Method Article

Structural Analyses of An Epidermal Growth Factor Receptor-Specific Single-Chain Fragment Variable via An In Silico Approach

DOI:

10.3791/65894

⸱

November 10th, 2023

 , 
1College of Arts and Sciences, Qatar University, 2Amity University

In This Article

Summary

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This antibody homology modeling prediction protocol is followed by antibody-receptor Pyrx docking and molecular dynamic simulation. These three primary methods are used to visualize the accurate antibody-receptor binding areas and the binding stability of the final structure.

Abstract

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Single-chain fragment variable (scFv) antibodies were previously constructed of variable light and heavy chains joined by a (Gly4-Ser) 3 linker. The linker was created using molecular modeling software as a loop structure. Here, we introduce a protocol forin silico analysis of a complete scFv antibody that interacts with the epidermal growth factor receptor (EGFR). The homology modeling, with Pyrx of protein-protein docking and molecular dynamic simulation of the interacting scFv antibody and EGFR First, the authors used a protein structure modeling program and Python for homology modeling, and the antibody scFv structure was modeled for homology. The investigators downloaded Pyrx software as a platform in the docking study. The Molecular dynamic simulation was run using modeling software. Results show that when the MD simulation was subjected to energy minimization, the protein model had the lowest binding energy (-5.4 kcal/M). In addition, the MD simulation in this study showed that the docked EGFR-scFv antibody was stable for 20-75 ns when the movement of the structure increased sharply to 7.2 Å. In conclusion, in silicoanalysiswas performed, and the molecular docking and molecular dynamics simulations of the scFv antibody proved the effectiveness of the designed immune-therapeutic drug scFv as a specific drug therapy for EGFR.

Introduction

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Conformational changes in the protein (ligand and receptor) always occur based on structure-based functions. The study of the possible binding grooves of the protein and prediction of the stable binding interaction is an advanced method to prepare drugs for better use in the human body. Homology modeling followed by docking and molecular dynamic simulation is a straightforward method for accurate prediction of stable interactions of binding between the residues of receptors and constructed antibodies that are used as specific personalized medicine1,2. The predicted model structure can show conformational changes and rearrangements in ligand-receptor binding sites, particularly at the antibody-receptor interface. There are many reasons for these changes, such as the rotation of side chains, global structural transformation, or more complex modifications. The main reason for homology modeling is to distinguish a protein's tertiary structure from its primary structure2,3.

A tyrosine kinase receptor called epidermal growth factor receptor (EGFR) plays many biological roles in cancer cells, including apoptosis4,5, differentiation6,7, cell cycle progression8,9, development9,10, and transcription11. EGFR is one of the well-known therapeutic targets for breast cancer12. The overexpression of regular kinase activity such as EGFR usually leads to cancer cell progression, which can be repressed by many kinds of cancer inhibitors13. The epidermal growth factor receptor (EGFR) was used as a receptor for the single chain fragment variable specifically constructed to work against this receptor. Its predicted structure was used to test the antibody binding activity.

In this paper, the scFv antibody structure was modeled using modeling software with Python script and the homology modeling method14,15. A homology model can be built from the protein and amino acid sequences of receptors and ligands16,17. Additionally, advanced bioinformatics technologies such as molecular docking were employed to predict how small molecule ligands will bind to the correct target binding site. The docking would balance the development of novel drugs directed toward multiple diseases. The binding behavior is taken into consideration5,18.

Furthermore, molecular docking is a critical technique to facilitate and speed up ligand-receptor binding development. Molecular docking enables scientists to virtually screen a library of ligands against a target protein and predict the binding conformations and affinities of the ligands to the target receptor protein. Molecular dynamic simulation (MNS) demonstrates how the residues move in space, simulates the antibody motions toward their receptors, and finally informs antibody design efforts. This study is a novel prediction of grid box dimensions that decided how the scFv antibody binds to EGFR and the detection of the energy and time of that binding in MDsimulation.

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Protocol

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1. Secondary structure predictions of a single chain fragment variable (scFv) protein

  1. Build the single-chain fragment variable (scFv) protein's 3D structure with BLAST protein data bank (PDB), KABAT numbering, and the modeling software. The scFv consists of a linker (Gly4-Ser) that connects a variable heavy chain (VH) and a variable light chain (VL).
  2. Use the molecular modeling software to build the linker as a loop structure, and perform all these methods as described in previous studies2,19,20.

