Improving Antibody Stability/Affinity Using MM-GBSA Residue Scanning

Tutorial Created with Software Release: 2026-1
Topics: Antibody Design, Biologics Drug Discovery
Products Used: BioLuminate

Tutorial files

37.5 MB
This tutorial is written for use with a 3-button mouse with a scroll wheel.
Words found in the Glossary of Terms are shown like this: Workspacethe 3D display area in the center of the main window, where molecular structures are displayed

 

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Abstract:

 

This tutorial guides you through the steps to set up an MM-GBSA Residue Scanning calculation and analyze the results to identify mutations that enhance antibody stability and affinity.

Tutorial Content

  1. Introduction

  1. Creating Projects and Importing Structures

  1. Performing MM-GBSA Residue Scanning

  1. Conclusion and References

  1. Glossary of Terms

1. Introduction

Targeted mutations in the complementarity-determining regions (CDRs) can significantly alter binding affinity, structural stability, or developability properties. Exploring these mutations experimentally is time-consuming and expensive, making computational approaches an essential component of antibody engineering workflows. Computationally predicting the properties of protein mutants can allow you to screen larger numbers of mutations before progressing the most promising ones to experimental validation.

BioLuminate provides different methods for predicting protein stability and binding affinity changes with different levels of accuracy and computational effort. The MM-GBSA residue scanning approach has the advantage of being quite fast, allowing you to screen hundreds of mutants with reasonable effort. However, the underlying approximations (use of a static structure and an implicit solvent model) limit the reliability of MM-GBSA results. FEP+ residue scanning allows you to explore multiple residues in a single mutation and correlates much better with the experimental data than MM-GBSA residue scanning. Protein FEP+ allows you to explore one mutation per site per simulation, and is even more accurate than FEP+ residue scanning.

We recommend first using the MM-GBSA residue scanning for making qualitative predictions. The most promising mutants can then be passed on to FEP+ Residue Scanning when rapid mutational scans can be run to exhaustively explore various sites. The most promising variants from that will then move on to Protein FEP+ to confirm the prediction of the single mutation variants, after which single mutants could potentially be combined, before rank ordering all of the predicted variants and assaying the top scoring single and combinatorial variants.

This tutorial uses the 4JPV structure, which consists of a 3bnc117 human antibody in complex with HIV-1 gp120. To save time, the 4JPV structure has been prepared using the Protein Preparation Workflow and is included in the tutorial files. Please refer to the Introduction to Structure Preparation and Visualization tutorial and Best Practices for Protein Preparation before starting out with your own structure.

In this tutorial, you will learn how to set up an MM-GBSA residue scanning calculation to identify mutations that enhance antibody stability and affinity.

2. Creating Projects and Importing Structures

  1. Open BioLuminate and create a new project named antibody_residue_scanning.prj for this tutorial.
  2. Download the tutorial zip file including input files and reference outputs here: Tutorial Files
  3. After downloading the zip file, unzip the contents in your Working Directorythe location where files are saved for ease of access throughout the tutorial.

3. Performing MM-GBSA Residue Scanning

In this section, you will perform an MM-GBSA residue scanning calculation on antibody CDR residues and analyze the results. Our goal is to improve the antibody structure’s stability as compared to the unfolded antibody without adversely impacting the binding affinity between the antibody and antigen.

The properties in the MM-GBSA Residue Scanning results are frequently most helpful in assessing the potential impact of a mutation are ∆ Affinity and ∆ Stability (solvated). See the documentation for an in-depth explanation of all properties in the table and how they are calculated. These values are the relative change in free energy (in arbitrary energy units) under the MM-GBSA approximations, so negative values correspond to favorable mutations. Mutations with ∆ Stability < 0 improve the stability of the folded mutant compared to its unfolded state, whereas mutations with ∆ Affinity < 0 improve the strength of the antibody-antigen interaction.

