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# Your brief name for this protocol
Name: Free energy perturbation calculations 
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# All software packages used and their versions:
Software: SchrödingerRelease2016v2/Maestro10.6/Desmondv4.6/Glidev7.1/Phasev4.7/FEP+/LigPrepv38014/Epikv3.6
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# List any number of key computational parameters in the following lines:
Parameter: OPLS3 force field
Parameter: Windows per transformation: 12
Parameter: Assumed pH 7.4
Parameter: Typical simulation time per window: 5 ns
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# Provide free text description of your method below. Use as many lines as you need. 
# The level of detail should be about what you would write for a publication.  
Method: Free Energy Perturbation Protocol
1)	First, a crystal structure was chosen based on SHAPE screening of Schrodinger. 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 the available crystal structures and mapped them on the molecules from the spiros group and the sulfonamide group.  
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.
Apart from the alignment of each D3R ligand with the native ligands, we also calculated the interactions of the spiros and sulfonamide 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.
The molecules were then visualized with the ones having the best interaction fingerprint first. From this ranking, we chose as the representative molecule the one with the pose closest to its minimum energy conformation. 
A docking pose was selected for the reference ligands FXR_99, FXR_74 based on the “PosePredictionPRotocolTEmplate-ZC.txt”


2)  	For this docking pose, flexible ligand alignment of the spiros and sulfonamide molecules was performed using as a criterion the biggest common core among the two compound series.  

3)	Prime energy minimization followed for each ligand-receptor complex. Only the protein near the ligand was allowed to move (4 Angstroms). The ligand was not minimized. We chose this option because of the plasticity of the protein that we have observed when aligning the different protein structures. 

4) 	These input poses were submitted to a FEP+/Desmond calculation. Desmond with FEP/REST provide an enhanced sampling technique for free energy perturbation, to calculate the relative binding affinities of the 26 sketched lead compounds binding to Arp2/3 complex. A total of 12 λ windows are used for both the normal FEP and FEP/REST calculations, but the FEP/REST simulation method modifies the potential energy for a localized region surrounding the binding pocket with a automated algorithm, which selects the REST region (“hot” region). The REST algorithm calculates the effective temperature of that region allowing the complex to sample the relevant structural rearrangements within the allowed sampling times. The final predictions are independent of the paths chosen to perform the transformation from one ligand to the other. As the lead compounds of each selected system are imported into the graphical interface, the FEP Mapper module of Desmond automatically generates the perturbation pathways by a variant of a mapping algorithm. Accordingly to their similarity scores the ligand pairs connect to each other by edges in a way that each edge represent one FEP/REST calculation. The minimal error in the free-energy calculation results is estimated based on the spatial conformations sampled in all simulations. As a measure of convergence of the calculations we ensured that all molecules run in closed thermodynamic cycles, the configured space of the lamda windows overlaps, and the cumulative ΔG vs time converges to one value. The systems ran on the graphics processing units (GPUs) cluster of the Greek national supercomputing facility, HPC ARIS.

5) 	FEP+ results gave us the final relative free energy differences ranking

6) 	We submitted the final snapshot of the MD trajectory for each ligand. All water molecules were removed.

7)	The problem of handling charged mutations. The spiros group contained several ligands with carboxylic groups, which are deprotonated (negative charge) according to the LigPrep module of Schrodinger. However, the set contained also neutral molecules. The total charge of the system should remain conserved while performing a FEP mutation. Previous studies have shown that turning on or off a charge in a system during a FEP calculation is nontrivial when using PME for the treatment of electrostatics. [Kastenholz, M. A.; Hunenberger, P. H. J. Chem. Phys. 2006, 124 (12), 124106-124127. Kastenholz, M. A.; Hunenberger, P. H. J. Chem. Phys. 2006, 124 (22), 224501-224520]. The problem arises mainly from the fact that a charge change in PME is handled by introducing a uniform neutralizing background charge to enforce neutrality [Hummer, G., Pratt, L. R., and García, A. E. J. Phys. Chem. 1996, 100 (4), 1206-1215]. In FEP calculations this means that instead of calculating the free energy of turning off/on a charge, the calculated free energy is the sum of turning off/on the charge and the free energy of turning on/off a uniform neutralizing background charge, which might introduce an error in the overall difference in the free energy of binding.
Therefore, we chose the following strategy. Two sets of FEP mutations were performed. One contained only the set of deprotonated ligands and the other contained the same ligands in their protonated form, plus the neutral ligands. The calculations showed that the protonated and deprotonated ligands rank the same. The R^2 of two DDG values was 0.82, so the correlation of the values was very high. Therefore, we chose to keep the protected ligand results and include the neutral ligand ranking within the deprotonated set.



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

