This blog post finally combines the two great passions of my life: antibodies and dogs. Therapeutic antibody development is a huge area and is certainly not limited to humans. In the process of developing antibodies, we often use mouse or rat antibodies, obtained by injecting the animal with the antigen of choice and then collecting the resulting antibodies. The first monoclonal antibodies (mAbs) were produced in this way, by fusing spleen B cells from an immunised mouse or rabbit with immortalised myeloma cells to form antibody-expressing hybridoma cells. However, using antibodies to treat disease in animals lags behind humans.
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Re-educating myself about the light chain
I have an unconscious habit of personification, and I always see the antibody light chain as lazy for not contributing more residues to binding interfaces (obviously a generalisation – e.g. insertions in CDRL4 in anti-HIV bNAbs [1]). Perhaps this is why I have a personal preference for the more diverse [2] heavy chain with its specificity-determining [3] CDR3. Having written this down, I realised it’s actually pretty weird to consider an antibody chain as a person and I ought to re-educate myself about the role that light chains play.
Continue readingC is for Cysteines (plus a fun quiz)
At group meeting a few weeks ago I presented this paper, “Landscape of Non-canonical Cysteines in Human VH Repertoire Revealed by Immunogenetic Analysis“, from Prabakaran and Chowdhury. The paper is an investigation of the frequency, location and patterns of cysteines contained in human antibody sequences. Cysteines are important amino acids found in proteins, including antibodies, which can form disulphide bonds with other cysteines due to the presence of their reactive sulfhydryl group in the side chain.
Continue readingObserved Antibody Space + miAIRR
Today is the day for another (potentially penultimate) blog post from me. Using this opportunity, I would like to introduce to you our recent update to the Observed Antibody Space (OAS) resource.
Continue readingJournal Club: the Dynamics of Affinity Maturation
Last week at our group meeting I presented on a paper titled “T-cell Receptor Variable beta Domains Rigidify During Affinity Maturation” by Monica L. Fernández-Quintero, Clarissa A. Seidler and Klaus R. Liedl. The authors use metadynamics simulations of the same T-cell Receptor (TCR) at different stages of affinity maturation to study the conformational landscape of the complementarity-determining regions (CDRs), and how this might relate to an increase in affinity. Not only do they conclude that affinity maturation leads to rigidification of CDRs in solution, but they also present some evidence for the conformational selection model of biomolecular binding events in TCR-antigen interactions.
Continue readingThe Coronavirus Antibody Database (CoV-AbDab)
We are happy to announce the release of CoV-AbDab, our database tracking all coronavirus binding antibodies and nanobodies with molecular-level metadata. The database can be searched and downloaded here: http://opig.stats.ox.ac.uk/webapps/coronavirus
Continue readingNon-specialist intro: Convalescent sera and some thoughts on its relevance to structural biology
A couple of weeks ago, I gave a group meeting talk on my current research. Interestingly most of the questions I received were not directly related to my research methods, but rather, on the broader application of antibody-related therapies, as I used the example of convalescent sera as a potential ‘quick fix’ in the current COVID-19 pandemic, to motivate why antibody research is important! So I thought in this blog post, I would give a quick introduction to convalescent sera. (Disclaimer: This does not contain any clinical information.)
Continue readingIdentifying 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 readingTCRBuilder: Multi-state T-cell receptor structure prediction
Hello friends of OPIG,
From my last blopig blog post [link: https://www.blopig.com/blog/2019/10/comparative-analysis-of-the-cdr-loops-of-antigen-receptors/], I summarised our findings that TCR CDRs are more flexible than their antibody counterparts. Because of this observation, we believe that it is more appropriate to represent TCR binding sites using an ensemble of conformations.
Continue readingParallelising antigen-specific B-cell isolation with LIBRA-seq
Today is the day when I write a blog about an exciting research paper in the field of B-cell receptor (BCR) repertoires analysis. At OPIG, we (antibody people) are working hard to model and characterise antibody 3D configuration from its sequence. Significant progress has been made in modelling software development, so that we can predict antibody structures with high confidence. This task becomes considerably harder when we model the entirety of BCR repertoire sequences. Current methods of BCR repertoire sequencing operate primarily on the heavy chain only. This limits our capacity to generate refined 3D antibody models to just approximation of shapes of complementarity determining regions(CDRs).
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