Due to the underlying approximations, MM-GBSA Residue Scanning is helpful for predicting mutations as significantly improving binding ( ∆ Affinity < -3 energy units), neutral (-3 energy units < ∆ Affinity < +3 energy units), or reducing binding affinity significantly (∆ Affinity > +3 energy units). These scores are best used as criteria for prioritizing mutants for further investigation with FEP+ residue scanning.

As noted by Beard et al. in this paper, the changes in binding affinity calculated with MM-GBSA are not directly equivalent to those measured experimentally. MM-GBSA reports energies in an arbitrary energy unit.

3.1 Set up an MM-GBSA Residue Scanning Calculation

Figure 3-1. Importing the structure and opening the MM-GBSA Residue Scanning panel.

  1. Go to Files > Import Structures.
  2. Navigate to and choose proteinprep_4JPV-out.maegz from the tutorial files in your Working Directorythe location where files are saved.
  3. Click Open.
    • The structure is added to Entriesa simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion and includedthe entry is represented in the Workspace, the circle in the In column is blue in the Workspacethe 3D display area in the center of the main window, where molecular structures are displayed.
  4. Find and open MM-GBSA Residue Scanning Calculations from Tasks.
    • The MM-GBSA Residue Scanning panel opens.

Figure 3-2. The MM-GBSA Residue Scanning panel.

 

  1. For Import structure from, choose Workspace.
  2. Click Import.
    • The Residues table is now populated with all the residues from the antibody (heavy and light chains) and the antigen.

 

When setting up a residue scan, you have to specify the properties to be calculated. In this case, we are working with an antibody – antigen complex and want to improve the stability of the antibody without adversely impacting the binding affinity between the antibody and antigen. So both Stability and Affinity need to be calculated for each mutant.

 

  1. For Calculation type, choose Stability and Affinity.

 

Next, you need to specify the chains of the antibody-antigen complex between which affinity should be calculated.

 

  1. For Binding partners, clear the G checkbox in the Chains menu.
    • Chains H, L (antibody) are binding to Chain G (antigen).

Mutagenesis studies are commonly performed by first scanning single mutations across multiple residues to systematically evaluate their individual effects on binding affinity and stability. The insights gained from single-mutant analysis then serve as a rational basis for performing simultaneous mutations in subsequent studies.

 

While performing simultaneous mutations, we recommend selecting 2 to 5 residues while performing residue scanning. This is crucial because multiple mutations can work synergistically, resulting in a significantly greater gain in affinity than individual mutations. It also provides the opportunity to balance stability and affinity, as additional mutations can be used to compensate for any destabilizing effects introduced by an affinity-boosting change.

Figure 3-3. Specifying mutations and running the job.

Now, you will specify which mutants you want to generate.

 

  1. In the Residues tab, make the following changes in the antibody CDR residues by clicking in the Mutations column of each entry:
    • Chain H: 61 (ARG) to Standard
    • Chain H: 60 (PRO) to Standard
    • Chain H: 56 (GLN) to Standard
  2. Change the Job name to mmgbsa_residue_scan_4JPV.
  3. Optional: Click Run.
    • We do not recommend running this job as it is time-consuming.
    • You will analyze the pre-generated results included in the tutorial files.

3.2 Analyze the pre-generated results

Figure 3-4. Importing the MM-GBSA calculation results.

  1. Go to Files > Import Structures.
  2. Navigate to and choose mmgbsa_residue_scan_4JPV-out.maegz.
  3. Click Open.
    • Structures are added to the Entriesa simplified view of the Project Table that allows you to perform basic operations such as selection and inclusion.
  4. Click the workflow icon next to mmgbsa_residue_scan_4JPV-out group header and choose View MM-GBSA Residue Scanning Results.
    • The MM-GBSA Residue Scanning Viewer panel opens.
    • Δ Stability (solvated) vs Δ Affinity graph is shown below the Mutations table.

ΔStability (solvated) and ΔAffinity and values are most helpful in assessing the potential impact of a mutation.

 

Interpreting ΔStability

Stability is the change in the Gibbs free energy of folding (ΔGfolding) i.e. it is the difference between the native (folded) state and the denatured (unfolded) state of a protein. A more negative ΔGfolding means a more stable protein. ΔStability measures how a mutation changes the overall stability of a protein i.e. it measures the change in the folding free energy of the protein itself upon mutation – independent of ligand binding.

