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Virtual Screening in LigandScout

Ligands with similar pharmacohoric patterns can exhibit similar biological activities according to one specific protein. Thus, this chapter gives a few guide lines how to model a pharmacophore and find ligands of a molecule library, that match to that specific pharmacophore. You can use the virtual screening tool in LigandScout or through the command line .

Pharmacophore and Molecule Library Preparation

Pharmacophore Preparation

Create a pharmacophore model ( structure-based or ligand-based ), according to a target protein, that is capable to find a high amount of active ligands and as few as possible inactives. Here we refer a structure-based pharmacophore of 1ke7 complex as an example. One of the essential steps is an exhaustive literature search about your protein of interest. Where does the ligand bind? Which amino acids are required such that the ligand can interact with protein's binding pocket. Thus, this leads to the knowledge about features that should be contained in the pharmacophore. Which ligands bind to the protein and show biological activity and which do not? Load your protein-ligand complex in the Structure-Based Modeling Perspective , zoom into the active site, check the double bonds of the ligand and create a pharmacophore. Check if the essential interactions are present and edit the pharmacophoric features if required.
It might be that several pdb complexes are available and therefore many pharmacophore models can be generated. In the Alignment Perspective you can edit the pharmacophores, align them, generate a shared or merged feature pharmacophore. Just add your pharmacophores and ligands one by one to the Alignment Perspective and investigate them until you have created an appropriate pharmacophore.

Molecule Library Preparation

LigandScout allows you to screen single or multi-conformational molecule databases. To efficiently speed up the screening process, LigandScout uses the proprietary LDB file format for storing molecule libraries. Hence, it is necessary to convert your molecule library into the LDB file format before you load your library for the screening.
LigandScout offers several ways to generate a multi-conformational database for screening. If you have existing molecules available in LigandScout, you can export them via menu File > Save as File and choose the LDB file type. Alternatively, you can generate a new screening library from an input file via the Create Screening Database icon, menu Library > Generate Library from , the command line tool idbgen or the stand-alone idbgen-GUI. .
For instance, store an existing multi-conformational library screeningDB.sdf as screeningDB.ldb use the following command:

idbgen --input screeningDB.sdf --output screeningDB.ldb --confgen-type import

Another idbgen command example shows how to generate from a SMI file input.smi a multi-conformational database output.ldb :

idbgen --input input.smi --output output.ldb --confgen-type omega-fast

Run Virtual Screening

Add your pharmacophore model to the Screening Perspective , e.g., by the Copyboard Widget . Switch to the Screening Perspective and load a molecule library (.ldb) by clicking the Load Database button. Then, choose a pharmacophore. LigandScout offers you to select more than one database to be screened against the selected pharmacophore. Select and mark the database containing active ligands in green. If a decoy database is available that includes inactive ligands, mark it in red. To customize the advanced screening settings, toggle on the Show Advanced Options Settings in the Screening Panel . Then, start the screening process by pressing the Perform Screening button.
Optionally, you can screen a pharmacophore against an external database in the Structure-Based Modeling Perspective. Make sure, that one pharmacophore is visible in the 3D View and select in the menu Pharmacophore > Screen Pharmacophore Against External Library . A screening settings dialog appears. Select your molecule database and modify the settings as required.
To execute the virtual screening process through the command line, please see the section called “Virtual Screening” in the Appendix.

Screening Results

When the screening process is finished, a hit list of molecules, that matches the pharmacophore is shown in the Library View . Click the results one by one and look how the ligands are aligned to the pharmacophore in the 3D View. If you want to export your screening hits, select in the menu File > Save as File .
To see the ROC plot, press the Plot ROC Curve icon. The ROC curve shows how good the pharmacophore model discriminates between active and inactive ligands. The AUC value (area under the ROC curve) quantifies the ROC curve between 0 and 1. A value near to 1 is favorable. If you want to export the ROC plot as image, select in the menu File > Save as File . Choose an appropriate image file format (e.g. PNG) and enter a file name in the Save as dialog. Then, select the ROC check box plot of the 3D export properties.
If the screening results produce less active hits, there can be several reasons. Question yourself, if the library ligands violate excluded volumes or is the pharmacophore too restrictive? Are the conformers of the ligands meaningful? Are essential features found in the ligands? To get more hits, edit your pharmacophore model. For instance, remove an excluded volume or other features or make the tolerance sphere larger and screen again. Another way is to raise the number of maximum omitted features , e.g., 1 or 2, depending on the size of the pharmacophore or designate some features as optional. To do so, not all of the resulting ligand hits may contain all of the features of the pharmacophore model, but again these can lead to interesting results. If you have the problem that too many hits are found, then the model is too general and you should restrict your pharmacophore model. You can create excluded volumes or an excluded volumes coat . An excluded volumes coat consists of many small excluded volumes which reflect the spatial geometry of the binding pocket. Note that the combination of generalizing and restricting the pharmacophore model is essential for influencing the hit results.
After all, finding a representative pharmacophore model of a ligand-protein binding mode needs a lot validation. One approach is to include active and inactive ligands in the molecule library. If most of the active ligands are contained in the hit results, then the pharmacophore covers the actives molecules well. On the other hand, if inactive molecules are matched, then the pharmacophore might possess the main feature scaffold matching the active ones; however pharmacophore model could not differentiate between the active and inactive ligands. In this case you will need to build an improved model.

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Page designed & authored by G. Wolber

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