becoming a power user of forge - cresset€¦ · becoming a power user of forge central to any...

1Cresset European User Meeting 2016

Becoming a Power User of Forge

Forge is Cresset’s workbench for ligand based design. It uses ligand alignment as a basis for SAR interpretation, model building and new molecule design.

In this workshop we will be focusing on alignment of compounds in 3D. This is Forge central experiment as we use alignment as the key to virtual screening, ligand design and SAR interpretation.



Central to any Forge experiment is the alignment of all compounds in 3D.

Molecules are aligned to a template which can come from a protein xray structure or from a pharmacophore analysis using the built in FieldTemplater module.

In the graphic, there are three structures that are known to be active against CCR5. Using FieldTemplater, you can cross compare these active molecules’ conformations to better understand the binding and the pharmacophore.

Shown above are three compounds showing activity against C‐C chemokine receptor type 5 (CCR5). CCR5 is a transmembrane GPCR protein which is expressed on T‐cells, amongst other places. It has been postulated and research supports that this protein plays a role in inflammatory responses to infection and provides a pathway for HIV entry, making it a well‐studied target for HIV and other anti‐viral research.



Because our ligand‐based approach is a 3D method, a few words need to be said about how we handle molecule conformation in the Cresset environment.

There are two types of molecules in the Cresset software:

(1) Reference molecule – This molecule has the field point pattern that is our ‘gold standard’, i.e., the bioactive conformation with the field point pattern that we want our designed molecules to look like in order to have comparable activity against a particular receptor. The reference molecule may be a ligand from a protein‐ligand crystal structure, a pharmacophore hypothesis, or a set of pre‐aligned single conformations of different molecules.

(2) Comparison molecules – Any molecule that will be compared to the reference molecule is treated as a conformation population. The field point patterns are extremely dependent on the molecule’s conformation, so when we compare molecules, we compare a number of the low energy conformations to the reference molecule. If conformations are already generated, the Cresset software will read those in; otherwise, it will generate the conformations using the XED force field.



Cresset European User Meeting 2016


Two alignment protocols are available in the Cresset environment; each with their own benefits and liabilities.

First, there is our standard field‐based alignment, in which we use a combination of electrostatics, shape, and hydrophobic field points to align molecules, and calculate a score using a combination of these feature alignments. This approach is independent of chemical structure, and works well for libraries where there is a large degree of structural diversity.

Looking at the results from the field‐based alignment, we see that the alignment is in tune with what we would expect from docking results or x‐ray data, in that groups do not necessarily overlap perfectly. Examining bioactive poses of different ligands in protein‐ligand crystal structures, we find it to be quite rare that substantial portions of ligand structures are perfectly overlaid or atoms found in the exact same places. Similarly, we see this deviation from perfect overlays in poses from docking experiments.



While the field‐based alignment may provide a more sensible alignment of features in the context of what the protein receptor ‘sees’, the MCS method produces an alignment where common structure is overlaid so that non‐common features can be quickly isolated and examined – very much a ligand‐centric approach. This can be useful for identifying SAR for a library built on a single scaffold with functional variations around the core. For more diverse sets, the MCS option is not preferred.

Let’s explore how these alignment algorithms work.



In field‐based alignment, each conformation of the comparison molecule is subjected to a rigid body comparison to the reference (also rigid, by its nature as a reference).

To generate a starting alignment, a variant of colour‐coded clique matching is done where we build up cliques of field points and try to match edges and faces. Once an appropriate starting alignments are found, the structures are aligned according to least‐squares fitting of the field point maps, then submitted to a simplex optimizer.



In scoring the alignment, we use a combination of field and shape based similarity. We see for the molecules here, that we have a reasonable field similarity of 0.66. Looking at the shape similarity, we have a score of 0.98.



Using these two similarity features in combination, we get a combined score of 0.82. By default, we use 50% fields / 50% shape, but this option is customizable to the user preference.



On the other hand, if we are working with a concentric series and wish to have a specific common moiety forced together in the alignment, we can use the Maximum Common Substructure option in the alignment.

