# Name for the protocol
Name: Physics-based pose predictions guided by native ligands
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# All software packages used and their versions:
Software: Schrödinger Release 2016-3: WaterMapv2.7/SchrödingerRelease2016v2/Maestro 10.6/ProteinPreparationWizard/Primev4.4/Impactv71014/LigPrepv38014/Desmondv4.6/Glidev7.1/Jaguarv9.2/Macromodelv11.2/Phasev4.7/Jaguarv9.2/Epikv3.6
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# List of key parameters used for system preparation
System Preparation Parameters: Using Schrödinger's Protein Preparation Wizard, we converted the raw PDB structures into all-atom, fully prepared protein models.
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# Plain-text description of system preparation method
System Preparation Method: The protein preparation took place using the following procedure from within the Protein Preparation Wizard, Schrödinger Release 2016-2, Maestro 10.6:
1) Automatically imported full PDB files from the PDB website
2) Automatically added missing hydrogen atoms
3) Enumerate bond orders to HET groups
4) Removed or kept co-crystallized water molecules as described below (see following section)
5) Capped protein termini with ACE and NMA residues
6) Highlighted residues with missing atoms or multiple occupancies
7) Pre-processed structures for Prime, Schrödinger's program for protein structure prediction
8) Determined the most likely ligand protonation state as well as the energy penalties associated with alternate protonation states with Epik
9) Determined optimal protonation states for histidine residues
10) Corrected potentially transposed heavy atoms in arginine, glutamine, and histidine side chains
11) Optimize the protein's hydrogen bond network by means of a systematic, cluster-based approach, which greatly decreases preparation times 
12) Perform a restrained minimization that allows hydrogen atoms to be freely minimized, while allowing for sufficient heavy-atom movement to relax strained bonds, angles, and clashes using Impact
Schrödinger Release 2016-3: Schrödinger Suite 2016-3 Protein Preparation Wizard; Epik, Schrödinger, LLC, New York, NY, 2016; Impact, Schrödinger, LLC, New York, NY, 2016; Prime, Schrödinger, LLC, New York, NY, 2016.
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W., "Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments," J. Comput. Aid. Mol. Des., 2013, 27(3), 221-234
# List any number of key computational parameters in the following lines:
Pose Prediction Parameters: Van der Waals scaling factor for ligand= 0.8
Pose Prediction Parameters: SP Docking
Pose Prediction Parameters: Ligand sampling= flexible
Pose Prediction Parameters: number of poses=3
Pose Prediction Parameters: rotatables groups of Ser, Thr, Tyr were allowed to rotate
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# Provide free text description of your method below. Use as many lines as you need. 
Pose Prediction Method:  
The pose prediction parameters and pose prediction methodology is described below.

1) Given the abundance of structural FXR data, we decided to utilize this information for the prediction of Downloaded 28 FXR structures that we found available in the PDB website:

1OT7
3OKI
3OMM
3OOF
3OLF
3OOK
1OSH
3FLI
3BEJ
3DCT
3DCU
3FXV
3GD2
3HC5
3HC6
1OSV
3L1B
3OKH
3P88
3RVF
3RUU
3RUT
4WVD
3OMK
3P89
4OIV
4QE8
4QE6

We also downloaded the APO STRUCTURE from D3R



2) Based on superposition of these proteins and clustering of water molecules in the binding site, it was decided which waters to keep in the protein preparation stage which is described above. 

This choice was confirmed by a WaterMap calculation on selected proteins: APO structure provided by D3R, PDB ID: 3OMM, 3RUT



3) Protein Preparation

The default parameters from Maestro Protein Preparation Module were used, except that we also added filling in missing loops and missing side chains.

Water was kept based on WaterMap calculations and alignments as explained above.
 
If a ligand had a carboxy group, the deprotonated form was kept.

The Maestro Protein Preparation Module was used to prepare the 29 crystal structures. 




4) Ligand Preparation 

LigPrep was used to prepare the ligands provided by D3R.
The LigPrep panel wass used to to set up and start ligand preparation calculations. LigPrep produced the corresponding low-energy 3D structures. LigPrep also produced multiple output structures for each input structure by generating different protonation states, stereochemistry, tautomers, and ring conformations.
Ligands were kept in their first probable protonation and tautomerization state (lowest potential energy). 


5) Choice of protein structure in which each molecule was docked

After ligand and protein preparation, the next step was to dock the ligand in FXR. Given the abundance of crystal structure data, the best crystal structure for each D3R ligand had to be chosen for the docking. For this purpose, we followed different steps:
 
a) SHAPE screening 

First, we used the SHAPE module of the PHASE tool of Schrodinger in order to identify molecules that have similar shape to a shape query, which is either a template molecule or a set of spheres of defined radius and coordinates. As a template molecule we used the native ligands from all 28 downloaded crystal structures that we mention above. 

