Introduction to Pharmacophores in MOE

Introduction to Pharmacophores in MOE MOE's pharmacophore modeling tools determine the chemical features and their spatial arrangement in 3D that are essential to the binding of a ligand to its receptor and thus to the ligand's drug activity. Pharmacophore models can be generated from the structural data of protein-ligand complexes as well as from ligands when no receptor information is available and from the receptor structure when no ligands are available. The generated models can be used to screen virtual compound libraries for potentially active molecules. 

What is a Pharmacophore?

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Pharmacophore Applications in MOE o Generating Pharmacophore Queries o Using Pharmacophore Queries

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Introduction to Pharmacophore Modeling and Searching o Preparing the Data o Pharmacophore Annotations 

About Annotation Points

o Manual Query Generation 

Example: Pharmacophore Annotation Points for the 1HPV Ligand

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Creating Features 

Example: Creating and Editing Pharmacophore Query Features for the 1HPV Ligand

o Automatic Query Generation 

Consensus Features

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Pharmacophore Elucidator

o Performing a Pharmacophore Search

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Example: Pharmacophore Search Using 1HPV-query1

o Refining the Query 

About Constraints

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Example: Refining the 1HPV-query1 Pharmacophore Query

o Validating the Query 

Example: 1HPV-query2 Query Validation

o Automatic Pharmacophore Query Generation 

Running in MOE/batch

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References

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Pharmacophore Reference

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SVL Commands

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See Also

What is a Pharmacophore? The term pharmacophore was coined by Paul Ehrlich in 1909 to mean "a molecular framework that carries (phoros) the essential features responsible for a drug's (pharmacon) biological activity" [Ehrlich 1909]. In other words, a pharmacophore is a 3D model describing the type and location of the binding interactions between a ligand and its target receptor. The IUPAC definition [IUPAC 1998] is more precise: "A pharmacophore is an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response." The term supramolecular means noncovalent. Typical pharmacophoric features include hydrogen bond donor, hydrogen bond acceptor, hydrophobic, and positively and negatively ionized areas.

A pharmacophoric feature characterizes a particular property and is not tied to a specific chemical structure; indeed different chemical groups may share the same property and so be represented by the same feature. It is thus a mistake to name as pharmacophoric features chemical functionalities such as guanidines or sulfonamides or typical structural skeletons such as flavones or steroids [IUPAC 1998]. The term pharmacophore modeling refers to the generation of a pharmacophore hypothesis for the binding interactions in a particular active site. Several different pharmacophore models for the same active site can be overlaid and reduced to their shared features so that common interactions are retained. Such a consensus pharmacophore can be considered as "the largest common denominator shared by a set of active molecules" [IUPAC 1998].

In MOE, the computerized representation of a hypothesized pharmacophore is called a pharmacophore query. A MOE pharmacophore query is a set of query features that are typically created from ligand annotation points. Annotation points (automatically detected in MOE) are markers in space that show the location and type of biologically important atoms and groups,

such as hydrogen donors and acceptors, aromatic centers, projected positions of possible interaction partners or R-groups, charged groups, and bio-isosteres. The annotation points on a ligand are the potential locations of the features that will constitute the pharmacophore query. Annotation points relevant to the pharmacophore are converted into query features with the addition of an extra parameter: a non-zero radius that encodes the permissible variation in the pharmacophore query's geometry. Once generated, a pharmacophore query can be used to screen virtual compound libraries for novel ligands. Pharmacophore queries can also be used to filter conformer databases, e.g. output from molecular docking runs, for biologically active conformations.

Pharmacophore Applications in MOE Generating Pharmacophore Queries In MOE, various tools are available for generating pharmacophore queries depending on the kind of structural data available. Ligand-Based. The pharmacophore query is determined from a collection of active (and possibly inactive) ligands. No target information is used. MOE applications relevant to this task include: 

Flexible Alignment. Flexibly align small molecules.

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Pharmacophore Consensus. Obtain consensus features from aligned ligands.

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Pharmacophore Elucidation. Automatically determine a pharmacophore query from active (and optionally inactive) ligands.

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Pharmacophore Query Editor. Create new and edit existing pharmacophore queries.

Complex-Based. The pharmacophore query is determined from the structural data of protein-ligand complexes. MOE applications relevant to this task include: 

PLIF. Generate a pharmacophore query from a consensus of Protein Ligand Interaction Fingerprints.

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Pharmacophore Query Editor. Create new and edit existing pharmacophore queries.

Target-Based. The pharmacophore query is determined from the structural data of the receptor only. MOE applications relevant to this task include: 

Site Finder. Locate binding cavities on a macromolecular structure.

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Surfaces and Maps. o Electrostatic Feature Map. Locate binding interaction sites using electrostatics. o Interaction Potentials. Predict the preferred location of userspecified probe atoms, o Non-Bonded Contact Preferences. Locate binding interaction sites using statistics culled from the Protein Data Bank.

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Pharmacophore Query Editor. Create new and edit existing pharmacophore queries.

