KEYWORDS: LIGANDS DOCKING
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In addition to following this sample docking problem, the user is encouraged to review the Rosetta user guide including the section on ligand-centric movers for use with RosettaScripts.
The experimental data for this tutorial is derived from: Chien, E. Y. T. et al. Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science 330, 1091-5 (2010).
This particular D3/eticlopride protein-ligand complex was used as a target in the GPCR Dock 2010 assessment, the results of which are discussed here: Kufareva, I. et al. Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19, 1108-1126 (2011).
If you are interested in more information on the performance of Rosetta in modeling and docking D3/GPCRs in general, please consult Nguyen, E. D. et al. Assessment and challenges of ligand docking into comparative models of g-protein coupled receptors. PLoS One 8, (2013).
Dopamine is an essential neurotransmitter that exhibits its effects through five subtypes of dopamine receptors, important members of class A G-protein coupled receptors (GPCRs). Both subtype two (D2R) and subtype three (D3R) function via inhibition of adenyl cyclase, and modulation of these two receptors has clinical applications in treating schizophrenia. However, the high degree of binding site conservation between D2R and D3R makes it difficult to generate pharmacological compounds that selectively bind one or the other, and thereby reducing side effects. Today, we will examine how eticlopride, a D2R/D3R antagonist, binds to human D3R.
A crystal structure is available for the D3R and eticlopride complex (PDB: 3PBL), but for the purposes of this exercise, we will model the protein-ligand interactions anyways. In reality, you may be using a comparative model rather than a crystal structure for the protein receptor, but the steps in this tutorial will apply to both.
For this exercise, we'll be doing our pre-docking preparations in the protein_prep and ligand_prep folders. The modeling will be done in the docking folder. The scripts folder contains helpful ligand docking specific scripts that we'll be using during this tutorial (you should never be copying files to or from this folder). All necessary files are also prepared in the answers directory in case you get stuck.
1.Navigate to the ligand docking directory where you will find the ligand_prep, protein_prep, docking, and answers folders
2.Prepare a human dopamine 3 receptor structure. We will do this by obtaining the crystal structure (3PBL) and removing the excess information. 2.1. Change into the protein_prep directory with the cd command:
2.2. Download 3BLP (pdb format) from http://www.rcsb.org/pdb/home/home.do into the protein_prep directory. The clean_pdb.py script will allow you to strip the PDB of information other than the desired protein coordinates. The 'A' option tells the script to obtain chain A only. The full crystal structure consists of two monomers as a crystallization artifact.
> cd protein_prep
2.3. There are two output files from clean_pdb.py: 3PBL_A.pdb contains a single chain of the protein structure and 3PBL_A.fasta contains the corresponding sequence. 3PBL_A.pdb is the receptor structure we will be using for docking, copy this into the docking directory.
> <path-to-Rosetta>/tools/protein_tools/scripts/clean_pdb.py 3PBL.pdb A
Note: This structure has a T4-lysozyme domain instead of the third cytoplasmic loop as a stabilizing feature for crystallography. Normally, we would truncate this lysozyme segment and perform loop modeling as discussed in the comparative modeling tutorial to regenerate the intracellular loop. However in the interest of time, we will use the lysozyme containing structure as the eticlopride binding site is far from the intracellular domain.
> cp 3PBL_A.pdb ../docking
3.Next, we will prepare the ligand files by generating params using a eticlopride conformational library. For more information about the ligand preparation, check ligand preparation tutroial.
3.1. cd into the directory named ligand_prep
3.2.In the directory, you will find a pair of already prepared files: eticlopride.sdf and eticlopride_conformers.sdf
> cd ../ligand_prep
3.2.1. eticlopride.sdf: This contains the eticlopride structure found in the 3PBL protein complex.
Note: You can also find the ligand file from this particular PDB structure by going to the 3PBL page and scrolling down to the "Ligand Chemical Component" section. From there, you can click "Download" under the ETQ identifier.
3.2.2. eticlopride_conformers.sdf: This is a library of conformations for eticlopride generated outside of Rosetta. The downloaded ligand .sdf file only contains conformations found in the PDB so we must expand the library to properly sample the conformational space. We also need to add hydrogens since they are not resolved in the crystal structure. Feel free to open the file in Pymol and use the arrow keys to scroll through the different conformations:
This particular conformational library was generated using the Meiler lab's BioChemicalLibrary (BCL). The BCL is a suite of tools for protein modeling, small molecule calculations, and machine learning. If you're interested in licensing the BCL, please visit http://www.meilerlab.org/bclcommons or ask one of the instructors. Other methods of ligand conformer generation include OpenEye's MOE software, CSD Mercury software and web-servers such as Frog 2.1 or DG-AMMOS. The generated libraries will differ depending on the chosen method.