When the MCS option is turned on, a special conformation hunt is done. The common substructure with the reference molecule is held in the same conformation as the reference molecule and groups that are not part of the common substructure are conformation hunted. The resulting conformations are then scored against the reference molecule using the current scoring metric with the top scoring conformations being presented as the top scoring alignments.

Within the panel, we can also control how the routine handles hybridization and element differences:

With Normal, there is a strict matching of the substructure.

With Permissive, the method ignores element, but matches on atom hybridization. For example, cyclohexane would match morpholine, but not benze.



With Very Permissive, ring atoms can match non‐ring atoms. For example, butane can match (part of) cyclohexane.


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With multiple reference molecules, the alignments are scored against each of the references.

These scores are then combined in a way that is controlled by this option – either the weighted average of the scores is taken or simply the highest possible score can be taken forward.

In essence, in ‘Maximum’ mode the alignment tries to match the single most similar reference molecule, ignoring the others.

In ‘Weighted Average’ mode, the alignment tries to match all of the references simultaneously, applying the scores according to the weighting table that is shown.



In this workshop, we will discuss 4 typical alignment scenarios, which are presented on order of increasing availability of experimental information.



For the presenters: this slide should be hidden as it is not important for the purposes of the training, the user will find the Forge project in the training material. However, this is the workflow which was followed to create the Forge project just in case.



The histograms plots show the distribution of the activity and selectivity values in the data set of 62 compounds.

All 62 compounds have been tested for NaV1.7 activity, which spans a 3‐fold range from 4.4 to 7.7. These include 5 very weakly active/inactive compounds whose activity was reported in the original paper as % inhibition only. These have been assigned an activity of 40000 nM in the project (pIC50 =4.39).

Only 39 compounds have been tested for NaV1.5 activity, and have accordingly also a selectivity value.NaV1.5 activity spans a 2‐fold range from 5.6 to 7.7.Selectivity (pIC50NaV1.7 –pIC50 NaV1.5) also spans a 2‐fold range from ‐1.4 to 0.63.

The above ranges are possibly not large enough for a 3D‐QSAR model but should be sufficient for exploring the SAR of this dataset with Activity Atlas.



In this dataset, there are three compounds with NaV1.7 pIC50 7.7, which differ only for the decoration of the phenyl ring on the RHS.

Compound 32 (o,m‐diF) was preferred to 20 (o‐F) as a reference as the metasubstituent will help the alignment of meta‐substituted compounds. No special reason for choosing 32 over 33 (o‐F,m‐Cl).

1. Open the AA_AM_ChEMBL_start.fqj file. This contains the 62 NaV1.7 ligands to be aligned

2. Sort by descending activity3. Look at the three most active compounds (32, 33, 20). Select compound 32. Copy

it to a new role 'cpd32'4. Carry our a 'Very accurate but Slow' conf hunt of compounds 32, no alignment.

This may take a while if run locally: the role cpd32_finished contains the completed conf hunt.

5. Convert to alignment6. Visualize the conformations for Cmpd32 and select the lowest energy conformer.

Promote to reference.7. Align the training set to the reference: Accurate but Slow conf hunt and



Substructure alignment.

Note: AA_AM_ChEMBL.fqj contains the finished experiment.



Within Forge, there are two approaches to elucidating activity cliffs and SAR features: Activity Miner and Activity Atlas.

In order to use either of these approaches to elucidate a SAR, there are two conditions:

(1) Molecules must have activity data of some kind;(2) Molecules must be aligned.

Like all approaches to (Q)SAR in 3D, having a sensible alignment is critical to your success of extracting SAR information. It is advisable to spend time inspecting your alignments and tweaking where necessary to ensure sensibility. If you go back and re‐align after you’ve looked at a less‐than‐decent model, you are introducing bias.

Activity Miner calculates the similarities between all pairs of molecules (in their aligned conformation) then calculates a disparity matrix for all pairs. The disparity is then used to identify the activity cliffs, which can be used as a starting point to navigate and visually explore the SAR landscape.



Activity Miner is great with a smaller number of molecules, however, visualization when there are over 100 molecules can be complicated.