Using Pharmacophore Queries Pharmacophore models can be used in various MOE applications: 

Pharmacophore Search. Search 3D conformation databases for structures that match the pharmacophore query. The Conformation Import application can be used to generate 3D conformation databases from vendor catalogs and in-house databases.

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Dock. Generate binding configurations of ligands to a macromolecular target. A pharmacophore query can be used to determine the initial placement of the ligand and to constrain the final poses.

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Scaffold Replacement and Fragment Linking. Scaffold Replacement. Replace the chemical core or scaffold of a biologically active series of compounds in an effort to improve activity and/or other desirable properties. Fragment Linking. Connect disconnected fragments (usually chemical entities whose binding positions in the pocket are known) to create a new molecular entity which binds to the pocket through the favorable binding locations of the constituent fragments.

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QuaSAR-Reagent. Estimate the contribution to biological activity of the R-groups and scaffolds of a virtual combinatorial library. Activity can be assessed using different kinds of models, including pharmacophore.

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Model-Composer. Create a composite model from one or more models of varying types, including pharmacophore.

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Model-Evaluate. Evaluate a model, which may include a pharmacophoric component, on a molecular database.

Introduction to Pharmacophore Modeling and Searching In MOE, creating a pharmacophore model or query involves a number of steps: 1. Prepare the data. 2. Generate pharmacophore annotation points. 3. Select features to include in the pharmacophore query. 4. Refine the query.

Preparing the Data The kind of preparatory steps needed depends on what data are available. For example, since a pharmacophore model ought to be representative throughout the 3D conformational space occupied by a set of ligands, in ligand-based query generation, a set of input conformations must be generated if they are not already available. Furthermore, for subsequent annotation using the automatic annotation services of the Pharmacophore Query Editor, these conformations must represent "exact" (i.e. washed) structures. For example, carboxylate is considered structurally distinct from carboxylic acid: no implicit tautomer transformations are performed. Molecules are annotated as-presented. If exact structures are not already available, they must be generated too.

Acetate

Acetic acid

In complex-based query generation, should the crystallized complex be available, ligand conformation is no longer an issue as the structure contains the ligand in its bound form. This simplifies the task of determining a pharmacophore for the target and minimal data preparation is necessary. In target-based query generation, exploratory investigation of the receptor is needed to locate binding cavities and identify within them where and what kind of important interactions take place. This kind of investigation can also used in the query refinement step.

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For a detailed explanation of how to prepare a database of conformations, please refer to Preparing a 3D Structure Database.

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For an example of how to prepare a database of conformations, please refer to the Pharmacophore Tutorial.

Pharmacophore Annotations Annotation is the process of identifying regions of pharmacophoric importance in space around a molecular conformation and associating with those regions pharmacophore types. In MOE, the Pharmacophore Query Editor both annotates the molecular system currently loaded in MOE as well as renders the resulting annotations graphically in the MOE Window. Locating and assigning pharmacophore annotations can be done in different ways. Although MOE provides a variety of built-in pharmacophore annotation schemes or simply schemes, each of which has its own particular set of annotation types, the default Unified scheme is to be preferred as it is comprehensive, providing the annotation types of all the other schemes without loss of efficiency. Once assigned, annotations can be turned into features using the Pharmacophore Query Editor. Note, however, that it is not a requirement that a feature be created from an annotation. Features can be created, positioned, and assigned pharmacophoric types within the Pharmacophore Query Editor independent of annotations, although it is rarely necessary to do so. Annotations thus serve as suggestions for which type of features to use and where they should be located. In almost all cases, features are created from annotation points.

About Annotation Points Annotation points can be broadly divided into three categories: atom, projected, and centroid, the latter including bioisosteres. A special fourth class of annotations, links, denotes points of substitution on candidate scaffold molecules and potential R-group substituents, and is designed to assist in scaffold replacement or mimetic projects. Atom Don

H-bond Donor

Acc

H-bond Acceptor

Cat

Cation

Ani

Anion

ML

Metal

Atom annotations are located directly on an atom of a molecule and typically indicate a function related to protein-ligand binding.

Ligator Hyd A

Hydrophobi c Atom Projected

Don2 Projected Donor Acc2

Projected Acceptor

ML2

Projected Metal Ligator

PiN

Ring Normal

Projected annotations are (typically) located along implicit lone pair or implicit hydrogen directions and are used to annotate the location of possible hydrogen bond or metal ligation partners, or possible R-group atom locations.

Centroid and Bioisostere Aro

Aromatic

PiR

pi-Ring

Hyd

Hydrophobi c

CN2

NCN+

O2

COO-

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Centroid annotations (Aro, PiR, CN2) are located at the geometric center of a subset of the atoms of a molecule. Bioisostere annotations (CN2, O2) indicate locations for bioisosteric replacement, i.e. substituents which may be exchanged for other substituents having similar physical or chemical properties such that biological properties of the parent compound are preserved or enhanced.

For more details, please refer to the Pharmacophore Annotation Reference.

Manual Query Generation A pharmacophore query can be created manually using the Pharmacophore Query Editor, which is the primary MOE interface for creating and editing pharmacophores queries. The Pharmacophore Query Editor is used primarily to manually select annotation points and convert them into pharmacophore features.

