Tag Archives: Immunoinformatics

The Antibody Dictionary

Similar to getting lost in a language when moving country, you might encounter a language barrier when moving research fields. This dictionary will guide you in the complex world of immunoinformatics, with a focus on antibodies. Whether your main research will be in this field, you want to apply your machine learning model on antibodies, or you just want to understand the research performed in OPIG, this dictionary will get you started.

The Antibody Dictionary:

Affinity maturation: The optimisation process of naive antibodies to memory antibodies such that the antibody is optimised for a specific antigen. 

Antibody: (immunoglobulin) a Y-shaped molecule important in the adaptive immune system. A canonical antibody consists of two identical heavy chains and two identical smaller light chains. 

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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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