2. Template selection and scFv and EGFR 3D structure prediction and homology modeling

  1. Choose template 1ivo for EGFR structures (based on its high resolution). Download the 1ivo.pdb file from the pdb website, as shown in Figure 1B.
  2. Prepare the input 1ivo.pdb file as described below.
    1. In the 1ivo.pdb file, remove all external ligands by opening the pdb.org website and selecting the 1ivo. Structure, and looking for the name of the ligands under the small molecule title on the 1ivo structure page of the pdb website.
    2. Find the ligand name NAG. Open the 1ivo.pdb file downloaded from the pdb website and find the termination residue (TER.).
    3. Delete the residues of the external ligands in the 1ivo structure, starting from the residue after TER. and before the residue ends. Save the 1ivo.pdb file on the system.
  3. Prepare the saved 1ivo.pdb file as described below.
    1. Download the Autodock docking software (autodock.scripps.edu) from the window selection area. Click on the Open 1ivo.pdb File.
    2. Use the Edit command to choose Add Hydrogen > Add, then select Polar Only, and then press Ok.
    3. Use the Edit command to add Kollman charges (Supplementary Figure 1). Use the Edit command to delete water. Save the 1ivo.pdb file on the pc.
  4. Minimize the energy of the 1ivo.pdb structure as described below.
    1. Download SPDBV. software from http://spdbv.vital-it.ch/disclaim.html. Open the 1ivo.pdb file.
    2. Select all. Select the command Perf > Energy minimization > Ok (Supplementary Figure 2). Save the 1ivo file on the pc.
  5. Prepare the full model scFv using homology modeling as described below.
    1. Download the modeling software17 and the Python script 3.7.9 shell from the Window- 64. Keep the downloaded software files in the D drive.
  6. Prepare the input files as described below.
    1. Load the scFv Pdb file in fasta format from the NCBI website and rename the file TARGET.ali. as described in Supplementary Coding File 1. Choose the template using the Blast section in NCBI, paste the sequenced file, select in pdb format 7det.pdb as described in Supplementary Coding File 2, and then submit. Then, use the pdb.org website to obtain the template file.
    2. Prepare the third input file as align2d.py (Python) as described in Supplementary Coding File 3, which opens as shown in Supplementary Figure 3A. Press the Show More Option, then go to Edit with IDLE > Edit with EDLE (64-bit). Run using the run module 5 command in the align2d.py to obtain two output files: Tar- 7det.ali and Tar- 7det.pap.
  7. Complete the previous three steps to use the command in the last input file.
  8. Add a new input file model-single.py (command python file) as shown in Supplementary Coding File 4 and as described below.
    1. Press the Show More Option, then go to Edit with IDLE > Edit with EDLE3.7 (64-bit). Run using the (run module 5) command as shown in Supplementary Figure 3B.
      ​NOTE: The resulting output files are the six files of the homology models shown in Supplementary Figure 3C.

3. Receptor secondary structure prediction and evaluation

  1. Detect the homology models' correction and accuracy as described below.
    1. Create the Ramachandran plot for the scFv models and EGFR models by downloading the visualization tool from  https://discover.3ds.com/discovery-studio-visualizer-download.
    2. Open the file, then right-click with the mouse and select the display sequence (Supplementary Figure 4). Copy the sequence and paste it into the Pictorial database of 3D structures (pdbsum) www.ebi.ac.uk/thornton-srv/databases/pdbsum/.
    3. Select search by sequence, paste the copy of the sequence, and then submit it. Create the plot as shown in Figure 1B,D.