 

     ΔStability = ΔGstability (mutant) – ΔGstability (wild-type)

 

Where,                                                 ΔGstability = Gfolded – Gunfolded

                                             

Interpreting ΔAffinity

Affinity is the change in the Gibbs free energy of binding (ΔGbinding) i.e. it is the energy difference between the bound and unbound states. A more negative ΔGbinding means a stronger, more favorable binding interaction. ΔAffinity measures how a mutation changes the binding strength between a protein and its binding partner.

 

ΔAffinity = ΔGaffinity (mutant) – ΔGaffinity (wild-type)

 

Where,                                               ΔGaffinity = Gbound – Gunbound

Figure 3-5. Investigating the favorable mutations.

You will now investigate the favourable mutations.

 

  1. On the graph, click and drag to select the highlighted region in the graph (see the adjacent figure).
  2. Confirm that the Sync selection with mutations table box is checked.
    • The filtered results are now selected in the Mutations table.

 

Note: It has been documented that there is a bias toward ARG and MET in residue scanning outputs. We recommend keeping this in mind when designing your residue scanning experiments and stability maturation, and to look critically at results that suggest beneficial methionine or arginine mutations.

The graph displays a correlation between Δ Stability (solvated) and the Δ Affinity for the generated mutations. Each point corresponds to a specific mutation, with its position determined by how the mutation affects the system’s stability (X-axis) and binding affinity (Y-axis).

Figure 3-6. Examining mutations favorable to affinity.

  1. Change the X-Axis Property to Mutant Row Number.
    • The graph updates to show ΔAffinity values for each mutation.
  2. Click and drag to select the highlighted points in the graph (see the adjacent figure).
    • The filtered results are now selected in the Mutations table.
    • Take your time to examine the favorable mutations.

Most of the favorable mutations are predominantly those that introduce bulkier hydrophobic or aromatic side chains, thus improving the binding affinity by enhancing the van der Waals and lipophilic contributions. For example, H:60 PRO to TRP/TYR provide extended aromatic surfaces that can engage in pi-pi or hydrophobic stacking with the antigen, stabilizing the interface.

Figure 3-7. Examining the mutations favorable to stability.

  1. Change the Y-Axis Property to ΔStability (solvated).
    • The graph updates to show ΔStability (solvated) values for each mutation.
    • Take your time to examine the favorable mutations.
  2. Optional: Select the points in the graphs with high positive ΔStability (solvated) values and examine the corresponding mutations.
  3. Close the MM-GBSA Residue Scanning Viewer panel.

When designing mutagenesis experiments, it is important to interpret improvements in predicted affinity or stability with caution. While mutations that enhance binding affinity and structural stability are often desirable, these gains do not always translate into improved functional potency. In antibodies, excessive stabilization or overly tight binding can alter binding kinetics, epitope recognition, or conformational flexibility that may be essential for biological activity. For example, mutations that rigidify CDRs or increase hydrophobic contacts may improve MM-GBSA scores but can impair induced fit, or negatively impact downstream effector functions.

4. Conclusion and References

In this tutorial, you learned how to set up an MM-GBSA Residue Scanning calculation and analyze the results to identify mutations that enhance antibody stability and affinity.

It is important to note that MM-GBSA Residue Scanning serves as an initial screening step, and that promising mutations identified should be further validated using FEP-based methods such as FEP+ residue scanning and Protein FEP+. Please consult the Antibody Modeling learning path or further learning section below for additional resources.

For further reading:

5. Glossary of Terms

Entries - 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

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

Recent actions - This is a list of your recent actions, which you can use to reopen a panel, displayed below the Browse row. (Right-click to delete.)

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 Entries (and Project Table) and the row for the entry is highlighted. Project operations are performed on all selected entries

Working Directory - the location where files are saved

Workspace - the 3D display area in the center of the main window, where molecular structures are displayed