For the SHAPE screening, we used the method “Volume scoring” together with the QSAR option.

“Volume Scoring” chooses the types of atoms between which volume overlaps will be calculated. Volume overlaps are computed between atoms that have the same atom type as defined by the choice of atom typing scheme below. The QSAR option calculates the volume overlaps between atoms that have the same pharmacophore type (Acceptor, Donor, etc.) as defined for Phase QSAR models.

We then grouped the D3R ligands based on the similarity that they had with the native ligands.


b) cross-docking in all crystal structures

Glide SP was then used to dock all ligands in all 28 crystal structures. We compared the docked posed with the native ligand poses to find the D3R ligands which had the best alignment with the native ligand.


c) interaction fingerprints

Apart from the alignment of each D3R ligand with the native ligands, we also calculated the interactions of the D3R ligands with the proteins and compared them to the interaction of the native ligands with the corresponding proteins. These interactions, are termed “Structural interaction fingerprints” in Maestro and can be used on a set of ligands that are docked to a receptor to calculate ligand similarities, and cluster or filter the ligands. The fingerprints record information on proximity of the ligand to receptor residues and the types of residues with which the ligand interacts. See Deng, Z.; Chuaqui, C.; Singh, J. J. Med. Chem. 2004, 47, 337-44 and Singh, J.; Deng, Z.; Narale, G.; Chuaqui, C. Chem. Biol. Drug. Des. 2006, 67, 5-12 for a detailed description of the method and its uses.

To use this facility, choose Scripts → Cheminformatics → Interaction Fingerprints in Maestro. More information on how to use it is available in the panel and the help topic.

Based on the best match between a) similarity of the D3R ligands interaction fingerprints with the native ligands interaction fingerprints, b) the SHAPE similarity, and c) the docking alignment with the native ligand, the final structure for docking of each D3R ligand was chosen.


5) Docking procedure 

The docking took place with the program Glide of Schrodinger and the SP scoring function.

Glide searches for favorable interactions between one or more ligand molecules and a receptor molecule. The combination of position and orientation of a ligand relative to the receptor, along with its conformation in flexible docking, is referred to as a ligand pose.

The shape and properties of the receptor are represented on a grid by several different sets of fields that provide progressively more accurate scoring of the ligand poses. We thus started by creating the receptor grid for each of the 28 crystal structures. The hydroxyl groups in residues such as Ser, Thr, and Tyr and the thiol group in Cys can adopt different orientations with different ligands. Glide can allow such groups to adopt different orientations when ligands are docked, to produce the most favorable interaction. For Ser and Thr, the hydroxyls can be oriented in any of the three local minima, and likewise for the thiol of Cys; for Tyr, they can be in either of the two local minima. Using the Rotatable Groups tab we chose the hydroxyl of Ser, The, and Tyr residues within a distance of 4 Angstrom from the ligand to be treated flexibly.

The ligand poses that Glide generates pass through a series of hierarchical filters that evaluate the ligand’s interaction with the receptor. The initial filters test the spatial fit of the ligand to the defined active site, and examine the complementarity of ligand-receptor interactions using a grid-based method patterned after the empirical ChemScore function.

Poses that pass these initial screens enter the final stage of the algorithm, which involves evaluation and minimization of a grid approximation to the OPLS nonbonded ligand-receptor interaction energy.

Final scoring is then carried out on the energy-minimized poses. By default, Schrödinger’s proprietary GlideScore multi-ligand scoring function is used to score the poses. In this case, we used GlideScore SP as the scoring function, and a composite Emodel score was used to rank the poses of each ligand. Emodel combines GlideScore, the nonbonded interaction energy, and the excess internal energy of the generated ligand conformation.

Whenever a common core was was present with a native ligand, a core constraint docking was performed.


Exceptions from this procedure:

- FXR_17, we performed a DFT/B3LYP/6-31G* Jaguar simulation to calculate the minimum energy pose of the ligand. 
Glide SP Docking was then performed using as starting structure the Jaguar minimized structures. 
Glide XP Docking was then performed using as starting structure the Glide SP produced pose.



6) Flexible ligand alignment
Whenever a common core was was present with a native ligand, the flexible ligand alignment was performed on the docked pose. The largest common scaffold was chosen or a common SMARTS was provided.



7) After the flexible ligand alignment, prime minimization of the complex generated by the flexible ligand alignment was performed to remove steric clashes. In all cases the steric clashes were removed.



Contributors: Christina Athanasiou, Sofia Vasilakaki, Zoe Cournia
Biomedical Research Foundation, Academy of Athens, Greece




