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!
Continue readingTag Archives: Antibodies
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.
Continue readingComparative analysis of the CDR loops of antigen receptors
Allow me to present our recently accepted paper: Comparative analysis of the CDR loops of antigen receptors, to appear in Frontiers in Immunology [1]. In the blog post I will give a quick five-minute summary of the key messages in this work.
Continue readingDo 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: 
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:
But 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”.