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

Transcription

1 BIOLUMINATE 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 = Peptide Modeling with BioLuminate std: static MM_Index snap(mm_index nt *refcoreatoms){int ncoreat = COMMON(glidelig). nc 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 Peptide Modeling with BioLuminate Created with: Release Prerequisites: Files Required: Categories: Keywords: Release or higher Introduction to Structure Preparation and Visualization peptidemodelingtutorial.prjzip, mcl1_bim_alaninescan-out.maegz Bclxl_bak_affinitymaturation-out.maegz, TIAN.csv Biologics, Structure Prediction and Analysis peptide docking, residue scanning, binding hotspots, affinity maturation, QSAR In this tutorial, we will use various tools in the BioLuminate program to perform Peptide Modelling related tasks. You will learn how to dock peptides to a protein receptor, identify possible binding hotspots, perform lead optimization, and predict binding affinity with QSAR. 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. This tutorial consists of the following sections: 1. Creating Projects and Importing Structures - p Docking Peptides to a Receptor - p Identifying Binding Hot Spots using Residue Scanning - p Optimizing Peptide Structure using Affinity Maturation - p Building Quantitative Sequence-Activity Relationships (QSAR) to Predict Binding Affinity - p Conclusions and References - p Glossary of Terms - p. 17

4 1. 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. The Toggle Table is another way to interact with structures and can be accessed via Window > Toggle Table. 1.1 Load pre-installed files into the Working Directory 1. Double-click the BioLuminate icon (No icon? See Starting BioLuminate ) 2. Go to File > Change Working Directory, find your directory and click Choose Note : If using the or release skip to step 5 3. In a browser, go to to download the required tutorial files 4. Unzip and copy the downloaded tutorial files to your Working Directory Figure 1-1. Change Working Directory option. 1

5 Figure 1-2. Tutorials panel, showing filtered results with Peptide as a keyword. Note : If using a release older than skips steps Go to Help > Tutorials 6. Next to Filter, type Peptide 7. Choose Peptide Modeling with BioLuminate 8. Click Copy All the tutorial files are copied into your Working Directory 9. Click Close 10.Go to File > Open Project and choose peptidemodeling.prjzip 11. Click Open Structures are shown in the Entry List A structure is included in the Workspace Figure 1-3. Open Project. 1.2 Save Project in the Working Directory 1. Go to File > Save Project As 2. Change the File name to peptidemodeling_new 3. Click Save The project is now named peptidemodeling_new.prj Note: Please see the Glossary of Terms for the distinction between included and selected Figure 1-4. Save Project. 2

6 2. Docking Peptides to a Receptor In this section, we will perform cognate ligand docking of a peptide that binds cyclophilin A (PDB ID 1AWR) using the SP-PEP protocol. This protocol was developed specifically for docking peptides; it performs optimally uncharged, linear peptides of less than 10 residues. To save time, structures in this tutorial have already been prepared using the Protein Preparation Wizard. This is an important step that should be done prior to performing a residue scan. Please see the Introduction to Structure Preparation and Visualization tutorial for instructions on using the Protein Preparation Wizard and LigPrep. 2.1 Dock a cognate peptide to a receptor Figure AWR_complex_prep is included and 1AWR-random is selected. 1. Include 1AWR_complex_prep in the Workspace A receptor structure is in the Workspace The entry is in the Toggle Table 2. From the Entry List, select 1AWR-random 3. Click Presets The Workspace is rerendered 4. Go to Tasks > Biologics > Peptide Docking Figure 2-2. Peptide Docking in Biologics Tasks. 3

7 5. For Define protein receptor region, check the Pick box A banner appears prompting you to pick a ligand 6. Select any atom of the ligand in the Workspace The ligand is now highlighted with a purple box and green box around it The ligand will be excluded from the grid generation Figure 2-3. The Peptide Docking Panel. Note : The purple bounding box defines the region that the docked molecule(s) can occupy to satisfy the initial stages of docking. The green box defines where the midpoint of any docked molecule must reside to satisfy the initial stages of docking. Figure 2-4. Receptor bounding boxes. Figure 2-5. Add selected sequences from the Project Table. 7. For Add Sequences from, choose Project Table The selected 1AWR-random inputted in the table 8. Change Job name to 1AWR_sp_pep 9. Click Run This job will take ~2 hours to run on a single CPU To save time, we will look at pregenerated results 10.Go to Workspace > Clear Workspace 4