The Pharmacophore Query Editor is also used to: 

Add and remove query features.

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Modify feature attributes, e.g. type and radius.

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Add expressions, including SMARTS filters, to features.

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Add volume constraints.

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Add partial matching options.

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Visualize pharmacophore queries and customize rendering options.

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Launch the Pharmacophore Consensus panel, which is used to suggest a consensus set of pharmacophore features.

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For more details, please refer to the Pharmacophore Query Editor Reference.

The panel is opened with the commands: MOE | File | New | Pharmacophore Query or MOE | Compute | Pharmacophore Query

Pressing the Scheme: Info button in the panel opens a list of the different annotation types and controls which ones are displayed in the Main Window. The Ligand Annotation options in the bottom of the panel control which atoms are annotated (e.g. visible atoms or atoms of selected chains only) and how the annotation points are displayed (e.g. as points, with or without labels). For example, in Long mode, each annotation point is labeled by its annotation type.

Example: Pharmacophore Annotation Points for the 1HPV Ligand The input data for this example is the crystal complex of 1HPV taken from the PDB. Since this complex provides the bound conformation of the ligand, conformational analysis is unnecessary. The only preparatory steps necessary for annotation are loading the data into MOE and then, for convenience, visually isolating the ligand.

1. Load the file. File | Open $MOE/sample/mol/1hpv.pdb Press OK in the Load PDB File panel (use the default settings). The structure of 1HPV should appear in the MOE Window. 2. Isolate the ligand. a. Hide all atoms. MOE | Popup | Hide | All Atoms b. Show just the ligand atoms. MOE | Popup | Show | Ligand 3. Center the view. MOE | RHS | Center 4. Render the ligand in stick mode. MOE | Footer | Atoms | Stick The ligand VX-478 should now be isolated in the MOE Window. This is an orally active HIV-1 PR ligand. To annotate the ligand, open the Pharmacophore Query Editor. MOE | File | New | Pharmacophore Query When the panel opens, the ligand is automatically annotated. By default, a wide variety of the calculated annotations are rendered in the MOE Window. For the purposes of the present example, the annotation types of interest are: Don

Donor Atoms

Acc

Acceptor Atoms

Aro

Aromatic centroids

Hyd

Hydrophobic atoms and centers

Pharmacophore annotations These may be isolated visually by turning off the display of all other types. This can be accomplished using the Unified Info panel, which is opened with Scheme: Info This panel lists all the types of annotation points defined in the current scheme. 1. Turn off the display of all annotations. Show | None 2. Turn on the display of the desired annotations. Use the checkboxes in the Unified Info panel. The HIV1-PR ligand should now be annotated with 4 kinds of annotation points. Creating Features

A feature can be added to a pharmacophore query by selecting one or more corresponding annotation points in the MOE Window and then pressing the Create: Feature button in the Pharmacophore Query Editor panel. The newly created feature appears in the panel list area, which displays each feature's automatically-generated name and an associated expression that describes the feature. To the right of the feature list are buttons for deleting features from the query and for changing their position in the list (for purposes of better visualization).

Each feature in the query has: 

A location in space, which can be modified directly in the MOE Window in the same way as that of an atom: by selecting the feature (either in the panel or in the MOE Window) and then using Shift-Alt-drag middle;

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A radius, which is controlled by the value of R in the panel; and

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A logical expression called a feature expression that may include both annotation point types and SMARTS strings. A feature inherits its types from its generating annotation points.

Once completed, a pharmacophore query can be saved to a MOE pharmacophore file for future use by pressing Save in the Pharmacophore Query Editor. The default extension is .ph4. This file can subsequently be edited with the Pharmacophore Query Editor or used by other applications such as Pharmacophore Search or Dock.

Example: Creating and Editing Pharmacophore Query Features for the 1HPV Ligand

1HPV pharmacophore query features For the HIV-1 PR ligand, a simple pharmacophore query can be created using four features, one for each of the rings, and one associated with the isopropyl group. 1. Create a feature for each ring. For each of the rings of the ligand do the following:

a. In the MOE Window, select the annotation point at the center of the ring. If two annotation points overlap, select them both. b. Press Create: Feature in the Pharmacophore Query Editor. A new feature will be drawn in the MOE Window and will appear in the Pharmacophore Query Editor list. One of the rings will result in a feature labeled Hyd, and two in features labeled Hyd| Aro. 2. Change the feature color. Select all features in the list with Popup | Select All, then use the Color option to change the color of all features to Green. The features can also be edited singly by selecting them one at a time and making the changes.

3. Change the feature radius. With all features selected, change the radius of every feature to 1.5 by entering the value in the R field or by using the dial. The changes take effect immediately. 4. Create an isopropyl group feature. Select the 3 annotation points on the isopropyl group. Press Create: Feature to create a Hyd feature at the centroid of the annotation points. Select the new Hyd feature and change its color to green and its radius to 1.5. 5. Change the feature type. In the feature list, select both Hyd features (select one, then hold the Ctrl key to toggle the selection of the second feature). Change the feature type of both features by

modifying the contents of the F* textfield to Hyd|Aro. Press Apply to put the change into effect. 6. Save the pharmacophore query for future use. Press Save. In the Write Query File panel, enter 1HPV-query1.ph4. Press OK.

