Exercise:AntibodyStructurePrediction v2

Antibody Structure Modelling: From Sequence to 3D Structure

Duration: 1–2 hours

Requirements: PyMOL and a web browser.

Introduction

Antibodies recognize antigens through six complementarity-determining regions (CDRs): three in the heavy chain and three in the light chain. These loops are supported by more conserved framework (FW) regions.

Modern deep-learning tools can predict antibody structures directly from amino acid sequences. However, not all regions are predicted with equal accuracy, and a predicted structure does not necessarily reveal what an antibody binds.

In this exercise, we will predict and explore the three-dimensional structure of an antibody from a patient with chronic lymphocytic leukemia (CLL).

Learning objectives:

• Identify the framework and CDR regions of an antibody.
• Generate an antibody structure using a modern prediction tool.
• Visualize and interpret the antigen-binding site.
• Understand prediction confidence and limitations.
• Explore how amino acid properties influence antigen recognition.

Antibody sequences

We will use the variable regions of the SI135 antibody.

Heavy chain (VH):

>SI135_VH
EVQLVESGGGLVRPGGSLRLSCAASGFTFSTYSMNWVRQAPGKGLEWVSSISSSSNSIYCADSVKGRFTISRDNANNSLYMQMNSLRAEDTAVYYCARDVNTMDVWGQGILVTVLL

Light chain (VL):

>SI135_VL
SYVVTQPPSVSVAPGKTARITCGGNNIGSKSVHWYQQKPGQAPVLVIYYDNDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDSGSDHPWVFGGGTKLTVL

These sequences correspond to the variable domains, not the complete antibody.

Part 1 — Identify the antibody regions (15 minutes)

Go to the ANARCII website.

ANARCII identifies antibody and T-cell receptor sequences and assigns standardized residue numbering.

1. Paste the light-chain protein sequence.
2. Select Antibody and the IMGT numbering scheme.
3. Submit the sequence and inspect the result.

Q1. Is the light chain kappa (κ) or lambda (λ)? Which light-chain type is more common in humans?

Part 2 — Predict the antibody structure (15–20 minutes)

Go to the ABodyBuilder2 website.

ABodyBuilder2 is a deep-learning-based tool that predicts the three-dimensional structure of an antibody from its amino acid sequence.

1. Paste the heavy-chain and light-chain sequences in the corresponding fields.
2. Select the IMGT numbering scheme.
3. Submit the prediction.
4. Explore the predicted structure and the prediction-error estimates.
5. Download the predicted structure as a PDB file.

Q2. Which regions have the highest predicted error? Are they framework regions or CDRs? Why might some regions of an antibody be harder to predict than others?

Part 3 — Explore the structure in PyMOL (25 minutes)

Download the PDB file generated by ABodyBuilder2 and open it in PyMOL.

Display the antibody as a cartoon and color the heavy and light chains differently.

You can use the graphical interface or type the following commands in PyMOL:

hide everything
show cartoon
color marine, chain H
color wheat, chain L

Note: These commands assume the chains are named H and L. Check the actual chain identifiers in your PDB file.

Identify the CDRs

Each antibody variable domain contains three CDR loops. Together, the heavy and light chains form the antigen-binding site.

Using IMGT numbering, the CDR regions correspond to:

Region	IMGT positions
CDR1	27–38
CDR2	56–65
CDR3	105–117

The exact number of residues in each loop varies between antibodies, and some IMGT positions may be absent.

Select the CDRs and color them red. If the PDB uses IMGT numbering and the chains are named H and L, you can use:

select cdrs, chain H+L and resi 27-38+56-65+105-117
color red, cdrs

If the file uses different residue numbering, use the numbering provided by the prediction tool to identify the corresponding regions.

Rotate the antibody and examine the six CDR loops.

Q3. Which CDR appears to protrude the most from the antibody surface? Is it also the longest loop?

Explore the antigen-binding site

Display the antibody as a molecular surface using PyMOL.

show surface
set transparency, 0.3

Look at the surface formed by the six CDR loops.

Q4. How would you describe the general shape of the antigen-binding site: flat, pocket, mountain, or groove? Explain briefly.

Part 4 — Explore the electrostatic properties (15–20 minutes)

The amino acid composition of the CDRs influences their interactions with antigens.

In PyMOL, generate an approximate electrostatic representation using:

Action → Generate → Vacuum Electrostatics → Protein Contact Potential

Inspect the distribution of positively and negatively charged regions around the antigen-binding site.

Remember:

• K (lysine) and R (arginine) are positively charged.
• D (aspartate) and E (glutamate) are negatively charged.
• Other amino acids can contribute to antigen recognition through hydrogen bonds, hydrophobic interactions, and shape complementarity.

Q5. Identify one or two charged residues in the CDRs that contribute to the electrostatic properties of the antigen-binding site. Where are they located?

Note: The vacuum electrostatic representation provides a qualitative indication of the surface charge distribution. It is not a rigorous calculation of binding energy.

Part 5 — How much can we trust the prediction? (10 minutes)

Return to the ABodyBuilder2 results and examine the reported prediction errors.

The framework of an antibody is usually more conserved than its CDR loops. In particular, CDR-H3 can be highly variable in both sequence and conformation.

Modern prediction tools can generate realistic antibody structures, but uncertainty remains, especially in flexible or highly variable regions.

Q6. Can we confidently determine which antigen this antibody recognizes from its predicted structure alone? Explain what we can learn from the model and what additional information would be needed.

Optional exercises

A. Compare traditional and modern antibody modelling

Go to the LYRA website.

LYRA predicts antibody structures using templates from experimentally determined structures.

1. Submit the same VH and VL sequences.
2. Examine the selected structural templates.
3. Download the predicted PDB structure.
4. Open the LYRA and ABodyBuilder2 models in PyMOL.
5. Superimpose the two structures and compare them.

Optional Q1. Which regions show the largest structural differences between the two models? Do they agree on the overall fold?

B. Modelling T-cell receptors

Antibodies are not the only immune receptors that can be predicted using modern deep-learning approaches.

Go to the TCRBuilder2+ website.

Explore an example structure prediction of a T-cell receptor (TCR).

Optional Q2. What structural similarities can you identify between antibody and TCR variable domains? How do their biological recognition functions differ?

Submission

Submit:

• One PyMOL screenshot showing the heavy and light chains with the CDRs highlighted.
• Short answers to Q1–Q6 (approximately 1–3 sentences per question).

Finished!