Exercise:AntibodyStructurePrediction: Difference between revisions

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= Antibody Structure Modelling: From Sequence to 3D Structure =
= Antibody Structure Modelling: From Sequence to 3D Structure =


'''Duration:''' 1–2 hours
__TOC__


'''Requirements:''' [https://pymol.org/2/ PyMOL] and a web browser.
== Introduction: ==


== Introduction ==
Antibodies recognize antigens through their variable domains, which contain six complementarity-determining regions (CDRs): three in the heavy chain and three in the light chain. These loops form the antigen-binding site and are supported by more conserved framework (FW) regions.


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.
Predicting the three-dimensional structure of antibodies helps us understand how their sequence, shape, and chemical properties may influence antigen recognition. Different computational approaches, from traditional template-based modelling to modern deep-learning methods, can be used to predict antibody structures.


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, you will model an antibody from a patient with chronic lymphocytic leukemia (CLL), explore its structure using PyMOL, and investigate how sequence variation and structural features may influence antigen binding.


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 ==


'''Learning objectives:'''
By the end of this exercise, you should be able to:


* Identify the framework and CDR regions of an antibody.
* Identify the framework and CDR regions of an antibody.
* Generate an antibody structure using a modern prediction tool.
* Understand the basic principles of template-based antibody modelling.
* Visualize and interpret the antigen-binding site.
* Generate and visualize a three-dimensional antibody model using computational tools.
* Understand prediction confidence and limitations.
* Explore how CDR sequence, structure, and electrostatic properties may influence antigen recognition.
* Explore how amino acid properties influence antigen recognition.
* Identify somatic mutations and discuss their potential effects on antibody binding.
* Compare antibody structures predicted using traditional template-based and modern deep-learning approaches.


== Antibody sequences ==
== Antibody sequences ==


We will use the variable regions of the SI135 antibody.
We will use the variable regions of the SI135 antibody.
'''Heavy chain (VH):'''
<pre>
>SI135_VH
EVQLVESGGGLVRPGGSLRLSCAASGFTFSTYSMNWVRQAPGKGLEWVSSISSSSNSIYCADSVKGRFTISRDNANNSLYMQMNSLRAEDTAVYYCARDVNTMDVWGQGILVTVLL
</pre>


'''Light chain (VL):'''
'''Light chain (VL):'''
Line 40: Line 33:
</pre>
</pre>


These sequences correspond to the variable domains, not the complete antibody.
'''Heavy chain (VH):'''
 
== Part 1 — Identify the antibody regions (15 minutes) ==
 
Go to the [https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/anarcii/ ANARCII] website.
 
ANARCII identifies antibody and T-cell receptor sequences and assigns standardized residue numbering.
 
# Paste the light-chain protein sequence.
# Select '''Antibody''' and the '''IMGT''' numbering scheme.
# 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 [https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/abodybuilder2/ ABodyBuilder2] website.
 
ABodyBuilder2 is a deep-learning-based tool that predicts the three-dimensional structure of an antibody from its amino acid sequence.
 
# Paste the heavy-chain and light-chain sequences in the corresponding fields.
# Select the '''IMGT''' numbering scheme.
# Submit the prediction.
# Explore the predicted structure and the prediction-error estimates.
# 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:


<pre>
<pre>
hide everything
>SI135_VH
show cartoon
EVQLVESGGGLVRPGGSLRLSCAASGFTFSTYSMNWVRQAPGKGLEWVSSISSSSNSIYCADSVKGRFTISRDNANNSLYMQMNSLRAEDTAVYYCARDVNTMDVWGQGILVTVLL
color marine, chain H
color wheat, chain L
</pre>
</pre>


''Note: These commands assume the chains are named H and L. Check the actual chain identifiers in your PDB file.''
== Part 1 — Identify the antibody regions ==


=== Identify the CDRs ===
Go to the [https://services.healthtech.dtu.dk/services/LYRA-1.0/ Lyra] web page.


Each antibody variable domain contains three CDR loops. Together, the heavy and light chains form the antigen-binding site.
''Lyra is a tool for predicting antibody and TCR structures. It is possible to use Lyra in automated or expert mode.''


