The IMGT have adopted a more permissive licence for their data. That is a really good thing. These days, they also have an API. But the service is somewhat hamstrung by usability issues. Also, detailed and digestible documentation for such a sprawling suite of databases and tools never just appears overnight, so this post includes a demo to help you get started.
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SAbDab2: The structural antibody database in the age of machine learning
Henriette L. Capel, Odysseas Vavourakis, Benjamin H. Williams, Christopher R. Taylor, and Charlotte M. Deane
The Structural Antibody Database
The Structural Antibody Database (SAbDab) [1] is a publicly available repository of experimentally determined antibody structures, first released in 2013. Explicit support for single-domain antibodies was added in 2021, with SAbDab-nano [2]. Detailed annotations and consistent maintenance have made SAbDab a central resource supporting important advances in the field. SAbDab has been used to study antibody-antigen interactions, including SARS-CoV-2; to predict antibody structure; to design antibodies de-novo; and to investigate antibody flexibility.
Continue readingThe Open Immune Window: Notes on Sweaty Workouts and Vanishing Immune Cells
Here is a question for you: is an intense, sweaty workout in the gym building up your immune health, or is it just opening a window of opportunity for a pathogen to ruin your week? To understand this, we first have to look at energy. The immune system is incredibly energy-hungry, constantly patrolling and repairing the body. When you exercise hard, your body is forced into a rapid game of resource allocation, diverting precious energy away from baseline functions to fuel your contracting muscles.
This brings us to a rather scary observation in sports science that I stumbled on one day reading random headlines. If you draw blood one to two hours after a hard run or heavy exertion, your immune cell count (specifically lymphocytes) absolutely plummets. Apparently for decades, scientists looked at this massive drop in the blood and concluded that our immune system temporarily crashed after exercise, leaving an “open window” of 3 to 72 hours where we were highly vulnerable to infections. Which leads us back to the main question – is a hard workout actually making you sick?
Thankfully, no. It turns out those missing immune cells didn’t just die off. Driven by the acute spike in adrenaline from your workout, those cells rapidly exit your bloodstream and migrate directly into peripheral tissues, specifically mucosal barriers like your lungs and gut. Think about it: during a hard workout, you are hyperventilating and exposing your airway to massive amounts of external air. Your body isn’t suppressing its defenses; it’s actively deploying its best troops exactly where a pathogen is most likely to enter. It is a state of heightened immune surveillance, not suppression.
So why do athletes often get the sniffles after a big race? Often, it is just non-infectious airway inflammation from heavy breathing, combined with the psychological stress and lack of sleep that accompany big events. Your workout actually acts as a natural immune adjuvant, making you more resilient. If you want to dive deeper into this topic, I highly recommend checking out the paper Debunking the Myth of Exercise-Induced Immune Suppression by Campbell and Turner (Frontiers in Immunology, 2018).
Nice TCR processing libraries
As someone who works with T cell antigen receptor (TCR) and peptide-major histocompatibility complex (pMHC) data, I have found several Python packages to be very useful for eliminating tedious steps in data cleaning and feature engineering stages.
Continue readingNew DPhil/PhD Programme in Pharmaceutical Science Joint with GSK!
Many OPIGlets found their way into a DPhil in Protein Informatics through our Systems Approaches to Biomedical Sciences Industrial Doctoral Landscape Award, which was open to applicants 2009-2024. This innovative course, based at the MPLS Doctoral Training Centre (DTC), offered six months of intensive taught modules prior to starting PhD-level research, allowing students to upskill across a diverse range of subjects (coding, mathematics, structural biology, etc.) and to go on to do research in areas significantly distinct from their formal Undergraduate training. All projects also benefited from direct co-supervision from researchers working in the Pharmaceutical industry, ensuring DPhil projects in areas with drug discovery translation potential. Regrettably, having twice successfully applied for renewal of funding, we were unsuccessful in our bid to refund SABS in 2024.