8 2.2 Analyze docking results 1. Select the group 1AWR_sp_pep_pv in the Entry List 2. Right-click on the group header and select View Poses The Pose Viewer opens The first structure in the group is fixed in the Workspace, the second is included 3. Click Set Up 4. Step through the results using the right and left arrow keys Note: Turn off AUTO-fit on the main toolbar to prevent the Workspace from zooming to fit the selection when viewing ligands Figure 2-6. Pose Viewer Set Up. 5. Turn off Display contacts 6. Double-click the In circle to fix 1AWR_chainG in the Workspace 7. Compare poses to the cognate ligand using the right and left arrow keys Figure 2-7. Overlay of crystallographic ligand (green) with eighth docked pose (cyan). 5

9 3. Identifying Binding Hot Spots Using Residue Scanning In this section, we will use Residue Scanning to reproduce experimental results for a Bim-derived peptide bound to the Mcl-1 protein. To save time, structures in this tutorial have already been prepared using the Protein Preparation Wizard. This is an important step that should be done prior to performing a residue scan. Please see the Introduction to Structure Preparation and Visualization tutorial for instructions on using the Protein Preparation Wizard. 3.1 Set up a residue scanning calculation 1. Go to Workspace > Clear Workspace 2. Include 2NL9_prepped in the Workspace Note: Type Z to fit the structure to the Workspace, if needed 3. Go to Tasks > Biologics > Perform Calculations The Residue Scanning panel opens Figure 3-1. Perform Calculations option under Residue Scanning/Affinity Maturation of Biologics Tasks. 4. For Import structure from, choose Workspace 5. Click Import The table is populated 6. For Calculation type, choose Stability and Affinity 7. For Binding partners, clear the B checkbox on the Chains menu. Chain A is binding to Chain B Figure 3-2. Calculating for Stability and Affinity of Chain A binding to Chain B. 6

10 8. For Set mutations for, choose Selected residues 9. In the table, use Ctrl-click (Cmd-click) to select the following residues of chain B: 54-58, 60, 62-70, Next to ALA, click Apply Selected mutations are set to ALA Figure 3-3. Selecting residues to mutate to ALA. 11.Use Ctrl-click (Cmd-click) to select the following residues of chain B: 59, 71, and Set mutation to GLU 13.Click Apply Selected mutations are set to ALA 14.Change Job name to mcl1_bim_alaninescan 15.Click Run These calculations take ~30 seconds per mutation To save time, we will look at pregenerated results Figure 3-4. Selecting residues to mutate to GLU. 7

11 3.2 Analyze the residue scanning results 1. Go to Tasks > Biologics > View Results The Residue Scanning Viewer panel opens 2. Click Import 3. Choose mcl1_bim_alaninescan-out.maegz 4. Click Open The mutations table is populated with the predicted changes in affinity and stability 5. Click on a Residue entry Result is zoomed to that region in the Workspace Note : As these values are the relative change in free energy, negative Δ Stability values correspond to favorable mutations Figure 3-5. The Residue Scanning Viewer, showing the table loaded with results. 6. In the graph, click and drag the bottom red dotted line to a Δ Affinity of 15 Filtered results are highlighted in Mutations table Selection is highlighted in the table and Workspace 7. Select Residue B:62 8. Check Display original structure in grey The Workspace is zoomed to residue 62 The mutation is green, while the original LEU residue is grey Figure 3-6. Adjust the graph results and display original structure in grey. 8

12 Note : In this example, alanine mutations with large, positive values for Δ Affinity are interpreted as being potential binding hot spots Figure 3-7. The LEU62 (grey) mutation to alanine (green). 4. Optimizing Peptide Structure Using Affinity Maturation In this section, we will use Affinity Maturation to predict combinations of mutations for which the binding and/or stability of a peptide-protein interaction is optimized. As compared to Residue Scanning, where mutations are performed serially, Affinity Maturation employs a Monte Carlo search algorithm to explore mutations in a combinatorial fashion. 4.1 Set Up an affinity maturation calculation 1. Clear the Workspace ( Workspace > Clear Workspace ) 2. Include 1BXL_minimized in the Workspace 3. Go to Tasks > Biologics > Perform Calculations The Residue Scanning panel opens Figure 4-1. Include 1BXL_minimized in the Workspace. 9

13 Figure 4-2. Calculating for Stability and Affinity of Chain A binding to Chain B. 4. For Import structure from, choose Workspace 5. Click Import The table is populated 6. For Calculation type, choose Stability and Affinity 7. For Binding partners, clear the B checkbox on the Chains Chain A is binding to Chain B 8. For Set mutations for, choose Selected residues 9. In the table, use Ctrl-click (Cmd-click) to select the following residues of chain B: 574, 578, 581, and Click the second menu for Set mutations for (following to ), and check NonPolar (Hydrophobic) 11.Click Apply Selected mutations are set to nonpolar residues Figure 4-3. Selecting residues to mutate to NonPolar (Hydrophobic) residues. 10