Automatic Query Generation When multiple ligands are available (and assuming they bind in similar ways to the receptor), it is possible to automate at least parts of the query generation process. Pharmacophore Consensus can be used to suggest features that are common to many of the actives, and is an aid to manual query generation. Pharmacophore Elucidator generates and scores pharmacophore queries for a set of aligned molecules, and little or no manual intervention is required. Both applications require molecular conformations. Consensus Features The Pharmacophore Consensus application calculates a set of consensus features from the aggregate annotation points of a set of aligned molecules. The features are scored based on the number of molecule in which they appear, and are easily included in a pharmacophore query. The molecular conformations must have been precalculated and aligned and loaded into MOE.

The Pharmacophore Consensus panel is opened from the Pharmacophore Query Editor by pressing Consensus (at top right). Once the suggested features have been calculated (by pressing Calculate in the Pharmacophore Consensus panel), they can be transferred to the invoking Pharmacophore Query Editor by selecting them in the Suggested Features list and pressing Load Selected. The selected features will be appended to the feature list in the query editor. 

Please refer to the Pharmacophore Tutorial for an example of how to use the Pharmacophore Consensus application.

Pharmacophore Elucidator The Pharmacophore Elucidator exhaustively generates pharmacophore queries for a set of aligned molecules. and scores them based on a combined measure of how well they overlay the ligands and how well they classify the data set into actives and inactives.

Molecular conformations can be either read in or calculated at runtime by choosing one of the conformation generation methods from the Conformations option menu (As-Is implies that the conformations have been precalculated). To run the application, an Output Database name must be supplied. The Active Coverage (which is the number of active molecules represented by the given query) and Feature Limit (maximum number of pharmacophore features) parameters can be adjusted based on the amount and type of data available. Query Spacing and Query Cluster are resolution tuning parameters used to determine when a query is considered to be representative of a molecule and when two queries are considered to be the same. Only different queries that satisfy the Active Coverage and Feature Limit parameters are output.

For more details about scoring and query generation, please refer to the Pharmacophore Elucidator manual page.

Performing a Pharmacophore Search Pharmacophore searching in MOE is performed using the Pharmacophore Search application. There are two ways to invoke Pharmacophore Search: 

In the Pharmacophore Query Editor panel, press Search.

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In a MOE Database Viewer, select DBV | File | Pharmacophore Search.

When invoked from a Pharmacophore Query Editor, the query currently open in the Editor is used for the search. This launch point is convenient when the query is undergoing iterative refinement, and search results are being examined after each modification step to evaluate the changes being made. A Pharmacophore Search panel launched from a Pharmacophore Query Editor automatically uses the latest version of the pharmacophore query for its search. The databases to be searched are supplied when Search is pressed, and can be modified from within the Pharmacophore Search panel using the Input controls. If Pharmacophore Search is invoked from a Database Viewer, a pharmacophore query must be specified in the Pharmacophore Search panel using the Query: Browse button. A Pharmacophore Query Editor can also be opened from the Pharmacophore Search panel using Query: Edit to edit the query currently being used by the Search panel.

In general, the bound conformations of the ligands being searched are not known. A conformational search must be performed on the candidate molecules prior to initiating the search (or existent conformational databases used); the pharmacophore search is then applied to those conformations. The search proceeds by annotating each candidate ligand conformation and then attempting to match the annotations to the constraints of the pharmacophore query such that all restrictions of the query are satisfied. There can be more than one way in which a candidate conformation can match a query. Successful matches (hits) can be written to an output database using the Output options; they can also be selected or isolated in the input Database Viewer using the Hit Entries menu.

Example: Pharmacophore Search Using 1HPV-query1 In the 1HPV example, a prepared database of bound conformations of HIV-1 PR inhibitors is already available so no initial conformational analysis need be done.

1. Open the database of bound conformations of HIV-1 PR inhibitors. MOE | File | Open hiv_1pr_ligands.mdb 2. Open the Pharmacophore Search panel from the Database Viewer. DBV | Compute | Pharmacophore | Search 3. Specify the query. Press Query: Browse to open a file selection box. In the resultant Open File panel, locate and select the query file 1HPV-query1.ph4 (from above); press OK. The query file name now appears in the Pharmacophore Search panel. 4. Choose how the search results will be reported. In the Pharmacophore Search panel, choose Hit Entries: Select. This will cause the

search hits to be selected in the Database Viewer. Keep the default parameters in the Results area of the panel: the output conformations will be written to the file ph4out.mdb. 5. Perform the search. Press Search to begin the pharmacophore search. The status line at the bottom of the panel reports the current status of the search. Hits will be selected in the Database Viewer as the search proceeds. 6. Examine the results. When the search is finished, the status line reports the number of unique molecules searched and the number of unique molecules of which at least one conformation was found to be a hit. In this case, almost all the molecules are search hits. The hit structures output to the database ph4out.mdb are aligned to the query. Press Results: Open to open the output database in a Database Viewer.