Happily though, we can now formally announce that our bid for a direct successor to SABS, the Transformative Technologies in Pharmaceutical Sciences IDLA, has been backed by the BBSRC, and we will shortly be opening for applications for entry this October [2026]. As someone who benefited from the interdisciplinary training and industry-adjacency of SABS, I’m thrilled to be a co-director of this new Programme and to help deliver this course to a new generation of talented students.
The Experimentally Relevant Future of Molecular Dynamics: Lessons from the Annual Danish Workshop on Advanced Molecular Simulations
I recently had the opportunity to present part of my PhD work on molecular dynamics (MD) studies of engineered T Cell Receptors at the Annual Danish Conference on Advanced Molecular Simulations in Aarhus, Denmark. The meeting had an emphasis on membrane biophysics, multi- & mesoscale simulations, with keynotes focusing on connecting MD to experimental relevance.
What I mainly got from the keynotes, Weria Pezeshkian, Mohsen Sadeghi, Matteo Degiacomi, Lucie Delemotte, and Ilpo Vattulainen is that the community is shifting from from exploratory, proof-of-concept simulations towards more quantitative, decision-ready modelling. i.e., multiscale workflows that admit their limits, report uncertainties, and actually talk to experiments. There was a shared way of thinking about multiscale simulations by first getting the chemistry and thermodynamics right with atomistic or coarse-grained MD, be honest about kinetics at the mesoscale, and only then claim mechanisms for membranes and proteins in ways that can be checked against data.
Here are the main things I took away:
Continue readingNanobodies® galore in Utrecht
At the end of September, I had the opportunity to present at the 4th Single-Domain Antibody (sdAb/VHH) Conference hosted in the city of Utrecht. The sdAb conference is a biennial event, and was held for the first time in Bonn (2019), then in Brussels (2021) and Paris (2023), before coming to the Netherlands this year.
This was the first time I’d attended a VHH-focused conference, and I was taken aback at just how large the community is; the Jaarbeurs ‘Supernova’ event hall was completely sold out, with over 400 researchers in attendance (pictures below courtesy of the organisers). The buzz reflects the ever growing interest in sdAbs as tools to discover new fundamental biology, vectors for diagnosing disease, and as prophylactic or curative therapeutics. Most every disease indication was represented at the conference, from anticancer and antiviral sdAbs to antivenom sdAbs (both for use in lateral flow tests to diagnose the snake that bit you, and as quick ‘epipen’-like therapeutics accessible even in the most remote parts of the world).
Continue readingAntibody developability datasets
Next to binding the antigen with high affinity, antibodies for therapeutic purposes need to be developable. These developability properties includes high expression, high stability, low aggregation, low immunogenicity, and low non-specificity [1]. These properties are often linked and therefore optimising for one property might be at the expense of another. Machine learning methods have been build to guide the optimistation process of one or multiple developability properties.
Performance of these methods is often limited by the amount and type of data available for training. These dataset contain experimental determined scores of biophysical assays related to developability. Some common experimental assays are described in a previous blog post by Matthew Raybould [2]. Here I will discuss some (commonly) used and new dataset related to antibody developability. This list is not exhaustive but might help you start understanding more about antibody developability.
Continue readingA Masterclass in Basic & Translational Immunology with Prof. Abul Abbas
On Thursday 17th April, a group of us made the journey ‘up the hill’ to the Richard Doll building to attend an immunology masterclass from Professor Abul Abbas. Prof. Abbas is an emeritus professor in Pathology at UCSF and author of numerous core textbooks including Basic Immunology: Functions and Disorders of the Immune System.
The whole-day course consisted of a series of lectures covering core topics in immunology, from innate immunity and antigen presentation through to B/T cell subsets, autoimmunity, and immunotherapy.
Continue readingTherapeutic antibodies and their function
Last week during a poster session in the Department of Statistics, I had an interesting discussing with Martin Buttenschoen (working on the other side of the group) regarding the difference between small molecules and antibodies as therapeutics. This discussion made me realise that even though I’m working on antibodies engineering and developability, I could use a little refresher on approved therapeutic antibodies and their mechanisms of action.
In case you also need this bigger picture, or want to get excited about therapeutic antibodies yourself, I will summarise the target, the development process, the molecular function, and the administration for three successful therapeutic antibodies.
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