3.2.3. Generate a .params file and associated PDB conformations with Rosetta atom types for eticlopride. A params file is necessary for ligand docking because Rosetta does not have records for custom small molecules in its database.
to learn more about the script for generating the params file.
> <path-to-Rosetta>/main/source/scripts/python/public/molfile_to_params.py -h
Note: You may encounter a warning about the number of atoms in the residue. This is okay as Rosetta is merely telling you that the ligand has more atoms than an amino acid.
$> $ROSETTA3/scripts/python/public/molfile_to_params.py -n ETQ -p ETQ --conformers-in-one-file ligand_prep/eticlopride_conformers.sdf
ETQ.params contains the necessary information for Rosetta to process the ligand, ETQ.pdb contains the first conformation, and ETQ_conformers.pdb contains the rest of the conformational library.
3.2.4. If you use the tail command on ETQ.params, you will notice the PDB_ROTAMERS property line that tells Rosetta where to find the conformational library. Make sure this line has ETQ_conformers.pdb as the property.
> tail ETQ.params> cp ETQ* ../
3.2.5. Now that we have the necessary files for ligand docking, let's copy them over the ligand_docking directory.> cd ../
4.Now we want to make our final preparations in the docking directory. 4.1. Switch over to ourligand_docking directory> pymol 3PBL_A.pdb ETQ.pdb
4.2. Open up our prepared receptor and ligand structures to examine the complex> cp protein_prep/3PBL_A.pdb . > cat 3PBL_A.pdb ETQ.pdb > 3PBL_ETQ.pdb
NOTE: if you cannot open pymol from the command line, you may need to set up your bash environment. Now make a pdb file by concatenating your protein and ligand, running this script:$> cp answers/docking/3PBL_ETQ.pdb .
If you don’t have these files, copy them from the answers directory:$> cp docking/dock.xml . $> cp docking/options . $> cp docking/crystal_complex.pdb .
4.3. Tip: 'All->Action->preset->ligand sites->cartoon' will help you visualize the protein/ligand interface. The All button is denoted by a single letter "A" in Pymol Since this is a rudimentary exercise, we will start with the ligand in the protein binding site. In practical application, we may need to define a starting point with the [StartFrom mover](https://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/Movers/movers_pages/StartFromMover) or to manually place the ligand into an approximate region using Pymol. 4.4. Once you close Pymol, make sure Rosetta has these four necessary input structure/parameter files in the ligand_docking tutorial directory. If you are missing any of these, copy them from ../answers/docking/ 4.4.1. 3PBL_A.pdb: a single chain of the protein receptor structure 4.4.2. ETQ.pdb: a default starting conformation for eticlopride 4.4.4. ETQ_conformers.pdb: A pdb file containing all conformers generated from the eticlopride library 4.4.5. ETQ.params: a Rosetta parameter file that provides the necessary properties for Rosetta to treat eticlopride 5.Next we need to make sure we have the proper [[RosettaScripts|rosetta_scripting]] XML file, input options file, and crystal complex (correct answer). These files are provided to you as dock.xml, options.txt, and crystal_complex.pdb. Copy these to your ligand_docking directory:$> $ROSETTA3/bin/rosetta_scripts.linuxgccrelease @options
5.1. dock.xml - This is the RosettaScripts XML file that tells Rosetta the type of sampling and scoring to do. It defines the scoring function and provides parameters for both low-resolution coarse sampling and high-resolution Monte Carlo sampling. 5.2. options - This is the options file that tells Rosetta where to locate our input PDB structures and ligand parameters. It also directs Rosetta to the proper XML file. 5.3. crystal_complex.pdb - This is the D3-eticlopride complex from the PDB. It will serve as the correct answer in our case allowing us to make comparisons between our models and actual structures. 6.Run the docking study (This should take a few minutes at most, as we're using a reduced number of output structures):> <path-to-Rosetta>/demos/tutorials/ligand_docking/scripts/visualize_ligand.py 3PBL_A_ETQ_0001.pdb > pymol 3PBL_A_ETQ_0001.pse