Using IMGT numbering, the CDR regions correspond to:
Insert the light and heavy chains in the proper text area and submit. After a few seconds you will arrive to the output page, where the alignment and all the templates are displayed.
 
Note that for each Region of each chain of the antibody a Template has been found.


{| class="wikitable"
{| class="wikitable"
! Region
! Region
! IMGT positions
! Templates
|-
| Framework
| ...
|-
|-
| CDR1
| Loop 1 (CDR1)
| 27–38
| ...
|-
|-
| CDR2
| Loop 2 (CDR2)
| 56–65
| ...
|-
|-
| CDR3
| Loop 3 (CDR3)
| 105–117
| ...
|}
|}


The exact number of residues in each loop varies between antibodies, and some IMGT positions may be absent.
Note that after the Regions there is an extra template used for packing the whole structure.


Select the CDRs and color them red. If the PDB uses IMGT numbering and the chains are named H and L, you can use:
Explore the output page and the different tabs - check the PDB file, Log, and manual selection ones to find this info.


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


If the file uses different residue numbering, use the numbering provided by the prediction tool to identify the corresponding regions.
Now answer the following questions:


Rotate the antibody and examine the six CDR loops.
'''Q1: what is the sequence similarity of the best H and L templates for the framework (FW)?'''


'''Q3.''' Which CDR appears to protrude the most from the antibody surface? Is it also the longest loop?
'''Q2: What is the light chain type? Is it the most common in humans?'''


=== Explore the antigen-binding site ===
=== Sequence alignment and CDR modelling ===


Display the antibody as a molecular surface using PyMOL.
Focus on the sequence alignment used to generate the model. You can find it in one of the tabs in Lyra website, as well as in the PDB file itself.


<pre>
'''Q3: where are the differences between target and template located? Which are the most drastic that could affect the modeling process (e.g. G->W)?'''
show surface
set transparency, 0.3
</pre>


Look at the surface formed by the six CDR loops.
''hint: if you don't remeber the amino acids, you can use the Blosum62 matrix to estimate the expected effect of a substitution: the lower the number, the larger the expected impact. For sake of time, you only need to give a qualitative answer.''


'''Q4.''' How would you describe the general shape of the antigen-binding site: flat, pocket, mountain, or groove? Explain briefly.
'''Q4: Do the loops/CDR have the same length in target and template? What does it mean, in terms of expected accuracy?'''


== Part 4 — Explore the electrostatic properties (15–20 minutes) ==
=== Selection of structural templates ===


The amino acid composition of the CDRs influences their interactions with antigens.
Now, look at the templates automatically selected by Lyra to build the final model.


In PyMOL, generate an approximate electrostatic representation using:
'''Q5: How many templates were used? Which is the region for which the template is more dissimilar to the target sequence?'''


'''Action → Generate → Vacuum Electrostatics → Protein Contact Potential'''


Inspect the distribution of positively and negatively charged regions around the antigen-binding site.
== Part 2 — Exploring the antibody structure in PyMOL ==


Remember:
=== Visualizing the antigen-binding site (ABS) ===


* '''K (lysine) and R (arginine)''' are positively charged.
Download the pdb file and open it with pymol. There is a small "S" bottom right, it shows/hides the sequence. Show the whole protein as cartoon and color by chain. Select and color in red all the CDRs. (the selected residues appear as the "sele" item in the menu). The CDR definition can be seen in the alignment generated by Lyra.
* '''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?
'''Q6: What is the general shape of the ABS: flat, a pocket, a mountain or a groove?''' ''It can help to show the antibody as surface''


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


== Part 5 — How much can we trust the prediction? (10 minutes) ==
'''Q7: Which loop is the longest and protruding the most? Is this the most common case?'''


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.
== Part 3 — Somatic mutations ==


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


'''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.
Use the [http://www.imgt.org/3Dstructure-DB/cgi/DomainGapAlign.cgi Domain Gap Align tool] identify the somatic mutations in the V and J regions. You need to use the amino acid sequence and select human as the source organism. In PyMol, show the mutations as sticks (main chain and side chain) and color them green.