Analyzing the 1HPV-query1 Search Results The output of the search can be visually compared to the to the pharmacophore query by browsing through the database of output molecules while the pharmacophore query is rendered in the MOE Window.

1. Close the current molecular system. MOE | File | Close 2. Open the query. MOE | Open | 1HPV-query1.ph4 3. Run the Database Browser. In the output Database Viewer (ph4out.mdb), choose DBV | File | Browser

The first structure from the database being viewed is automatically loaded into MOE.

Browsed molecule with query 4. Change the molecule rendering to stick mode. MOE | Popup | Atoms All subsequently browsed structures will be rendered in this mode. 5. Hide the query annotation points. In the Pharmacophore Query Editor, choose Ligand Annotation: None 6. Scroll through the database of search hits. In the Database Browser, use the arrows or the slider. Notice the many different orientations of the ligands. It is likely that many of these would clash with the receptor.

The 1HPV-query1.ph4 pharmacophore query is rather simplistic and would probably match many inactive compounds. At the same time, it successfully matched most of the candidate inhibitors. This suggests that the query may be a reasonable starting point for a more advanced query. In the next section, query refinement will be discussed.

Refining the Query Pharmacophore queries can be improved by an iterative cycle of refinement and validation. The goal is a query with a high true positive rate and low false positive and false negative rate. A simple pharmacophore query, for example the one presented in the HIV-1 PR example, might use

only ligand information. If the receptor structure is available, knowledge of the binding pocket can be used to enhance the query with steric and other structural constraints. Some strategies for refining pharmacophore queries include: 

Using constraints. MOE pharmacophore queries can be enhanced with a variety of constraints. These can be used, for example, to characterize the structure or other properties of the binding pocket. o Volumes. Volume filters help avoid clashes with the receptor and confine hits to limited spatial regions. o Logic constraints and SMARTS expressions. Explicit descriptions of permitted types and chemical environments can be associated with both features and volumes. o Partial Matching and Essential Features. Both optional and required features can be specified.

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Adding / deleting features and volumes. Larger numbers of features may reduce the hit rate but increase specificity.

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Determining features from pocket analysis. Examination of the binding pocket, for example in terms of electrostatics and preferred contacts, can reveal potentially important pharmacophoric features.

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Adjusting feature radius and position. Custom fine-tuning can improve performance. Note that features are normalized with respect to feature size (radius) so that large radius features (i.e. features with higher positional tolerance) are not given more importance than small radius ones.

When further refinement of a pharmacophore query using explicit 3D constraints yields little gain, 2D modeling approaches in MOE can be used to refine the pharmacophore search results. For example, a binary QSAR filter could be used to pre- or post-process the hit list. 

Please refer to the Pharmacophore Tutorial for an example of how to refine a query using interactive tuning and directional features.

About Constraints MOE pharmacophore queries can be refined with a number of different constraints: Volume and Shape Filters

Volume constraints are spatial constraints imposed on particular atoms. These constraints serve both to confine hits to spatial regions in which steric clashes with the receptor are avoided as well as to ensure that favorable contact regions will be populated with appropriate atoms. Each volume constraint, analogously to features, has an associated volume expression. The volume expression is a SMARTS string. The empty string (i.e. if no expression is specified) is equivalent to [#Q], which means "any heavy atom". The volume constraint places restrictions only on atoms that match the volume expression. There are three types of volume constraint:

Excluded volume. No atoms matching the volume expression are permitted inside the volume (useful for modeling receptor shape). The volume delimits the region of space that may not be occupied by the prescribed atoms.

Exterior volume. No atoms matching the volume expression are permitted outside the volume (useful for modeling ligand shape). The volume delimits the region of space (the exterior boundary) that may be occupied by the prescribed atoms.

Included volume. At least one atom specified by the volume expression must be located inside the volume.

The volume constraint is specified as one or more spheres where each sphere is centered on a selected atom. Tip Volume constraints are evaluated on atom centers, i.e the atoms are considered to be points having zero radii. As a consequence, there still may be clashes between the hit molecules and the pharmacophore volumes. It is advisable therefore to take into account the van der Waals radii of candidate ligand atoms by calculating the Interaction (VDW) surface and adjusting (increasing, if excluded volume, decreasing otherwise) the radii of the volume constraints so that they just penetrate the interaction surface. To allow for

some tolerance, relax (decrease if excluded volume, increase otherwise) this radius by 0.3-0.5Å. Feature Expressions These are logical constraints that can be added to both features and volumes, e.g. Don & Acc and can help focus the query toward a specific property. SMARTS Matches The chemistry of a group (substructure) to be matched can be specified within a feature expression using SMARTS strings, e.g. Don & Acc ! "[N]" Partial Matches Allowing partial matches permits optional features to be included and can make the query less specific. Essential Features Features marked essential to the query must always be matched, even when partial matching is enabled. Group Constraints These specify that a certain number of the specified features must be matched. This kind of constraint may be useful for accounting for alternative feature locations, e.g. to allow for sidechain flexibility. 