7.The Rosetta models are saved with the prefix 3PBL\_ETQ\_ followed by a four digit identifier. Each model PDB contains the coordinates and Rosetta score corresponding to that model. In addition, the model scores are summarized in table format in the score.sc file. The two main scoring terms to consider are: 7.1. total_score: the total score is reflective of the entire protein-ligand complex and is good as an overall model assessment 7.2. interface\_delta_X: the interface score is the difference between the bound protein-ligand complex and the unbound protein-ligand. Interface score is useful for analyzing ligand effects and for comparing different complexes. 8.One other metric to keep an eye on is the Transform\_accept\_ratio. This is the fraction of Monte Carlo moves that were accepted during the low resolution Transform grid search. If this number is zero or very low, the search space may be too restrictive to allow for proper sampling. 9.In benchmarking examples when we have a correct crystal structure, ligand\_rms\_no\_super\_X will give us the RMSD difference between our model ligand and the crystal structure ligand given in crystal_complex.pdb. This is an important metric when benchmarking how well your models correlate to reality. When the crystal structure is unknown, we can also calculate model RMSDs using the best scoring structure as the "true answer". 10.Use pymol to visually compare your best-scoring model and worse-scoring model with the crystal structure provided in crystal_complex.pdb. The "All->Action->preset->ligand sites->cartoon" setting in Pymol is ideal for visualizing interfaces. What interactions were successfully predicted by Rosetta? 11.The visualize_ligand.py script in the scripts directory is a useful shortcut to doing quick visualizations of protein-ligand interfaces. It takes in a PDB and generates a .pse Pymol session by applying common visualization settings. The example below shows the command lines for using this script on the 0001 model but you are free to try it on any one (or more!) of your models:> <path-to-Rosetta>/demos/tutorials/ligand_docking/scripts/extract_scores.bash score.sc
This only works if you have correct environment settings for your pymol. ### Analysis Since we generated such a small number of structures, it is unlikely to capture all the possible binding modes that you would expect to encounter in an actual docking run. In the "out" directory, there are 500 models pre-generated using the exact same protocol. We will look at an example of how we can analyze this dataset. 1.cd into the out directory in your ligand_docking: cd out 2.In addition to the 50 structures here, you will find the score.sc, a score\_vs\_rmsd.csv file, a rmsds_to_best_model.data, and several .png image files. 2.1. score.sc: summary score file for the 50 structures as outputted by Rosetta 2.2. score\_vs\_rmsd.csv: a comma separated file with the filename in the first column, total_score for the complex in the second column, the interface score in the third column, and ligand RMSD to the native structure in the fourth column. This file was tabulated using the extract_scores.bash script and the score.sc file as input. This is a very specific script made for extracting useful information in ligand docking experiments. However, the script can be easily customized for extracting other information from Rosetta score files. If you have any in-depth questions about how it works or how to modify it, feel free to ask. To see how it in action, run:> cd ../ > <path-to-Rosetta>/demos/tutorials/ligand_docking/scripts/calculate_ligand_rmsd.py -n 3PBL_ETQ_0003.pdb -c X -a 7 -o rmsds_to_best_model.data *_000*.pdb
2.3. rmsds\_to\_best\_model.data: a space separated file containing RMSD comparisons with the best scoring model (not crystal structure!) for all PDB files. A more detailed discussion of this file will come further down in the tutorial. This file has the filename in the first column, an all heavy-atom RMSD in the second column, a ligand only RMSD without superimposition in the third column, a ligand only RMSD with superimposition in the fourth column, and heavy atom RMSDs of side-chains around the ligand in the fifth column. This file is generated using the calculate\_ligand\_rmsd.py script. It uses pymol to compare PDB structures containing the same residues and ligand atoms. It's a quick way of calculate ligand RMSDs of Rosetta models. To see how this works, let's try it on the five models we generated in the previous steps:cd out
This command compares all five of your models to the one after the -n option. Your best scoring model may not be the one labelled 0003 so feel free to customize that option. The -c tells the script that the ligand is denoted as chain X. The -a tells the script to use 7 angstroms as the cutoff sphere for side-chain RMSDs. The -o option is the output file name. Lastly, we provided a list of PDBs using the wildcard selection. The script produces the rmsd\_to\_best\_model.data file that you can open in any text editor. Feel free to ask questions if you like to discuss more of how to customize this script for your own applications. Now let's go back to the pre-generated model directory:
2.4. PNG files: plots made from the various data file mentioned above. Python and the matplotlib package was used here but you are free to use any plotting software you prefer.
