Tag Archives: Antibodies

Antibody Binding is Mediated by a Compact Vocabulary of Paratope-Epitope Interactions

While my own research focuses mainly on what happens in an antibody before it binds its antigen, I recently came across a paper by Akbar et al [1] that examines antibody-antigen interactions using an elegant approach to identify a set of structural motifs that antibodies use to interact with their epitopes. Since I am interested in emergent properties that arise when a sequence is mapped onto an antibody structure, this paper was very exciting. I will also shamelessly admit that I’m a sucker for a pretty figure and this paper has many! Regardless, on to the findings!

Example of identified interaction motifs. Figure from Akbar et al, 2021
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The Coronavirus Antibody Database: 10 months on, 10x the data!

Back in May 2020, we released the Coronavirus Antibody Database (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, most of which were characterised shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain…

10 months later (2nd March 2021), we now have tracked 2,673 coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These datapoints originate from 100s of independent studies reported in either the academic literature or patent filings.

The entire contents CoV-AbDab database as of 2nd March 2021.
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It’s been here all along: Analysis of the antibody DE loop

In my work, I mainly look at antigen-bound antibodies and this means a lot of analysing interfaces. Specifically, I spend a lot of my time examining the contributions of complementarity-determining regions (CDRs) to antigen binding, but what about antibodies where the framework (FW) region also contributes to binding? Such structures do exist, and these interactions are rarely trivial. As such, a recent preprint I came across where the authors examined the DE loops of antibodies was a great motivator to broaden my horizons!

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Identifying shared antibodies using deep learning

Antibody convergence is the presence of similar antibodies in different individuals – suggesting that the individuals have had exposure to a common antigen, which has stimulated the production of similar, antigen-specific antibodies. We want to be able to identify these shared antibodies, sometimes referred to as ‘public clones’, as it could lead to development of immunodiagnostic tests against the shared antibodies, and potentially assist in the design of vaccines and therapeutic antibodies. A recent paper on bioRxiv by Sai Reddy’s group[i] has applied deep learning techniques – variational autoencoders (VAE) and support vector machines (SVM) – to the problem of how to identify shared antibodies.

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Do we need the constant regions of Antibodies and T-cell receptors in molecular simulations?

At this week’s journal club I presented my latest results on the effect of the constant regions of antibodies (ABs) and T-cell receptors (TCRs) on the dynamics of the overall system. Not including constant regions in such simulations is a commonly used simplification that is found throughout the literature. This is mainly due a massive saving in computational runtime as illustrated below: cutConstRegions

The constant regions contain about 210 residues but an additional speed up comes from the much smaller solvation box. If a cubic solvation box is used then the effect is even more severe:

waterbathBut the question is: “Is is OK to remove the constant regions of an AB or TCR and simulate without them?”.

Using replica simulations we found that simulations with and without constant regions lead to (on average) significantly different results. The detail of our analysis will soon be submitted to a scientific journal. The current working title is “Why constant regions are essential in antibody and T-cell receptor Molecular Dynamics simulations”.