Activity Atlas is a good approach when working with a large number of compounds, or with datasets that have multiple chemotypes. Activity Atlas is used to summarize information from Activity Miner and to present it in a graphical and intuitive way.



The Activity Atlas model for NaV1.7 was calculated using the standard model building conditions.

In this workshop we will use only the results of the Activity Cliff Summary analysis. In the picture above, only the summary of electrostatics and shape are shown, as the summary of hydrophobic is largely superimposable to that of shape.



This is out standard example for training people on Forge alignment. In this workshop, everybody should be familiar with it: for this reason, I don’t think we should give a practical demonstration but in the next couple of hidden slides there is an example just in case. The files for this example are in the Case2 directory.



1. The PERK.fqj file contains a set of 25 PERK inhibitors, not yet conformationallyhunted and aligned.

2. Start from a blank project. Download the 4G31 protein and reference (Name 0WH) from the PDB. Let Forge choose the protonation state. Remove waters and other small molecules. Import as protein. Note: 4g31.pdb is saved in the Case2 directory in case there is no internet connection to the PDB.

3. Undisplay the protein. Double click on the reference to open the Molecule Editor. Check protonation, atoms and bonds. Minimize the reference and compare to original structure. Click OK to save changes.

4. Select the 25 molecules in the training set.5. Open the Forge Processing Panel. Choose Normal and Normal. Click Show

Options and discuss different options for conf hunt and substructure alignment.6. Optional: Copy the Training set molecules to a new Role named Normal. Repeat

the alignment on the Training Set molecules only using Normal/Substructure. Note that conformers from a Normal alignment cannot be used for MCS. Compare the two alignments.

7. NOTE: each conf hunt/alignment run takes 3‐4 minutes to complete.



FieldTemplater allows a modeller to take a series of known actives for which there is no knowledge of the bound conformation, and to compare the structures in a biologically relevant way.

FieldTemplater requires 2‐9 known active, efficient molecules that are believed to bind to the same active site. The system works best using 3‐4 actives to generate templates.

In the graphic, there are three structures that are known to be active against CCR5. Using FieldTemplater, you can cross compare these active molecules’ conformations to better understand the binding and the pharmacophore.

The template can be further used in FieldScreen or FieldAlign.

Shown above are three compounds showing activity against C‐C chemokine receptor type 5 (CCR5). CCR5 is a transmembrane GPCR protein which is expressed on T‐cells, amongst other places. It has been postulated and research supports that this protein plays a role in inflammatory responses to infection and provides a pathway for HIV entry, making it a well‐studied target for HIV and other anti‐viral research.



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Notes:

FieldTemplater is used for comparing molecules using their electrostatic and hydrophobic fields in order to find common patterns. When applied to several structurally-distinct molecules with a common activity, FieldTemplater can determine the bioactive conformations and relative alignments of these molecules without requiring any protein information.

FieldTemplater attempts to provide a full picture of how the active molecules bind, which features they use, what shape they are, and how different series can be compared.

The hypothesis on which FieldTemplater relies is that two molecules which both bind to a common active site tend to make similar interactions with the protein and hence have highly similar field point patterns. FieldTemplater thus searches for common field patterns across the explored conformational space of a set of ligands looking for commonality.

A field pattern which can be generated by multiple independent structurally-distinct molecules is likely to be related to how those compounds bind to a common receptor. Field points are used in the early stages of molecular alignment to give an approximate measure of commonality. This is then optimised using the full field. A set of hypotheses is thus produced, each of which suggests a bioactive conformation for each of the supplied molecules and presents how those bioactive conformations relate to each other. Each such hypothesis is termed a 'template'.

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Notes:

'Trio Templating' has some distinct advantages over choosing to build a template on alternately sized sets of molecules.

For example, using only 2 molecules can result in many different templates, making the choice of the best template difficult. Using 4 structures works nicely; with 5 or more structures the dependence on individual pairs combining well can increase, thus skewing the template. Six to nine structures will run, but it will be slow, as there are a large number of conformations of each molecule to be compared to a large number of conformations for every other molecule.