== Optional exercises ==
Unfortunately today the server has a planning update so the output of the tool are provided below


=== A. Compare traditional and modern antibody modelling ===
[[image:Imgt_GapAlign_results_VH.png|1000px|center]]


Go to the [https://services.healthtech.dtu.dk/services/LYRA-1.0/ LYRA] website.
[[image:Imgt_GapAlign_results_VL.png|1000px|center]]


LYRA predicts antibody structures using templates from experimentally determined structures.
'''Q8: Which Mutations are located inside/near the CDRs? Which are the ones that are most likely to have an impact on the binding of the antibody?''' ''A qualitative answer is sufficient''


# Submit the same VH and VL sequences.
== Part 4 — Electrostatic properties of the antigen-binding site ==
# Examine the selected structural templates.
# Download the predicted PDB structure.
# Open the LYRA and ABodyBuilder2 models in PyMOL.
# 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?
=== Visualizing the electrostatic potential ===


=== B. Modelling T-cell receptors ===
Generate the electrostatic potentials of the Ig ('''A'''ction -> generate -> vacuum electrostatic -> protein contact potentials).


Antibodies are not the only immune receptors that can be predicted using modern deep-learning approaches.
'''Q9: Identify the residues in the CDRs that mostly contribute to the charge distribution.'''


Go to the [https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/tcrbuilder2/ TCRBuilder2+] website.
''highlight them in the sequence and look for charged residues such as K, R, D, E''


Explore an example structure prediction of a T-cell receptor (TCR).
=== Modifying the electrostatic properties (optional) ===


'''Optional Q2.''' What structural similarities can you identify between antibody and TCR variable domains? How do their biological recognition functions differ?
'''Q10: (optional) Mutate some residues in order to have a positive charge in the middle of the antigen binding site, then recalculate the electrostatic potentials. Can you see the changes?'''


== Submission ==


Submit:
== Part 5 — Comparing antibody modelling approaches ==


* One PyMOL screenshot showing the heavy and light chains with the CDRs highlighted.
=== LYRA versus ABodyBuilder2 ===
* Short answers to '''Q1–Q6''' (approximately 1–3 sentences per question).


'''Finished!'''
'''Q11: Go to the AbodyBuilder 2 site and make a new structure prediction. Download the PDB file and compare with the model generated by LYRA. Can you see some differences?'''




 
'''Finished!'''
Antibody modelling
 
Requirements: Pymol
 
Download the version for your OS, then use the license file provided by the teachers
 
Modeling of CLL antibody
 
These are the sequences for the light and heavy chain of the antibody:
 
    >SI135 VH
 
    GAGGTGCAGCTGGTGGAGTCTGGGGGAGGCCTGGTCAGGCCTGGGGGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTCAGTACCTATAGCATGAACTGGGTCCGCCAGGCTCCAGGGAAGGGGCTGGAGTGGGTCTCATCCATTAGTAGTAGTAGTAATTCCATATACTGCGCAGACTCAGTGAAGGGCCGATTCACCATCTCCAGAGACAACGCCAACAACTCACTGTATATGCAAATGAATAGCCTGAGAGCCGAGGACACGGCTGTCTATTACTGTGCGAGAGATGTTAACACTATGGACGTCTGGGGCCAAGGGATCCTGGTCACCGTNCTCCTCA
 
    >SI135 VL
 
    TCCTATGTGGTGACTCAGCCACCCTCAGTGTCAGTGGCCCCAGGAAAGACGGCCAGGATTACCTGTGGGGGAAACAACATTGGAAGTAAAAGTGTGCACTGGTACCAGCAGAAGCCAGGCCAGGCCCCTGTGCTGGTCATCTATTATGATAACGACCGGCCCTCAGGGATCCCTGAGCGATTCTCTGGCTCCAACTCTGGGAACACGGCCACCCTGACCATCAGCAGGGTCGAAGCCGGGGATGAGGCCGACTATTACTGTCAGGTGTGGGATAGTGGTAGTGATCATCCTTGGGTGTTCGGCGGAGGGACCAAGCTGACCGTCCTA
 
Translate the nucleic acid sequences to protein sequences (use a service of your choice, e.g. expasy or virtual ribosome).
 