Please refer to the Pharmacophore Tutorial for an example of how to use volume constraints.

Example: Refining the 1HPV-query1 Pharmacophore Query An analysis of properties of the binding pocket can suggest features to include in a pharmacophore query. In the following, a pocket analysis is first demonstrated, then the results of the analysis applied to refining the simple query that was generated in earlier. Analyzing the Binding Pocket The binding pocket can viewed both in 2D and 3D to help identify important points of interaction. A 2D depiction of the ligand interactions with the pocket can be obtained using the Ligand Interactions application. With $MOE/sample/mol/1hpv.pdb loaded in the system, open the panel using MOE | Compute | Ligand Interactions

In the Ligand Interactions panel, press Isolate to isolate the ligand and pocket residues in the MOE Window. Turn on hydrogen bond display with MOE | Footer | Contacts A bound water HOH_201 (also bound to ILE50) and an H-bond interaction of the ligand hydroxyl group with residue ASP25 can be observed in both the Ligand Interactions panel and the MOE Window.

Water HOH_201 bound to ILE50 and H-bond to ASP25

Now we will look more closely at the ligand-pocket interactions in 3D.

Protonated ligand and receptor 1. Protonate the system. For accurate drawing of surface interaction maps and of creation of volume constraints, explicit atoms are required. Currently, the system is in hydrogen-suppressed mode. The system can be protonated using MOE | Compute | Protonate 3D In general, explicit hydrogen atoms are required for all-atom molecular mechanics, dynamics, and electrostatic calculations. The Protonate 3D application assigns ionization states and calculates hydrogen positions in a macromolecular structure. In the Protonate 3D panel, press OK. The calculation may take a few moments to complete; progress is reported in the MOE Window.

2. Ligand and receptor, hydrogens hidden

Hide non-polar hydrogens. Hiding the non-polar hydrogens permits a clearer view of the binding site: MOE | RHS | Hydrogens Press the Hydrogens buttons successively until the hydrogen display is as desired. Successive presses cycle between no hydrogens displayed, polar hydrogens only, and all hydrogens. Closer examination of the binding site will reveal that the water hydrogens are wellpositioned for hydrogen-bonding as is the ligand hydroxyl hydrogen. The ASP25 residue is now protonated as well, and donates an H-bond to the ligand hydroxyl group. In this image, hydrogen bonds are displayed as white dotted lines, whilst red and purple dotted lines represent ligand-solvent and ligand-pocket interactions respectively, as calculated by the Ligand Interactions application. 3. Draw the pocket. The binding pocket can be depicted using a 3D molecular surface. Select MOE | Compute | Surfaces and Maps

Molecular surface of pocket, top and side views Press Apply to calculate the Molecular Surface around the receptor atoms near the ligand. Here, Color has been set to Constant, with the Surface Color orange and the surface type Solid. By rotating the view to obtain a side view, the active site channel between the A and B chains of the receptor can be seen.

In these images, the transparency of the surface has been adjusted (using the TF and TB sliders in the Surfaces and Maps panel) for better ligand visibility.

4. Calculate pocket electrostatics. The electrostatic maps of the 1HPV pocket predict where hydrophobic, hydrogen bond acceptor and hydrogen bond donor contacts are preferred. Partial charges must be assigned to the system before the calculation is performed. Here, the charges have already been assigned by the Protonate 3D application. In the Surfaces and Maps panel, change the Surface to Electrostatic Map. Press Apply. The calculation solves the Poisson-Boltzmann equation for the electrostatic potential, which is then used to generate the acceptor, donor, and hydrophobic predictive maps. The maps show the locations in space at which an atom of the given type has a potential equal to the value (in kcal/mol) given by the corresponding Level slider. We will now examine each of the electrostatic maps in turn.

5. Hydrophobic map Analyze the hydrophobic map. Isolate the hydrophobic map. The hydrophobic map gives the locations of receptor contact and of low preference for acceptors and donors. Here, the rendering mode has been set to Solid and the color to green. Hide the other two maps by setting the rendering mode of Acc and Don to None. There are four -3 kcal/mol hydrophobic regions. The 1HPV receptor comprises two chains identical in sequence, and, accordingly, the hydrophobic areas appear as a mirrored pair, with one smaller and one larger hydrophobic preference region associated with each chain. These regions are suggestive of locations for hydrophobic and/or aromatic pharmacophore features.

6. Acceptor map Analyze the hydrogen acceptor map. Isolate the hydrogen acceptor map by setting the Hyd and Don rendering modes to None. Here, the rendering mode of Acc has been set to Solid.

There is a -2 kcal/mol hydrogen acceptor region contacting the bound water HOH_201. This suggests adding a projected feature to the pharmacophore to contact this area. 7. Analyze the hydrogen donor map.

Donor map Isolate the hydrogen donor map by setting the Hyd and Acc rendering modes to None. Here, the rendering mode of Don has been set to Solid. There is a -2 kcal/mol hydrogen donor region at ASP25 contacting the hydroxyl group on the ligand. This suggests a possible donor pharmacophore feature. Refining the Query Having analyzed the ligand-pocket interactions, we can now refine the 1HPV-query1 pharmacophore query by adding the pharmacophore features suggested by the analysis. If it is still open, close the Ligand Interactions panel as it is no longer needed.