14 12.Click the Options tab 13.Check Perform affinity maturation (protein design) A Combinations section opens 14.For Property to optimize, choose Affinity 15.Change Job name to bclxl_bak_affinitymaturation 16.Click Run This job will take ~4 hours to run on a single CPU To save time, we will look at pregenerated results Figure 4-4. Options tab of Residue Scanning panel. 4.2 Analyze affinity maturation results 1. Go to Tasks > Biologics > View Results 2. Click Import, and select the file bclxl_bak_affinitymaturation-out. maegz Similar to Residue Scanning, results are displayed in the table and graph Each row of data corresponds to a combination of mutations Figure 4-5. View affinity maturation results. 11

15 3. Click Create LOGO plot a. A consensus LOGO plot opens b. A.png image of the LOGO plot is saved to your Working Directory 4. Click OK Note : In this LOGO plot, it can been seen that residue 578 is the least amenable to mutation Figure 4-6. The resulting LOGO plot. 5. Building Quantitative Sequence-Activity Relationships (QSAR) to Predict Binding Affinity Peptide QSAR can be used to build mathematical models for the prediction of a property, such as affinity, based on amino acid sequence alone. Models are built by transforming a sequence into peptide descriptors, which are used as independent variables for K partial-least squares model building. To build an accurate model, it must be (a) trained on a sizable set of data for which the activity is known and (b) validated on a large Test set, i.e. sequences with known activity that were not used to train the model. In this section we will use a dataset published by Tian et al to build a model of the MHC binding affinity of antigenic peptides. 5.1 Build a peptide QSAR model 1. Go to Tasks > Biologics > Peptide QSAR The Peptide QSAR panel opens Figure 5-1. Peptide QSAR in Biologics Tasks. 12

16 2. Click Load Sequences and Observables The Peptide QSAR - Load Sequences and Observables panel opens 3. For Load sequences from, choose CSV file 4. Click Browse 5. Choose TIAN.csv and click Open 6. Click OK A dataset of 153 peptide sequences is loaded into the Peptide QSAR table Figure 5-2. Loading peptide sequences. 7. For Peptide descriptor type, choose dpps 8. Click Advanced Options for PLS Figure 5-3. Set Peptide descriptor type and open Advanced Options for PLS. 13

17 9. Set the Maximum number of PLS factors to 10 Note : The more PLS factors used to build the model, the more likely the model is to be over-fitted. As a suggestion, set the maximum number to be no more than the number of sequences divided by ten. Figure 5-4. The Advanced Options - PLS panel for Peptide QSAR. 10.Click OK and then click Build When the model is built, the Results tab opens 5.2 Analyze QSAR results Note : While the Pearson correlation coefficient (R^2) for the Training set is satisfactory, the corresponding statistic for the Test set (Q^2) is significantly worse. Figure 5-5. Results for the peptide QSAR model. 11. Next to PLS factors, click the up and down arrows The statistics don t change much with altering the number of PLS factors This suggests that a smaller number of factors could be used to build a comparably predictive model 12.Click Plot Peptide QSAR Scatter Plot panel opens showing Observed vs. Predicted activity values for the test and training sets 14

18 Note : While the statistics could be improved, a trend is observable in the data, with only a few large outliers. However, to increase the robustness of the model, more data is necessary. Try decreasing the percentage of the dataset allocated to Test to 25% and you will observe that while R^2 increases significantly, Q^2 suffers, suggesting that the model is overfit. Figure 5-6. Plot of Observed vs. Predicted activity values for the test and training sets. 13.Click the Setup tab 14. Next to Use all rows in the table, Randomly select 25% for test set 15.Click Build Figure 5-7.Change to 25% of the dataset allocated for the test set. 16.Click the Results tab Note: With a 25% test set, while R^2 has increased slightly, Q^2 is much lower. This suggests that the model is overfit. Figure 5-8. Overfit model scatter plot. 15

19 6. Conclusion and References In this tutorial, we performed peptide-docking, identified potential binding hot spots from the residue scanning results. We also used affinity maturation to predict combinations of mutations for which binding of a peptide-protein interaction is optimized. Finally, we built a peptide QSAR model to predict binding affinity. For further information, please see: Bioluminate User Manual 7. Glossary of Terms cognate ligand - a ligand that is bound to its protein target 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 16