static MM_Index snap(mm_index corect, MM_Index ligct, int imatch0, int *moleatoms, i

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1 GLIDE static MM_Index snap(mm_index corect, MM_Index ligct, int imatch0, int *moleatoms, int *refcoreatoms){int ncoreat = :vector<phpcoremapping> mappings; PhpCoreMapping mapping; for COMMON(glidelig). ncoreatoms;int offset = imatch0 * ncoreat; std::vector<phpcoremapping> mappings; PhpCoreMapping mapping;for(int static MM_Index snap(mm_index corect, MM_Index ligct, int imatch0, int *moleatoms, i hpcoremapping mapping; static MM_Index snap(mm_index corect, MM_Index ligct, int (int i = std: static MM_Index snap(mm_index nt *refcoreatoms){int ncoreat = COMMON(glidelig). nc Structure-Based Lead Optimization with Glide MM-GBSA corect,mm_index imatch0, int *moleatoms, int *refcoreatoms){int ncoreatoms;int offset = imatch0 * ncoreat; imatch0, int *moleatoms, int *refcoreatoms){int ncoreat = COMMON(glidelig). oreatoms;int offset = imatch0 * ncoreat; std::vector<phpcoremapping> mappings; P

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3 Structure-Based Lead Optimization with Glide & MM-GBSA Created with: Release Prerequisites: Files Required: Categories: Keywords: Release or higher Introduction to Structure Preparation and Visualization Structure-Based Virtual Screening Using Glide FXa_leadoptimization17-1.prjzip, 2boh_grid.zip Lead Optimization, Structure-Based Design, Data Analysis and Communication scoring functions, core constraint, virtual screening This tutorial demonstrates the use of various scoring functions and core constraints for lead optimization. You will learn how to optimize binding poses and rank order a congeneric series of FXa inhibitors. Words found in the Glossary of Terms are shown like this: Workspace File names are shown with the extension like this: 1fjs.pdb Items that you click or type are shown like this: File > Import Structures This tutorial is written using a 3-button mouse with a scroll wheel. The tutorial consists of the following sections: 1. Virtual Screening Prerequisites - p Creating Projects and Importing Structures - p Scoring using Standard and Extra Precision - p Optimizing using Core Constraints and MM-GBSA - p Conclusions and References - p Glossary of Terms - p. 10

4 1. Virtual Screening Prerequisites Structure files obtained from the PDB, vendors, and other sources often lack necessary information for performing modeling-related tasks. Typically, these files are missing hydrogens, partial charges, side chains, and/or whole loop regions. In order to make these structures suitable for modeling tasks, we use the Protein Preparation Wizard to resolve issues. Similarly, ligand files can be sourced from numerous places, such as vendors or databases, often in the form of 1D or 2D structures with unstandardized chemistry. LigPrep can convert ligand files to 3D structures, with the chemistry properly standardized and extrapolated, ready for use in virtual screening. In this tutorial, the protein and ligands have already been prepared in order to save time. However, these preparation steps are a necessary part of a virtual screen and must be done before docking. Please see the Introduction to Structure Preparation and Visualization tutorial for instructions on using the Protein Preparation Wizard and LigPrep. 2. Creating Projects and Importing Structures At the start of the session, change the file path to your chosen Working Directory in Maestro to make file navigation easier. Each session in Maestro begins with a default Scratch Project, which is not saved. A Maestro project stores all your data and has a.prj extension. A project may contain numerous entries corresponding to imported structures, as well as the output of modeling-related tasks. Once a project is created, the project is automatically saved each time a change is made. Structures can be imported from the PDB directly, or from your Working Directory using File > Import Structures, and are added to the Entry List and Project Table. The Entry List is located to the left of the Workspace. The Project Table can be accessed by Ctrl+T (Cmd+T) or Window > Project Table if you would like to see an expanded view of your project data. 2.1 Import pre-installed files into the Working Directory 1. Double-click the Maestro icon (No icon? See Starting Maestro ) 2. Go to File > Change Working Directory, find your directory, and click Choose Note : If using the Maestro release skip to step 5 Figure 2-1. Change Working Directory option. 3. In a browser, go to to download the required tutorial files 4. Unzip and copy the downloaded tutorial files to your Working Directory 1

