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## Project for Molecular Immunology (AIM 620):
Modeling a Humanized Version of a Mouse Antibody

## Introduction

In this project, you will make a homology model of a the variable regions of a humanized mouse antibody, using a human immunoglobulin-G (IgG) model as a template. Then you will compare your homology model to the actual structure determined by X-ray crystallography.

### What is a humanized mouse antibody?

A scientist at Flybynight Pharmaceuticals has found a mouse monoclonal antibody that triggers lasting protection against a fatal disease in mice. A similar disease occurs in humans, so the scientist would like to make a human antibody with the same function (the mouse antibody is rejected by humans). One way to begin designing a human version is to replace the complementarity-determining regions (CDRs) in a human antibody with those from the promising mouse antibody. The result is called a humanized antibody -- all parts are human except the CDRs, to minimize the chances that the human immune system will recognize the antibody as foreign. The scientist hopes that the humanized antibody will have a similar effect in humans, or at least that it can serve as a starting point for design of such an antibody.

The scientist uses modern techniques in molecular biology to produce useful quantities of this hybrid antibody. It has low levels of the desired function. The scientist now wishes to modify specific residues to improve the function. Which residues to alter? One approach is to make a model of the new antibody by homology modeling and to see which non-CDR residues are in contact with the CDRs. These residues are candidates from modifications that might improve the function of the humanized version. A plausible template for the modeling is the human antibody used as the framework for the humanized version -- if it's structure is known from crystallography or NMR spectroscopy.

Follow the instructions below to build a model of the functional regions of this humanized antibody.

## Files

Download and save the following three files into a folder named Project:

1. Target.fst: A plain-text file containing the sequence of the humanized mouse antibody in FastA format (one letter abbreviations of the amino-acid residues). You will build a three-dimensional homology model of the target using an appropriate three-dimensional as a template. If this file appears in your browser, File: Save As... , set Format to Text, and save the file.
2. Template.pdb: X-ray crystallographic model of a generic human IgG, which will serve as your template for constructing a homology model of the target. This template is derived from PDB file 8FAB.
3. Actual.pdb: X-ray crystallographic model of the target protein. You will compare your homology model of the target with this structure, which was determined experimentally. This model is taken from PDB file 1BJ1.

## Instructions

These instructions are not detailed. They presume that you have worked through sections 1-11 of the Deep View Tutorial, and are familiar with the workings of the program. Conventions for designating commands and keystrokes are the same as those in the tutorial.

1. Open Target.fst in a word-processing program, just to look at its contents. The letters beginning in the second line are abbreviations for the amino-acid residues in the target protein. The semicolon tells Deep View where to separate the residues into two chains: the light-chain-variable and heavy-chain-variable regions of the target antibody. Close the file without making any changes.
2. Start Deep View and cancel the opening dialog.
3. Swiss-Model: Load Raw Sequence to Model. Load Target.fst.
4. Color: Chain. You will see two colors, indicating two chains.
5. File: Open PDB File... Find and open Template.pdb. Press the zoom button (or <insert> on PC, or <=> on Mac) to include both proteins in the view. Color: Layer to help you distinguish them after the next operations.
6. Make sure that Target is the active layer (red in Alignment window). Fit: Magic Fit. Deep View threads the target onto the template. Display only the target.
7. Select: aa Making Clashes. Note in the Layer Infos window how many of these problem residues there are. To attempt to fix them, Tools: Fix Selected Sidechains: Quick and Dirty (other Fix options will probably take too long). Again Select: aa Making Clashes to see how many were fixed. Alternate fixing and selecting until the number of problem residues does not decrease further. Now your model is about as good as you can get with Deep View alone.
8. Select: Visible Groups. Then Save: Selected Residues Only... Name your new model 1stModel.pdb.
9. Close all files. Now open Actual.pdb. This is a model of the humanized antibody as determined by X-ray crystallography. The CDRs are colored red and the rest of the model is colored CPK.
10. Open 1stModel.pdb. Zoom to include both models.
11. Fit: Best (with Struct. Align.)... to align 1stModel onto Actual and to align their sequences in the Align window.
12. Color the CDRs in 1stModel. To make this easy, use to Align window to see the colors in Actual and to select corresponding residues in 1stModel. Then click the colr heading in the Control Panel to pick a color for them.
13. Now blink between the two models. How good is the correspondence between the CDRs in 1stModel and in Actual?
14. Make 1stModel the active layer. Color: RMS to color 1stModel according to how well it superimposes onto Actual. Note that agreement is good for the core structures, but not so good for the part we are most interested in: the CDRs.
15. To guide the scientist in picking residues to modify, find a way to color residues that are in contact with the CDRs, but that are NOT contiguous with them in sequence. Save: Layer to save your colored model as 2ndModel.pdb.

It turns out that modeling one antibody from another is very difficult. As is often the case in homology modeling, if the target and template have different functions (in this case, they bind different antigens), then the functional regions are poor templates for modeling. Even this relatively simple modeling problem confounds sophisticated modeling programs. That's why, in this exercise, I did not instruct you to send the model for optimization by Swiss-Model.

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