system is 190 fold faster than running on CPU. The results from DARC that runs on CPU and GPU systems should be same (with same input and constant seed).

First make sure you built rosetta with the flag extras=opencl to run on GPU systems

$ cd Rosetta/main/source
$ ./scons.py mode=release extras=opencl bin

Then use the opencl executables (eg: DARC.opencl.linuxgccrelease) instead of default executables with the flag '-gpu 1', i.e call DARC.opencl.linuxgccrelease instead of DARC.linuxgccrelease. All other options are same as CPU. Here is an example for GPU command:

$ Rosetta/main/source/bin/score.opencl.linuxgccrelease -in:file:s 4ERF.pdb -out:output -no_optH false -gpu 1
$ Rosetta/main/source/bin/make_ray_files.opencl.linuxgccrelease -pocket_static_grid -protein 4ERF_0001.pdb -central_relax_pdb_num 61 -darc_shape_only -gpu 1
$ Rosetta/main/source/bin/DARC.opencl.linuxgccrelease -protein 4ERF_0001.pdb -ligand 000_conformers.pdb -extra_res_fa 000.params -ray_file ray_4ERF_0001_61.txt -darc_shape_only -gpu 1 -num_particles 500 -num_runs 500 -search_conformers true

Expected output: DARC_4ERF_0001_000.pdb

Running DARC will result in these output files:

  • DARC_4ERF_0001_LG1.pdb is the final docked pose for single conformer.
  • DARC_4ERF_0001_000.pdb is the final docked pose for multiple conformers. Note:The flag '-minimize_output_complex' can be used to minimized the output model
  • mini_4ERF_0001_LG1.pdb is final docked and minimized pose for single conformers.
  • mini_4ERF_0001_000.pdb is final docked and minimized pose for multiple conformers.

Analyzing the results

The output should look similar to:

Rosetta/main/source/bin/DARC.linuxgccrelease -protein 4ERF_0001.pdb -ligand 4ERF_XTL_0001.pdb -extra_res_fa 4ERF_XTL.params -ray_file ray_4ERF_0001_61.txt -espGrid_file DARC_4ERF.agd -num_particles 10 -num_runs 10
core.init: Rosetta version 1e0e3a1462814b2fa93580e9e1b1f7ab81736efc 2014-11-19 15:22:07 -0600 from git@github.com:RosettaCommons/main.git
core.init: command: /Users/ragul/Rosetta/main/source/bin/DARC.linuxgccrelease -protein 4ERF_0001.pdb -ligand 4ERF_XTL_0001.pdb -extra_res_fa 4ERF_XTL.params -ray_file ray_4ERF_0001_61.txt -espGrid_file DARC_4ERF.agd -num_particles 10 -num_runs 10
core.init: 'RNG device' seed mode, using '/dev/urandom', seed=590325598 seed_offset=0 real_seed=590325598
core.init.random: RandomGenerator:init: Normal mode, seed=590325598 RG_type=mt19937
core.init: Resolved executable path: /Users/ragul/Rosetta/main/source/bin/DARC.linuxgccrelease
core.init: Looking for database based on location of executable: /Users/ragul/Rosetta/main/database/
core.chemical.ResidueTypeSet: Finished initializing fa_standard residue type set.  Created 738 residue types
core.pack.task: Packer task: initialize from command line() 
origin_space : 7.25
Reading ligand 4ERF_XTL_0001.pdb
core.pack.task: Packer task: initialize from command line() 
Starting PSO
Time for DARC runs: 0.589119
SCORES : 4ERF_0001_LG1  Conformer 1 has DARC Score : 4.29993

In this case the DARC score for the final docked pose is 4.29993. This score can be use to compare with other ligand that are docked using the same ray file. The lower the score the better the match between the protein pocket and ligand. DARC_4ERF_0001_LG1.pdb is the out file that contains the final docked pose for single conformer.

If we use the flag -minimize_output_complex the model will be minimized and we get the file mini_4ERF_0001_LG1.pdb as output.