1. Hydrophobic map and query

Open the query. Open the simple pharmacophore query created earlier: MOE | File | Open | 1HPV-query1.ph4. The Pharmacophore Query Editor will open with the 1HPV-query1.ph4 query loaded. At the same time, the query will be rendered in the MOE Window. 2. Tidy up the rendering area. For better visualization: o Hide all electrostatic maps by setting their rendering mode to None in the Surfaces and Maps panel. o Hide the receptor atoms with MOE | Popup | Hide | Receptor.

3. Acceptor map with query Add features suggested by the hydrophobic map. Show the hydrophobic map (in isolation) by setting the Hyd rendering mode to Solid. The hydrophobic features already in the query can be seen to coincide with the regions of preferred hydrophobic contact. Thus, no additional features are needed to capture the interactions depicted by the hydrophobic map. 4. Add features suggested by the hydrogen acceptor map. Hide the hydrophobic map (set its rendering mode to None in the Surfaces and Maps panel). In the Pharmacophore Query Editor, press the Scheme: Info button to open the information panel for the current scheme (Unified). In the Unified Info panel, turn on

Acc2 to enable the projected acceptor annotation points (the pharmacophore type is Acc2) and then close the Info panel. Turn on Show Projecting Vectors in the Pharmacophore Query Editor. In the MOE Window, projected Acc2 acceptor annotation points will be displayed. It can be seen that the projections from the C=O and SO2 groups coincide with the bound water. Select these two annotation points, then press Create: Feature. An Acc2 projected feature will be created at the centroid of the selected annotation points.

5. Donor map with query Add features suggested by the hydrogen donor map. In the Unified Info panel, turn off Acc2. In the Pharmacophore Query Editor, change the Ligand Annotation render mode to Long. In the MOE Window, an annotation point can be seen on the hydroxyl group. Select this annotation point (it is easiest to do this by left-dragging the mouse), then press Create: Feature to create a Don&Acc feature on the hydroxyl group.

6. Excluded volumes Add excluded volumes on the receptor atoms of the binding site to avoid steric clashes within the pocket. First ensure no atoms are selected using MOE | Popup | Select | Clear. Then, select the binding pocket atoms with MOE | Popup | Select | Pocket. In the Pharmacophore Query Editor, press Create: Union. This will create a grouped volume feature made up of volumes placed on all selected atoms. By default, the volume is already of type Excluded. Increase the radius R of the volume feature to 2.0. As explained above in the section on constraints, atoms are considered to have zero radii when evaluating them against volume constraints. To avoid clashes, the volume radius should be increased to take into account the van der Waals radii of the ligand atoms of interest. The value of 2.0 accounts for the atom radii, with some tolerance. For easier visualization, you can turn off the display of the volumes in the MOE Window by selecting V1 in the feature list of the Pharmacophore Query Editor and then turning on the Hidden checkbox. When the volume feature is selected in the list and the Hidden attribute is on, the centers of the volumes are marked with selection markers and a dotted contour line drawn around the volumes showing the volume's location. Note that the Hidden attribute only affects rendering in the MOE Window. It has no effect on searching: hidden features participate in pharmacophore searching as normal. 7. Save the refined query. Press Save in the Pharmacophore Query Editor. In the Write Query File panel, enter the name 1HPV-query2.ph4 and press OK.

Validating the Query Once a pharmacophore query has been refined, it can be evaluated by performing a pharmacophore search. If the results of this search are found to be unsatisfactory, another round of query refinement can be done. In this way, the pharmacophore query can be iteratively improved.

Example: 1HPV-query2 Query Validation Close the current system with MOE | File | Close. Open the database of HIV-1-PR inhibitors: MOE | File | Open | $MOE/sample/mol/hiv1pr_ligands.mdb.

1. Open the Pharmacophore Search panel. DBV | File | Pharmacophore Search 2. Specify the pharmacophore query. Press Query: Browse. In the Open File panel, locate and select 1HPV-query2.ph4. Press OK.

3. Specify how the search output will be reported. In the Pharmacophore Search panel, change the Hit Entries option to Select, which will select the results in the Database Viewer. Set the Output database to ph4out2.mdb. 4. Perform the search. Press Search. The status line at the bottom of the panel reports the search status. The hits will be selected in the Database Viewer. Some observations can be made on closer examination of the search results: 

The search results in 24 hits out of 82 molecules. Compared to the original query 1HPVquery1.ph4, the refined query is more selective. If the output database is examined as described above in the example on pharmacophore search, it can be seen that the hits exhibit mostly the same orientation, a desirable result.

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Experiments using partial matches can be made. However, since the presence of the projected acceptor feature Acc2 prevents hits that displace the bound water, the Acc2 feature should probably be made essential when partial matches are enabled. To make a feature essential, turn on its F:Essential attribute in the Pharmacophore Query Editor.

