Category Archives: Antibodies

New 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.

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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:

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Dispatches from Lisbon

Tiles, tiles, as far as the eye can see. Conquerors on horseback storming into the breach; proud merchant ships cresting ocean waves; pious monks and shepherds tending to their flocks; Christ bearing the cross to Calvary—in intricate tones of blue and white on tin-glazed ceramic tilework. Vedi Napoli e poi muori the Sage of Weimar once wrote—to see Naples and die. But had he been to Lisbon?

The azulejos of the city’s numerous magnificent monasteries are far from the only thing for the weary PhD student to admire. Lisbon has no shortage of imposing bridges and striking towers, historically fraught monuments and charming art galleries. Crumbling old castles and revitalised industrial quarters butt up against the Airbnbs-and-expats district, somewhere between property speculation and the sea. An endearing flock of magellanic penguins paddles away an afternoon in their enclosure at the local aquarium (which is excellent), and an alarming proliferation of custard-based pastries invites one to indulge.

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Nanobodies® 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).

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Handling OAS Scale Datasets Without The Drama

Working with Observed Antibody Space (OAS) dataset sometimes feels a bit like trying to cook dinner with the contents of the whole fridge emptied into the pan. There are countless CSVs, all of different sizes (some might not even fit onto your RAM), and you just want a clean, fast pipeline so you can get back to modelling. The trick is to stop treating the data like a giant spreadsheet you fully load into memory and start treating it like a columnar, on-disk database you stream through. That’s exactly what the 🤗 Datasets library gives you.

At the heart of 🤗 Datasets is Apache Arrow, which stores columns in a memory-mapped format (if you are curious about what that means there is a great explanation in another blog post here. In plain terms: the data mostly lives on disk, and you pull in just the slices you need. It feels interactive even when the dataset is huge. Instead of a single monolithic script that does everything (and takes forever), you layer small, composable steps—standardize a few columns, filter out junk, compute a couple of derived fields—and each step is cached automatically. Change one piece, and only that piece recomputes. Sounds great, right? But of course, the key question now is how to get OAS data into Datasets to begin with.

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Antibody 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.

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A 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.

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Therapeutic 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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The “AI-ntibody” Competition: benchmarking in silico antibody screening/design

We recently contributed to a communication in Nature Biotechnology detailing an upcoming competition coordinated by Specifica to evaluate the relative performance of in vitro display and in silico methods at identifying target-specific antibody binders and performing downstream antibody candidate optimisation.

Following in the footsteps of tournaments such as the Critical Assessment of Structure Prediction (CASP), which have led to substantial breakthroughs in computational methods for biomolecular structure prediction, the AI-ntibody initiative seeks to establish a periodic benchmarking exercise for in silico antibody discovery/design methods. It should help to identify the most significant breakthroughs in the space and orient future methods’ development.

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