Exercise:AntibodyStructurePrediction

__NOTITLE__

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.

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!