The iterative process of pharmacophore query refinement is facilitated by the intimate connection between the Pharmacophore Search and the Pharmacophore Query Editor panels. Pressing Edit in the Pharmacophore Search panel opens the Pharmacophore Query Editor. Subsequently, all searches will use the query currently loaded in the editor. Thus any changes made in the editor take effect immediately in the next search. Similarly, pressing Search in the Pharmacophore Query Editor opens a Pharmacophore Search window linked to the editor in the same way.

Automatic Pharmacophore Query Generation Pharmacophore queries can be manually generated, edited, and customized using the Pharmacophore Query Editor. In addition, there are various tools in MOE for generating pharmacophore queries automatically: 

Pharmacophore Consensus. Obtain consensus features from aligned ligands. The Pharmacophore Consensus panel is opened from the Pharmacophore Query Editor. It calculates consensus features from a set of aligned molecules loaded in MOE or found in a database. Ligand alignment can be achieved using MOE's Flexible Alignment application. The suggested features can be loaded back into the Pharmacophore Query Editor.

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Pharmacophore Elucidation. Automatically determine a pharmacophore query from active (and optionally inactive) ligands.

The pharmacophore query is elucidated without knowledge of the receptor. It is assumed, however, that the ligands bind to the receptor in the same mode, i.e. they overlay well, and that the collection of ligands is sufficiently diverse so that important pharmacophoric features will emerge. The elucidator operates on 3D conformations; these can be generated automatically by the application if not provided. 

PLIF. Generate a pharmacophore query from a consensus of Protein Ligand Interaction Fingerprints. The PLIF application uses fingerprints to encode interactions such as hydrogen bonds, ionic interactions, and surface contacts between ligands and proteins. The application operates either on a database of complexed ligand-proteins (e.g. a collection of crystal structures) or on a receptor loaded in MOE and a database of ligands in their bound conformations (e.g. the results of rigid-receptor docking). Since the fingerprints encode structural information, a set of consensus pharmacophore features can be derived from a consensus of interaction fingerprints. In order to be meaningful, the ligand binding modes should be similar, i.e. the bits in the interaction fingerprint which are important to activity should be highly conserved across all fingerprints being used to generate the pharmacophore features.

Running in MOE/batch Pharmacophore query generation in general requires user interaction and is not well-suited for batch mode. Pharmacophore search, on the other hand, can be done in MOE/batch using an SVL script or runnable file that invokes the SVL function ph4_Search. In the following, we show how to use a script to run pharmacophore search as a batch job. An SVL script is a sequence of SVL expressions stored in a plain text file. The expressions are executed as though they had been entered at the SVL CLI (Command Line Interface), except that in a script each expression must be terminated by a semi-colon (;). Here is an example of a one-line script for performing a pharmacophore search on the database dbfile.mdb using the pharmacophore query file query.ph4: ph4_Search ['dbfile.mdb', 'query.ph4'];

The above call assumes that dbfile.mdb contains a pre-calculated molecule index field called mseq (molecule sequence number). Here is an example of a more complicated invocation, using some of the possible ph4_Search options. These lines would be the exact contents of the script file: ph4_Search [ 'dbfile.mdb', 'query.ph4', [ molfield: 'ligand2',

// input molecule field name

maxmolhits: 5000, use_mseqfield: 0, out_dbfile:'srch_out.mdb', out_append: 0

// // // //

max # hits to generate per entry don't use the mseq field output database file name overwrite output database file

]

];

Supposing one of the above calling sequences is saved to the file 'ph4search.svl'. The search can then be executed in MOE/batch using the following in a command shell (Unix) or in a command prompt window (Windows): % moebatch -script ph4search.svl

This command invokes moebatch to run the script and then terminate once the script has finished executing. Using print and write statements in the script file allows additional information to be reported to the shell: ph4_Search ['dbfile.mdb', 'query.ph4']; write ['\nDone. {}\n\n', asctime []];

will, on completion of the search, print a message similar to this: Done. Thu Dec 18 12:45:30 2008

References [Ehrlich 1909] Ehrlich, P.; Ber. Dtsch. Chem. Ges. 42 (1909) 17–47. [IUPAC 1998] Wermuth, C.G., Ganellin, C.R., Lindberg, P., Mitscher, L.A.; Glossary of Terms Used in Medicinal Chemistry (IUPAC Recommendations 1998); Pure & Appl. Chem. 70:5 (1998) 1129–1143.

Pharmacophore Reference 

Overview of Pharmacophore Applications.

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Pharmacophore Annotation Schemes. Automatically identify ligand annotation points.

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Pharmacophore Query Editor. Create and edit pharmacophore queries.

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Pharmacophore Elucidator. Automatically generate pharmacophore queries from collections of compounds.

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Pharmacophore Search. Screen virtual databases of 3D conformations.

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Pharmacophore Tutorial. Use MOE pharmacophore applications to study serotonin 5HT3 receptors.

SVL Commands ph4_Search

See Also PLIF Flexible Alignment Site Finder Surfaces and Maps Dock Pharmacophore-Type Molecular Fingerprints Pharmacophore Feature Descriptors