3.In this case, we have the correct answer based on the crystal structure so we can examine a score vs rmsd plot to see if the better scoring models are indeed closer to the native ligand binding mode. Open up the plot with the following command:
On the X-axis you will see the ligand RMSD to the ligand in the crystal structure. On the Y-axis you will see the interface delta score in Rosetta Energy Units. Notice the general correlation between RMSD and Rosetta Score, with a large cluster of highly accurate and good scoring models in the lower left hand corner.
4.In practical applications, we would not have the crystal structure for comparison. However, we can treat the best scoring model as the correct model and see if we generate a similar funnel. This is one application of how we might use the calculate_ligand_rmsd.py script discussed earlier. Once we identify a desired "best model", we can run the script to generate the rmsds_to_best_model.data. Some scripting may be required to put the information from multiple files together, depending on which software package you choose to graph with. To identify the best scoring model for this example, I selected the top 200 models based on the best overall score and then identified the best model by interface score. The best model for these plots is 3PBL_A_ETQ_0347.pdb. Open up the first plot with:
Again, we see a cluster of good scoring models near the best scoring model with a general downward trend further away. We can zoom in on the cluster in the lower left hand corner to get an even better picture.
We see the same overall trend in this cluster, suggesting that the top scoring models in this run are likely to be good predictors of the true ligand binding position.
5.Finally let's look at some structures. To sort the CSV file by interface score and take the top twenty, type:
sort -t, -nk3 score_vs_rmsd.csv | head -n 20
These should all be very low RMSD models. To compare a certain structure to the native in Pymol, use:
pymol 3PBL_ETQ_0001.pdb ../crystal_complex.pdb
Don't forget the ligand site preset mode for visualizing interfaces or use the visualize_ligand.py script to generate pymol sessions. If you like, we can also look at some of the poor scoring models to see exactly what went wrong. To find the top 20 worse models by interface score:
sort -t, -nk3 score_vs_rmsd.csv | tail -n 20
3PBL_A_ETQ_0033.pdb should come up as a poor scoring, high RMSD structure. When we open it up in Pymol, we can see that the ligand binding direction is different from the native position. This can happen when there is an extended binding pocket but in this case, the Rosetta score was able to discern the difference between these models.
pymol 3PBL_A_ETQ_0033.pdb ../crystal_complex.pdb
Q: My protein has a quite large cavity and a small ligand (not bigger than a Leucine). In the XML file these are the standard parameters:
<Transform name="transform" chain="X" box_size="7.0" move_distance="0.2" angle="20" cycles="500" repeats="1" temperature="5"/>. Why is the
move_distance and the angle so small? Would it make sense to increase the box_size to 12, the move_distance to 5 and the angle to 360 to sample more space the ligand is allowed to move in?
A: The Transform algorithm is a Monte Carlo proceedure, and the
move_distance and angle are the size for the individual steps in the MC protocol, not the maximum extent of the movement. They're also the sd of a gaussian, so you're not necessarily limited to the given amount in any given step. That said, if you're increasing the size of the pocket, it might make sense to bump the move size up in proportion. (So for a box size of 12, a move_distance of 0.25 to 0.5 or so might be appropriate.)
If you're exploring the pocket, I'd also suggest setting the
initial_perturb option. By default Transform will always start with the input position. If you add the
initial_perturb=X.X option, then it will first randomize the starting location of the ligand within an X.X Angstrom sphere from the starting position, as well as randomizing the orientation. -- And a new random position/orientation will be taken for each nstruct, so you can sample the pocket even if your MC moves aren't sufficient to wander across it. Also, if you're increasing the size of the pocket, you're likely going to need to increase the size of the Grid such that it will cover the maximal extent of ligand travel. If it doesn't, Transform will reject any ligand which accidentally falls outside the grid.
Q: Does the
initial_perturb option also randomize the angles? Because there is another option
initial_angle_perturb in the TransformMover.
A: Yes, by default setting
initial_perturb will completely randomize the angles (ligand orientation) - the
initial_angle_perturb is there if you want to reduce the angle perturbation. (e.g. if you're refining the orientation)
Congratulations, you have performed RosettaLigand docking study! Now use your docked models to generate hypotheses and test them in the wet lab!