2.2.1. File Preparation
Users must provide BLinDPyPr with a ligand .mol2 file (or a multi .mol2, for virtual screening) ready for docking. As for the receptor, BLinDPyPr can receive a protein .pdb file and submit it to FTMap through scripted online access. All the result files generated by FTMap are downloaded to the working directory and are automatically forwarded to the docking phase. On the other hand, it is also possible to previously run FTMap independently and provide BLinDPyPr with the resulting .pdb file, which contains the target protein as well as the docked probe crossclusters. This option is required for site-specific docking calculations, where it is necessary to visually select crossclusters in pockets of interest on the receptor surface (cf.
Section 2.2.2).
The docking-ready receptor .mol2 file will be automatically generated from the FTMap pdb result: the protein is separated from the probes and is submitted to Chimera DockPrep tool. In the case the user prefers to use a tailored receptor file, containing, for instance, specific charges or nonstandard residues, BLinDPyPr can be configured to use such .mol2 file for docking. However, it is important to note that such characteristics will not be taken into consideration by FTMap, since its protocol automatically adds hydrogens and charges and removes nonstandard atoms. Therefore, the probes will be docked in these conditions, even if the ligand is docked in the custom, user-defined receptor.
2.2.2. Cavity Definition
DOCK6 employs a sphere-based method to define the docking space. Spheres are described in an .sph file and can be placed anywhere in the receptor surface. They can be created automatically by DOCK6 tool sphgen, selected in a specific radius around a reference point by the tool sphere_selector, or added by hand in the .sph file. In a DOCK6 run, the docking box is created around all the spheres present in a receptor surface; however, ligands are only docked where there are spheres, even if the docking box encompasses the entire protein.
Using the concept of spheres, BLinDPyPr provides different options to specify the regions and pockets for docking. The spheres can be classified into two groups: the FTMap sphere group, which is the new method introduced by BLinDPyPr, and the classic sphere group, which is created using sphgen and is automated with BLinDPyPr.
Here, FTMap probe crossclusters (the probe groups output by FTMap after final clustering) are converted into spheres, referred to in this manuscript as FTspheres. This means that the spheres will be restricted to the regions FTMap chooses as potential binding pockets. This increases the specificity of the search for a docking pose, therefore increasing the chance of an accurate pose prediction. The conversion is carried out as follows (
Figure 1).
The BLinDPyPr script separates the probes from the main FTMap .pdb file and converts them into .mol2 format using PyMOL. These are then provided as input to the DOCK6 scoring function Pharmacophore Similarity Score (FMS), which is used to calculate the various pharmacophore definitions of the probes and to output them into a text file. The BLinDPyPr main conversion routine then converts the pharmacophore text file into a DOCK6 sphere file, along with information translated from the pharmacophore definitions themselves. If desired, chemical matching may be used to process this information and further orient the ligand: BLinDPyPr will replicate the pharmacophore parameter file utilized by FMS and provide it as input to DOCK6 so that it may find, in the candidate ligands, the same pharmacophore patterns it found for the FTMap probes. Additionally, it will create a correlation table instructing DOCK6 to match ligand pharmacophores to the ones found in the docking spheres’ labels. This matching occurs by discarding ligand conformations which produce unfavourable matches (i.e., a ligand pharmacophore overlaps with a sphere labeled for a different pharmacophore) [
27].
Furthermore, users may choose any combination of FTMap crossclusters they wish by passing their numbers to BLinDPyPr. Only the desired crossclusters will be converted into spheres, guiding DOCK6 towards site-specific docking, multiple-site docking, or, if none are specified, BLinDPyPr will select all of them in order to perform blind docking.
This type of approach cannot be categorized in either of the previously mentioned blind docking approaches. The type of blind docking prepared by BLinDPyPr with FTspheres guides DOCK6 towards multiple potential binding sites robustly identified by FTMap, which prevents it from probing the whole protein surface, while at the same time allowing it to evaluate each candidate ligand in all the FTMap defined pockets, simultaneously, in a single virtual screening or docking run.
BLinDPyPr can also generate default spheres using the DOCK6 sphere generator (sphgen), which calculates spheres throughout the whole receptor surface and automatically clusters them, thus sphere Cluster 1 is the most probable to overlap with the real receptor binding site, while Cluster 0 is equivalent to the whole sphere set.
If the sphgen flag is passed to BLinDPyPr, it will refrain from generating spheres from the FTmap probes and will instead use the classic spheres. Users can select any sphgen sphere cluster. If Cluster 0 is selected, all spheres will be used for docking, which will consequently happen in a conventional blind docking manner, as previously discussed.
It is also possible to automatically select spheres using DOCK6 sphere_selector through BLinDPyPr. If the user analyses the FTMap results and identifies crossclusters of interest but needs classic spheres in that region, the numbers of the selected crossclusters may be passed in addition to the sphgen flag. In this case, BLinDPyPr will select spheres within a three Angstrom radius around them.
Independently of the sphere type created, BLinDPyPr runs DOCK6 program showsphere, which creates a .pdb from the sphere .sph file, so that it can be observed by the user through a visualization program.
2.2.3. Docking Preparation
DOCK6 tool sphgen requires a receptor surface file, which BLinDPyPr will generate automatically through UCSF Chimera if the user opts for DOCK6 classic method. It is noteworthy that the surface component calculation with UCSF Chimera is challenging for some proteins. If there is an error in these calculations, impacting the integrity of the surface file, sphgen will not run successfully. Such errors do not occur with FTMap spheres since they are converted from the docked probe pharmacophores and their creation does not require surface file generation.
Following sphere generation, BLinDPyPr will define the docking box using the DOCK6 tool showbox, with a default margin of five Angstroms around the generated spheres. If desired, users can alter this value to any value of interest.
The grid calculations for the electrostatic and Van der Waals potentials in the docking box are automatically run through DOCK6 grid, with an input file bearing default parameters. This can be time-consuming depending on the size of the docking box and the computational power employed. To save time, if this calculation was already performed previously, the “grid” flag can be passed to BLinDPyPr so that the script does not needlessly run this step again. The grid.bmp and grid.nrg files must be placed on the working directory.