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Notes:

Each trio overlay is ‘scored’ according to its template similarity. Above we see the correlation between the template similarity score and the RMSD from the X‐ray poses of the ligands in their bioactive conformations.

When choosing molecules for your template, there are some considerations:

• Do not just choose the most active molecules• Ligand efficiency (in field space: binding energy per field point!)• Rigidity – more rigid ligands are better• SAR – more pre‐knowledge is better• Structural and chemical dissimilarity is good!• Ideally all mols from different conformation spaces (note that this does not necessarily mean different series)•Avoid molecules with solvating groups etc



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Notes:

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Notes:

There are some additional considerations when deciding the number of conformations each structure can assume for the overlay and template generation.

• There is a reliance on having a good conformation in the mix• The diversity of conformations is likely more important than completeness• A smaller number of conformations will give less noise (fewer than 100 is preferred)• General rules of thumb:

0‐3/4 rotatable bonds = 50 confs4‐5/6 rotatable bonds = 100 confs6‐7/8 rotatable bonds = 200 confs>8 rotatable bonds = ?? 300 confs

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1. The CCR5_start.fqj file contains a set of 242 CCR5 inhibitors which we want to align, not yet conformationally hunted.

2. The 4 active molecule which we want to use to build the template are in the role Molecules to be Templated. Select and send them to FT using the right click menu/Run FieldTemplater on Selected/test

3. This will open the FT window. You can see that all molecules have conformations, but there are no alignments and templates.

4. Set the FT processing as shown in the following slide:1. Add constraints to basic Ns (note: BCML has two basic Ns, the one to

constraint is on the 6‐membered ring)2. Normal (large mols) conf hunt (this step will anyway be skipped as all

molecules already have conformations)3. Normal alignment4. Normal (large mols) templating. In this panel, change the number of

minimum molecules per Template to 4.5. Start the search and kill it – it takes to long to be completed during the

workshop.

5. Open the ‘4mols_Nconstr_empty’ FT window. This contains conformations,



alignment and constraints for all 4 molecules, but no templates. Start the search using the templating conditions shown above. Inspect the templates. Right click on top scoring template (chosen conformations should be TAK 39, BCML 58, SCH B7, UK 15) and Send Selected Template to Forge. Import the template into the reference role.

6. NOTE: the ‘4mols_N_constrain’ FT window in CCR_start.fqj contains the finished FT experiment.



1. The 242 CCR5 inhibitors in the CCR_start file do not all belong to the BCML and UK series, which are those one we want to align.

2. Copy the BCML reference molecule.3. Add a structure/substructure filter. Open the molecule editor, paste the BCML

reference. Edit the molecule to make it simpler, keeping only the substructure shown above. Close the molecule editor.

4. Turn the substructure filter into a SMARTS filter, remove the charge on the N and the chirality labels to make the filter more generic. Make sure the filter is set to Include.

5. Select the Training set molecules which pass the filter, tag them as 'BCML'. 6. Set the structure filter to Don’t care. 7. Repeat with the UK‐427847 Reference.8. Add a filter on Tags, exclude BCML‐ or UK ‐tagged molecules from the Training set.9. Select the training set molecules which pass the filter (not tagged) and delete

them. Re‐include the tagged molecules.10. Remove TAK/SCH from the reference molecules.11. You should now have 190 molecules in the Training set, all tagged BCML/UK.

Select them and align only the selected molecules using an Accurate but slow conf hunt and a Very permissive MCS alignment with Maximum scoring. This is a



very long calculation, so kill it.

12. Note: the CCR5.fqj file contains the finished experiment.



I didn’t plan a practical session for this case, but should anyone ask:

1. Open CCR5_xray start in the Case 3 directory. This file contains the X‐ray structure of Maraviroc ('UK' compound in the previous case), downloaded from the PDB (code 4MBS)

2. Copy the 4MBS reference into the Molecules to be Templated role. Convert the X‐ray conformation into an alignment.

3. Remove UK‐427857 ‐ isomer 1 from Molecule to be Templated4. Rebuild the pharmacophore using the settings used in the previous case.

5. Note: the 4mols_constr_xray FT window contains the finished FT project.



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