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Chapter 54. Greater tutorial 07
54.1. MIFs for a set of passively absorbed drugs.
The aim of the present tutorial is to demonstrate the use of Greater with several targets in mol2 format.
The calculations will be performed for a series of passively absorbed drugs with reliable data on human intestinal absorption (%HIA) covering a range of absorption values from 0.3 to 100 %. The following 20 compounds are used for analysis: metoprolol, nordiazepam, diazepam, oxprenolol, phenazone, oxazepam, alprenolol, practolol, pindolol, ciprofloxacin, metolazone, tranexamic acid, atenolol, sulpiride, mannitol, foscarnet, sulphasalzine, olsalazine, lactulose, raffinose.
54.1.1. Working Section
Change to the tutorial directory and list its contents.
cd greater07
ls -l
In this directory we have included all the 20 molecules in Sybyl mol2 format. 3D molecular structures were build using CORINA followed by Energy minimisation with the Merk force-field as implemented in Sybyl 6.4. For this simple example all the molecules were built in their uncharged form. 3D molecular structures and the %HIA are reported below.
| Molecular structure | Name & symbol | Logit (%HIA) |
![]() | Alprenolol | 1.1372 |
![]() | Atenolol | 0.0422 |
![]() | Ciprofloxacin | 0.3074 |
![]() | Diazepam | 1.2086 |
![]() | Foscarnet | -0.7041 |
![]() | Lactulose | -2.2321 |
![]() | Mannitol | -0.4715 |
![]() | Metolazone | 0.2151 |
![]() | Metoprolol | 2.0086 |
![]() | Nordiazepam | 1.3936 |
![]() | Olsalazine | -1.6413 |
![]() | Oxazepam | 1.2086 |
![]() | Oxprenolol | 1.2086 |
![]() | Phenazone | 1.2086 |
![]() | Pindolol | 0.9224 |
![]() | Practolol | 1.0746 |
![]() | Raffinose | -2.5344 |
![]() | Sulphasalzine | -0.8799 |
![]() | Sulpiride | -0.2698 |
![]() | Tranexamic acid | 0.0591 |
Importation of mol2 structures in Greater
Prepare a file containing the list of the molecules:
ls -1 *.mol2 > filemol2.lst
Now, we should convert all the molecules from "mol2" into "GRID-pdb" format. This process is quite sophisticated, because GRID has to recognise the chemical types of atoms, distinguish between fixed and mobile hydrogens etc. ... This work is carried out by Greater in automatic way.
Start the Greater program:
Greater
then select the command Targets->Add multiple targets (or press the Add multiple button). Select the tab "List file" from the following window:

Now search for the file list pressing the button "..." and select the file filemol2.lst just created.
Pressing the OK button will let Greater process all the 20 molecules one after the other. After few seconds the process is terminated and some new files are created with .pdb, .kout and .lout extensions. This conversion is fully automatic, and the resulting files can be inspected through Greater right clicking on a compound line and selecting "view text files" from the pop-up menu. The GRINLOUT window contains messages concerning the GRIN run so it is always a good idea to check the content of this file/window.
The GRID-kout files are now ready so we have to choose the probe to use for the GRID run; select Probes->Choose probes from the greater menu and click on the OH2 probe so its "selected" status changes to "IN". Press OK and notice how the OH2 probe was added to "probes" on the upper right corner of the Greater window.
Now we are going to change some of the default parameters for the GRID computation; these are located in the lower right corner of the Greater window.
In the "box" section, click on NPLA and change the value from 1 to 2, then press the OK button; this changes the grid spacing to 0.5Å.
Now that everything is ready select Compute->Run from the menu or press the Run button and the following window will appear:

Select "visualise fields", write a Job title and start the actual GRID computation on the 20 compounds pressing the Run button.
Notice how the status for ALL the compounds changed to "running" at the very same time. That is the signal Greater is running GRID on the set of molecules as a whole.
We obtain the output files, grid.kont and grid.lont. GRID has calculated the interaction between a water molecule and our molecules at a large number of grid nodes (about 120000 for each probe). As we did not define explicitly the cage size, GRID has used a cage large enough to enclose all the compounds. The default grid space of 1 Å was changed at 0.5Å to produce better interactions estimation. All these interactions are, in binary format, into the grid.kont file.
54.1.2. 3D Molecular Interaction Fields visualisation
In order to visualise the MIF together with the molecular structures, select Targets->View field from the menu or press the View button.
The yellow region indicate interaction energies around the molecule This contour level has been automatically selected by Gview programme.
To change the energy level, select:
Edit->Field style
Move the cursor of the interaction energy levels, and then press Exit. To change the molecule's style, press:
Edit->Molecule style change the Rendering style to Sticks. Then press Exit
Move the image pressing the left button of the mouse and moving the mouse at the same time.
Gview automatically displays the KOUT structures with the associated MIF for the selected probe and, since the GRID kont file contains more than a molecular structure, you can use Gview to browse the structures and the MIFs in the graphic window:
Edit->Select
Click on tab called "Field", scroll down the below listed molecules and then click Exit.
For more information about Gview setting click here.
The produced MIFs can be correlated to a complex biological response, through useful molecular descriptors from a computational procedure called VolSurf. Using PLS, available in the VolSurf package http://www.moldiscovery.com/soft_volsurf.php) the Human Intestinal Absorption %HIA of this series of molecules ca be related to the GRID MIF obtained from water and DRY probe (the DRY probe was not used in this example).

Typical example of correlation is reported in the Figure above, where the experimental data are reported against the calculated one obtained from VolSurf in the GRID Molecular Interaction Fields.
Summing up, the MIF maps can be easily generated, they are easily interpreted in terms of molecular structure and are very suitable both for designing test sets and for quantitative structure-property modelling.
You have now completed your seventh Tutorial. Well done!! We look forwards to hearing from you if we can help in any way.
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