Go to the Lyra web page.
 
Lyra is a tool for predicting antibody and TCR structures. It is possible to use Lyra in automated or expert mode.
 
Insert the light and heavy chains in the proper text area and submit. After a few seconds you will arrive to the output page, where the alignment and all the templates are displayed.
 
Q1: what is the sequence similarity of the best H and L templates for the framework (FW)?
 
hint: In the output page you have a few tabs - check the PDB file, Log, and manual selection ones to find this info.
 
Q2: What is the light chain type? Is it the most common in humans?
 
Focus on the sequence alignment used to generate the model. You can find it in one of the tabs in Lyra website, as well as in the PDB file itself
 
Q3: where are the differences between target and template located? Which are the most drastic that could affect the modeling process (e.g. G->W)?
 
hint: if you don't remeber the amino acids, you can use the Blosum62 matrix to estimate the expected effect of a substitution: the lower the number, the larger the expected impact. For sake of time, you only need to give a qualitative answer.
 
Q4: Do the loops/CDR have the same length in target and template? What does it mean, in terms of expected accuracy?
 
Now, look at the templates automatically selected by Lyra to build the final model
 
Q5: How many templates were used? Which is the region for which the template is more dissimilar to the target sequence?
 
Download the pdb file and open it with pymol. There is a small "S" bottom right, it shows/hides the sequence. Show the whole protein as cartoon and color by chain. Select and color in red all the CDRs. (the selected residues appear as the "sele" item in the menu). The CDR definition can be seen in the alignment generated by Lyra.
 
Q6: What is the general shape of the ABS: flat, a pocket, a mountain or a groove? It can help to show the antibody as surface
 
Q7: Which loop is the longest and protruding the most? Is this the most common case?
 
Use the Domain Gap Align tool identify the somatic mutations in the V and J regions. You need to use the amino acid sequence and select human as the source organism. In PyMol, show the mutations as sticks (main chain and side chain) and color them green
 
Q8: Which Mutations are located inside/near the CDRs? Which are the ones that are most likely to have an impact on the binding of the antibody? A qualitative answer is sufficient
 
Generate the electrostatic potentials of the Ig ( Action -> generate -> vacuum electrostatic -> protein contact potentials).
 
Q9: Identify the residues in the CDRs that mostly contribute to the charge distribution.
highlight them in the sequence and look for charged residues such as K, R, D, E
 
Q10: (optional) Mutate some residues in order to have a positive charge in the middle of the antigen binding site, then recalculate the electrostatic potentials. Can you see the changes?
 
Q11: Go to the AbodyBuilder 2 site and make a new structure prediction. Download the PDB file and compare with the model generated by LYRA. Can you see some differences?
 
Finished!

Latest revision as of 04:17, 21 October 2026

Antibody Structure Modelling: From Sequence to 3D Structure

Introduction:

Antibodies recognize antigens through their variable domains, which contain six complementarity-determining regions (CDRs): three in the heavy chain and three in the light chain. These loops form the antigen-binding site and are supported by more conserved framework (FW) regions.

Predicting the three-dimensional structure of antibodies helps us understand how their sequence, shape, and chemical properties may influence antigen recognition. Different computational approaches, from traditional template-based modelling to modern deep-learning methods, can be used to predict antibody structures.

In this exercise, you will model an antibody from a patient with chronic lymphocytic leukemia (CLL), explore its structure using PyMOL, and investigate how sequence variation and structural features may influence antigen binding.

Learning objectives

By the end of this exercise, you should be able to:

  • Identify the framework and CDR regions of an antibody.
  • Understand the basic principles of template-based antibody modelling.
  • Generate and visualize a three-dimensional antibody model using computational tools.
  • Explore how CDR sequence, structure, and electrostatic properties may influence antigen recognition.
  • Identify somatic mutations and discuss their potential effects on antibody binding.
  • Compare antibody structures predicted using traditional template-based and modern deep-learning approaches.