5 Note : If using a release older than skip steps 5-9 Figure 2-2. Tutorials panel, showing filtered results with MM-GBSA as a keyword. 5. Go to Help > Tutorials 6. Next to Filter, type MM-GBSA 7. Choose Structure-Based Lead Optimization with Glide and MM-GBSA 8. Click Copy All the tutorial files are copied into your Working Directory 9. Click Close 2.2 Save Project in the Working Directory Figure 2-3. Save Project panel. 1. Go to File > Open Project 2. Select FXa_leadoptimization_17-1.prjzip and choose Open 3. In Save Scratch Project, click OK 4. Go to File > Save Project As 5. Change the File name to FXa_leadoptimization, click Save The project is now named FXa_leadoptimization.prj Structures are shown in the Entry List Note: Please see the Glossary of Terms for the distinction between included and selected 3. Scoring Using Standard and Extra Precision In this tutorial, we will compare virtual screening results using the Glide SP (standard precision) and XP (extra precision) scoring function. The XP scoring function contains more stringent terms for modeling desolvation, hydrophobic effects, and charged interactions. This causes it to be a longer calculation than when using SP, so pre-run results are contained in the tutorial files if needed for the sake of time. We will visualize the results and create scatter plots comparing measured binding affinity to docking scores. 2

6 3.1 Screen a lead series using default Glide parameters 1. Select the group leadseries_prepped 2. Go to Tasks > Browse > Glide > Ligand Docking Note: This can be added to your Favorites Bar by clicking on the star to the left of Ligand Docking 3. Next to Receptor grid, click Browse 4. Choose 2boh_grid.zip, and click Open 5. For Use ligands from, choose Project Table (selected entries) Figure 3-1. Input receptor grid and ligands into the Ligand Docking panel. 6. Click the Output tab 7. For Write out at most, increase the number to 3 poses per ligand 8. Change Job name to FXa_SP 9. Click Run This job takes ~15 minutes on 4 processors To save time, we will look at pregenerated results Figure 3-2. Output tab of the Ligand Docking panel. 3

7 3.2 Select and examine top poses from the SP results 1. Select the group prerun-fxa_sp_pv 2. Go to Tasks > Browse > Receptor Based Virtual Screening > Select Top Poses Select Top Poses panel opens Figure 3-3. Select Top Poses option in Receptor-Based Virtual Screening. Figure 3-4. Select Top Poses panel. 3. For Use structures from, choose Project Table (241 selected entries) 4. For Keep at most, enter 1 structures per compound 5. Change Job name to SP_topposes 6. Click on the cog icon The Job Settings panel opens 7. For Incorporate, choose Append new entries 8. Click Run A new group called SP_topposes-poses is added to the Entry List 9. In the group FXa_2boh_leadseries, use shift-click to include the 2boh_complex and 2boh_ligand 10.Click Presets 11.In the Workspace Configuration Toolbar, click the Labels button Labels are removed from the Workspace 4

8 Figure 3-5. The ligand is visualized in the receptor, with the labels toggled off. 12.Right-click the group SP_topposes and choose Include 13.Click Continue in the Warning window The cores of the structures are not aligned well in the binding site Figure 3-6. Poor alignment of ligand cores with default Glide SP docking. Rendering is done by clicking Presets and turning off Labels. 3.3 Plot SP docking scores versus binding affinity Figure 3-7. The Plot option in the Project Table toolbar. 1. Type Ctrl+T ( Cmd-T ) to open the Project Table 2. Select the group SP_topposes 3. In the Project Table toolbar, click Plot 4. Choose New Scatter Plot A new plot opens 5. Set the X-axis to deltag_bind and the Y-axis to glide gscore 6. Check Best fit line The squared Pearson Coefficient value is 0.10, which indicates poor correlation between the measured binding affinity and docking score Figure 3-8. Plot of measured binding affinity vs. SP docking score, showing poor correlation. Note : Plots can be given names in the Manage Plots panel by clicking Rename 5