4. Protein-protein docking

  1. Download the virtual screening tool software.
  2. Go to File> Read Molecules > Load 1ivo.pdb. Right-click on the protein in the autodock panel to make a macromolecule. Right-click again to make a ligand (Supplementary Figure 5).
  3. Click in the autodock panel to select protein and then select ligand.
  4. Open the protein list. Then, from the list, select scFv Protein.
  5. Go to Toggle Selection Spheres. Adjust the grid box to the center of the receptor. Click Forward when the round pink button appears.
  6. To prepare the pdbqt files for both scFv-antibody and EGFR (1ivo) structures, use the following steps.
    1. Go to C Drive > Program Files (86) > Users, then choose the pyrx file that contains macromolecules and protein output files that were saved as a pdbqt file.
    2. Then, save the single-chain fragment variable (scFv) antibody pdbqt file.
  7. Download PyMOL software at PyMOL | pymol.org. Use PyMOL software to show the scFv antibody-receptor EGFR configurations.
    1. Go to file and open C:\Users\ilham\.mgltools\PyRx\Macromolecules\protein. Prepare the docking configurations of the scFvantibodyinteracting with the receptor in Figure 2A as described below.
      1. Use the display option to show the (1ivo)-receptor file as sequence residues with a white background shown in Supplementary Figure 6.
      2. Display the docking configuration file with higher resolution to see the ligand color in green and red residue colors. Display the (1ivo)-receptor rigid surface in yellow.
  8. Prepare the docking configurations of the scFvantibodyinteracting with the receptor in Figure 2B, as described below.
    1. Download the docking software from the window selection area. Use Autodock to show the scFv antibody-receptor EGFR configurations and conformations.
      1. In Autodock, choose the Analyze Option, then open the Autodock Vina result. Go to File and open C:\Users\ilham\.mgltools\PyRx\Macromolecules\protein.
      2. Select the protein receptor pdb file, then select the area of the ligand configuration (scFv antibody structure). Connect the rigid surface of the receptor with the configuration ofthe docked structure and hide the rest of the receptor. Hide the far residues of the receptor from the connected residues to the ligand, as shown in Figure 2B.
    2. The protein-protein complex was then considered ready to perform MD simulation.

5. Molecular dynamic simulation (MD simulation) of the EGFR-scFv antibody docking complex

  1. Download the MD simulation software and use it as follows.
    1. Prepare the EGFR (1ivo) pdb file using the reparation wizard as in Supplementary Figure 7A. Operate the preprocess section to refine the file. Send the refined file to be set in the system builder.
    2. Load the molecular dynamics simulation software from the working directory. Add the ions and upgrade the refined file to reach 20 Å to submit the job (Table 1), also shown in Supplementary Figure 7B.
    3. Load the EGFR (1ivo) pdb from the imported file, then choose 100 ns timesteps to run it (Supplementary Figure 7C).
  2. Start the analysis of the simulation after the completion of the MDsimulation as described below.
    1. Create a job folder and save it in the cms file . Load the cms file to perform this step in the MD simulation.
    2. Create a working directory for project folders and report the energy values. Use S.I.D. pdf to report the simulation, as shown in Figure 3A, and the interaction diagram and H bond, as shown in Figure 3B.
    3. Load the pdf file of cms by browsing from the folder and use TIP3P as the model for file volume minimization.
    4. Create the solvation file to perform this step shown in Supplementary Figure 7D. Save pdf file through the software, and analyze the data, resulting in Figure 4, Figure 5, and Figure 6.
  3. Generate an MDsimulation finalization setup by creating the resolve file. Find the results in the boundary box, as shown in Figure 7.

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Results

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Using phage display technology, the scFv gene anti-EGFR was created from the mouse B-cell hybridoma line C3A820,21. The single chain fragment variable (scFv) structure models of the VH and VL structures were built separately, according to Chua et al.22. After that, the models were visible as ribbons produced using RasMol. Then, using molecular modeling software, a synthetic peptide [Gly4Ser)3 was used to join the separately modeled VH and ...

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Discussion

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EGFR is the primary target receptor of breast cancer. EGFR overexpression increases breast cancer cases around the world. Meanwhile, specific antibodies such as single chain fragment variables are antibodies that move easily via blood circulation and have a fast clearance rate in the body. Antibodies are a wise solution and an effective immunotherapy drug37. Therefore, structure-based drug design must identify inhibitory medicines, such as scFv antibodies, that work specifically against a target r...

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Disclosures

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The authors have nothing to disclose.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Autodock softwareCenter for Computational structural Biology AutoDock (scripps.edu)
Desmond Maestro 19.4 software Schrodingerwww.schrodinger.com 
Download Discovery Studio 2021  Dassault Systems https://discover.3ds.com/discovery-studio-visualizer-download.
Modeler Version 9.24[17] University of Californiahttps://salilab.org/modeller/9.24/release.html
Pictorial database of 3D structures (pdbsum)EMBL-EBI www.ebi.ac.uk/thornton-srv/databases/pdbsum/
PyMOL software SchrodingerPyMOL | pymol.org
Pyrx software Sourceforge Download PyRx - Virtual Screening Tool (sourceforge.net)
Python script 3.7.9 shell from the window (64)PythonPython Release Python 3.7.9 | Python.org
SPDBV software Expasyhttp://spdbv.vital-it.ch/disclaim.html

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Single Chain Fragment VariableEpidermal Growth Factor ReceptorIn Silico AnalysisHomology ModelingMolecular DockingMolecular Dynamics SimulationProtein Structure ModelingAntibody EGFR InteractionProtein Protein DockingBinding Energy
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