Antibody sequences

We will use the variable regions of the SI135 antibody.

Light chain (VL): 

>SI135_VL
SYVVTQPPSVSVAPGKTARITCGGNNIGSKSVHWYQQKPGQAPVLVIYYDNDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDSGSDHPWVFGGGTKLTVL

Heavy chain (VH): 

>SI135_VH
EVQLVESGGGLVRPGGSLRLSCAASGFTFSTYSMNWVRQAPGKGLEWVSSISSSSNSIYCADSVKGRFTISRDNANNSLYMQMNSLRAEDTAVYYCARDVNTMDVWGQGILVTVLL

Part 1 — Identify the antibody regions

Go to the Lyra web page.

Lyra is a tool for predicting antibody and TCR structures. It is possible to use Lyra in automated or expert mode.

Insert the light and heavy chains in the proper text area and submit. After a few seconds you will arrive to the output page, where the alignment and all the templates are displayed.

Note that for each Region of each chain of the antibody a Template has been found.

Region Templates
Framework ...
Loop 1 (CDR1) ...
Loop 2 (CDR2) ...
Loop 3 (CDR3) ...

Note that after the Regions there is an extra template used for packing the whole structure.

Explore the output page and the different tabs - check the PDB file, Log, and manual selection ones to find this info.


Now answer the following questions:

Q1: what is the sequence similarity of the best H and L templates for the framework (FW)?

Q2: What is the light chain type? Is it the most common in humans?

Sequence alignment and CDR modelling

Focus on the sequence alignment used to generate the model. You can find it in one of the tabs in Lyra website, as well as in the PDB file itself.

Q3: where are the differences between target and template located? Which are the most drastic that could affect the modeling process (e.g. G->W)?

hint: if you don't remeber the amino acids, you can use the Blosum62 matrix to estimate the expected effect of a substitution: the lower the number, the larger the expected impact. For sake of time, you only need to give a qualitative answer.

Q4: Do the loops/CDR have the same length in target and template? What does it mean, in terms of expected accuracy?

Selection of structural templates

Now, look at the templates automatically selected by Lyra to build the final model.

Q5: How many templates were used? Which is the region for which the template is more dissimilar to the target sequence?


Part 2 — Exploring the antibody structure in PyMOL

Visualizing the antigen-binding site (ABS)

Download the pdb file and open it with pymol. There is a small "S" bottom right, it shows/hides the sequence. Show the whole protein as cartoon and color by chain. Select and color in red all the CDRs. (the selected residues appear as the "sele" item in the menu). The CDR definition can be seen in the alignment generated by Lyra.

Q6: What is the general shape of the ABS: flat, a pocket, a mountain or a groove? It can help to show the antibody as surface

CDR loop structure

Q7: Which loop is the longest and protruding the most? Is this the most common case?


Part 3 — Somatic mutations

Identifying mutations in the antibody sequence

Use the Domain Gap Align tool identify the somatic mutations in the V and J regions. You need to use the amino acid sequence and select human as the source organism. In PyMol, show the mutations as sticks (main chain and side chain) and color them green.

Unfortunately today the server has a planning update so the output of the tool are provided below

Q8: Which Mutations are located inside/near the CDRs? Which are the ones that are most likely to have an impact on the binding of the antibody? A qualitative answer is sufficient

Part 4 — Electrostatic properties of the antigen-binding site

Visualizing the electrostatic potential

Generate the electrostatic potentials of the Ig (Action -> generate -> vacuum electrostatic -> protein contact potentials).

Q9: Identify the residues in the CDRs that mostly contribute to the charge distribution.

highlight them in the sequence and look for charged residues such as K, R, D, E

Modifying the electrostatic properties (optional)

Q10: (optional) Mutate some residues in order to have a positive charge in the middle of the antigen binding site, then recalculate the electrostatic potentials. Can you see the changes?


Part 5 — Comparing antibody modelling approaches

LYRA versus ABodyBuilder2

Q11: Go to the AbodyBuilder 2 site and make a new structure prediction. Download the PDB file and compare with the model generated by LYRA. Can you see some differences?


Finished!

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