9 3.4 Repeat docking with XP scoring Figure 3-9. The Ligand Docking panel with XP chosen as the scoring function. Figure Select Top Poses panel evaluating XP results. 1. Select the group leadseries_prepped 2. Go to Tasks > Browse > Glide > Ligand Docking 2boh_grid.zip is still chosen as the Receptor grid In the Ligands tab, Use ligands from is still set to Project Table (selected entries) In the Output tab, Write out at most is still 3 poses per ligand 3. Click the Settings tab 4. Change Precision to XP (extra precision) 5. Change Job name to FXa_XP and click Run This job takes ~3.5 hours on 4 processors To save time, we will look at pregenerated results 6. Select the group prerun-fxa_xp_pv 7. Go to Tasks > Browse > Receptor Based Virtual Screening > Select Top Poses The Select Top Poses panel opens Use structures from and Keep at most settings from the previous calculation are still chosen 8. Change Job name to XP_topposes 9. Click Run A new group called XP_topposes-poses is added to the Entry List 6

10 10.In the group FXa_2boh_leadseries, use shift-click to include the 2boh_complex and 2boh_ligand 11.Right-click the group XP_topposes and choose Include 12.Click Continue in the Warning window The cores of the structures are more clustered in the binding site than with SP, but still not aligned 13.Click Presets 14.In the Workspace Configuration Toolbar, turn off Labels Figure Poses from XP docking. 3.5 Plot XP Docking Scores Versus Binding Affinity Figure Plot of measured binding affinity vs. XP docking score, showing improved correlation over using SP scores. 1. Click on the group XP_topposes to select all the entries 2. In the Project Table toolbar, click Plot 3. Choose New Scatter Plot A new plot opens 4. Set the X-axis to deltag_bind and the Y-axis to XP Gscore 5. Check Best fit line The squared Pearson Coefficient value is 0.31, which has improved correlation over SP scoring, but is not optimal 4. Optimizing using Core Constraints and MM-GBSA When screening a congeneric series of molecules, using core constraints forces the core of the molecules to adopt the same conformation as a reference structure. Conformations of R-groups are then sampled and optimized, increasing the likelihood of identifying the correct binding pose. The molecular mechanics with generalized Born and surface area (MM-GBSA) scoring function has been optimized to predict binding free energies for a congeneric series of molecules. Like the XP scoring function, these calculations take a longer time. If time is short, pre-generated results can be found in the Project Table. 7

11 4.1 Set a core constraint Figure 4-1. The Ligand Docking panel with a core constraint from a SMARTS pattern. Figure 4-2. The core of 2boh_ligand is used to generate the SMARTS pattern. 1. Select the group leadseries_prepped 2. In the entry group FXa_2boh_leadseries, include the 2boh_complex 3. Go to Tasks > Browse > Glide > Ligand Docking 2boh_grid.zip is still chosen as the Receptor grid Settings in the Ligand, Settings, and Output tabs from previous calculation are kept 4. Click the Core tab 5. Check Use core pattern comparison The ligand is highlighted in the Workspace in pink 6. Check Pick core-containing molecule 7. Select SMARTS pattern 8. In the Workspace, shift-click on the heavy atoms of the indole -amide-1-isopropyl piperdine portion of the ligand Atoms are highlighted in blue 9. Click Get from Selection A SMARTS string for is generated The atoms are now highlighted in green as in Figure Change Job name to FXa_XP_core 11.Click Run This job takes ~2 hours on 4 processors To save time, we will look at pregenerated results 8

12 Figure 4-3. Select Top Poses panel evaluating XP with core constraint results. 12.Select the group prerun-fxa_xp_core_pv 13.Go to Tasks > Browse > Receptor Based Virtual Screening > Select Top Poses Select Top Poses panel opens Use structures from and Keep at most settings from the previous calculation are still chosen 14.Change Job name to XP_topposes_core 15.Click Run A new group called XP_topposes_core-poses is added to the Entry List 16.In the group FXa_2boh_leadseries, use shift-click to include the 2boh_complex and 2boh_ligand 17.Right-click the group XP_topposes_core and choose Include 18.Click Continue in the Warning window The cores of the structures are more clustered in the binding site than with SP, but still not aligned 19.Click Presets Figure 4-4. Good alignment of ligand cores with XP docking using a core constraint. 4.2 Plot core constraint XP docking scores versus binding affinity Figure 4-5. Plot of measured binding affinity vs. Glide XP docking score with core constraints, showing good correlation 1. Click on the group XP_topposes_core to select all the entries 2. In the Project Table toolbar, click Plot 3. Choose New Scatter Plot A new plot opens 4. Set the X-axis to deltag_bind and the Y-axis to XP Gscore 5. Check Best fit line The squared Pearson Coefficient value is 0.75, indicating good correlation between docking score and binding affinity 9

13 4.3 Rescore XP poses using MM-GBSA Figure 4-6. The Prime MM-GBSA panel. 1. Expand the group FXa_2boh_leadseries and include 2boh_receptor in the Workspace 2. Click on the group header XP_topposes_core to select all the entries 3. Go to Tasks > Browse > Lead Optimization > MM-GBSA The Prime MM-GBSA panel opens 4. In the Prime MM-GBSA panel, select Take complexes from separated ligand and protein structures 5. Change Job name to prime_mmgbsa_xp_core 6. Click Run This job takes ~2 hours on 4 processors To save time, we will look at pregenerated results 4.4 Plot predicted vs. actual binding affinities Figure 4-7. Plot of measured binding affinity vs. predicted MM-GBSA binding affinity, showing strong correlation. 1. In the Project Table, click on the group prerun-prime_mmgbsa_xp_core-out to select all the entries 2. Click Plot and choose New Scatter Plot 3. Set the X-axis to deltag_bind and the Y-axis to MMGBSA dg Bind 4. Check Best fit line The squared Pearson Coefficient value is 0.89, indicating strong correlation between measured and predicted binding affinity 10

14 5. Conclusion and References In this tutorial, we demonstrated a workflow for lead optimization that began with Glide SP docking, then rescored with the XP scoring function, added in core constraints, and finally used Prime MM-GBSA. This showed how a docking experiment with a congeneric series of ligands could be optimized to produce binding modes that agree with the co-crystallized structure and rank order ligands. We saved multiple poses per ligand during the docking stages, and then visualized the top scoring poses in the Workspace. Finally, we observed that the MM-GBSA scoring function could be used to produce the best correlation between measured and experimental activities. For further information, please see: Introduction to Structure Preparation and Visualization Structure-Based Virtual Screening Using Glide Glide User Manual Prime User Manual 6. Glossary of Terms congeneric - a series of ligands that are structurally related, such as sharing a common core constraints - information added to the docking experiment that require a particular interaction or atom proximity to be satisfied Entry List - a simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion included - the entry is represented in the Workspace, the circle in the In column is blue incorporated - once a job is finished, output files from the working directory are added to the project and shown in the Entry List and Project Table Project Table - displays the contents of a project and is also an interface for performing operations on selected entries, viewing properties, and organizing structures and data Scratch Project - a temporary project in which work is not saved, closing a scratch project removes all current work and begins a new scratch project selected - (1) the atoms are chosen in the Workspace. These atoms are referred to as "the selection" or "the atom selection". Workspace operations are performed on the selected atoms. (2) The entry is chosen in the Entry List (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries Working Directory - the location that files are saved Workspace - the 3D display area in the center of the main window, where molecular structures are displayed 11