Atom14 and Atom37: how protein design models write down atoms

AKA why a protein does not naturally fit neatly into a tensor, and how RFdiffusion3, BoltzGen and La-Proteina cope when the amino-acid sequence is not known yet

The problem: proteins are not rectangles

Neural networks like fixed-shape arrays. Proteins are not fixed-shape: the 20 standard amino acids contain between 4 and 14 heavy atoms (hydrogens are usually ignored). Atom14 and Atom37 are two ways of squeezing that variety into a rectangle. For a protein with L residues, both give a coordinate tensor of shape [L, A, 3]: one row per residue, A atom slots, and three Cartesian coordinates per slot. Atom14 uses A = 14 and Atom37 uses A = 37. These are maximum widths, not claims that every residue has that many atoms, so each tensor travels with a mask that marks which slots hold a real atom. [1, 2]

This post first explains the two layouts, then asks the question that matters for generative models: what happens in de novo design, where the sequence, and therefore the list of atoms, is not known in advance? We look at three recent answers: RFdiffusion3, BoltzGen and La-Proteina.

Same backbone, different side chains

Every amino acid shares a backbone of four heavy atoms: N, CA (the alpha carbon), C and O. The side chain starts at CB, the beta carbon, and is what makes each residue type different. Glycine has no side chain at all, serine and cysteine add two atoms, arginine adds seven, and tryptophan adds ten. Any representation must hold the shared backbone and a variable-length side chain at once. [1]

Figure 1. Five residues drawn atom by atom, using the AlphaFold atom names. Backbone atoms have a thick outline. Bonds are schematic and hydrogens are omitted.
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Lessons in Bemistry: identifying the signature of a medicinal chemist

Andrew D. Blevins and Ian K. Quigley, co-founders of Leash Bio, released a pre-print last year that I think deserves some more attention. Using a Bemis-Murcko scaffold split, the authors show that it is possible to train a machine learning model to identify which of 1,815 medicinal chemists produced a molecule from its structure alone, achieving a top 5 accuracy of 60% and a top 1 of 27%.

Let’s take a step back. Why aim to predict who made a molecule in the first place? The authors motivate this well, drawing on the story of Clever Hans. Owned and showcased by Wilhelm von Osten in 20th century Germany, Clever Hans was a horse purportedly able to solve mathematical problems, stamping out solutions with his hoof. However, it was quickly realized that Clever Hans could not actually perform arithmetic, but rather read the micro-expressions and posture of those posing the question, stopping his count when the faces of observers revealed the right answer had been reached. The moral? Clever Hans learned how to predict a correlate of a task, which was mistaken for mastery of the task itself.

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Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler

For the final project of my PhD in OPIG, I developed the Therapeutic Nanobody Profiler, or ‘TNP’: a tool to characterise nanobody developability analogous to TAP (the Therapeutic Antibody Profiler). TNP uses 6 metrics, tailored to the unique features of nanobodies, to benchmark the structural and physicochemical properties of a candidate nanobody against 36 clinical-stage examples. In collaboration with Twist Bioscience, we also collected experimental developability assay data for 108 nanobodies expressed as IgG constructs, and examined how these results are related to the TNP guidelines.

More info can be found in the paper published in Communications Biology at: https://www.nature.com/articles/s42003-026-09594-y

The TNP software and datasets are open-source and can be found at: https://opig.stats.ox.ac.uk/webapps/tnp (for the web-app) and https://github.com/oxpig/TNP (for the GitHub repo).

OAS-nano: New Repertoire Database Just Dropped

Those familiar with the immunoinformatics work of our group will know of our Observed Antibody Space (OAS) and Observed T-cell Receptor Space (OTS) databases, which compile and consistently process V(D)J transcriptomics datasets from across a range of studies on natural immune repertoires. These have provided a central community resource of training data for adaptive immune receptor (AIR) large language models, as well as references for studies aiming to identify clones related to particular disease states.

We are now delighted to release the final installment of our natural AIR database series: introducing OAS-nano. This project was enabled by key collaborative contributions from Klara Kropivsek Brumat and Ario de Marco at the University of Nova Gorica and our industrial partners at AstraZeneca: Olga Obrezanova, Erwin de Genst, and Rebecca Croasdale-Wood.

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Le Tour De Farce v13.0

Well by righteous democracy I’m once again given the privilege of inscribing Le Tour De Farce into the annals of history, this year being the 13th consecutive year of OPIG’s summer social cycling pub crawl extravagansa. I will try to recount as much as I can from pieces of other peoples memories (my own is a less reliable source). Here is a reasonable portion of the group standing with steeds at the back of the Stats department as is tradition (Figure 1).

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The First OpenBind Blind Challenge

At OpenBind, we’re working to generate large-scale, open experimental datasets of protein-ligand structures and binding measurements to support the development and evaluation of computational methods for drug discovery. One thing I’m particularly excited about is using these data not just for retrospective benchmarking, but for prospective, blind evaluation.

So, I’m very pleased that we’re launching the first OpenBind blind challenge on 14 October 2026, in partnership with OpenADMET and the ASAP Discovery Consortium. The interim submission deadline is 11 November, with final submissions due by 16 December 2026 at 23:59 UTC.

The challenge asks participants to predict protein-ligand complexes of 400 compounds bound to Zika virus NS2B-NS3 protease, ranging from small fragments to larger, lead-like molecules.

A key challenge will be using the structural information that already exists to accurately predict the binding modes of new, unseen ligands. There are already a number of publicly available structures of Zika NS2B-NS3 protease in the PDB that participants can use.

We’re also setting a high bar for success. While many protein-ligand benchmarks use an RMSD threshold of 2 Å, we’re tightening this to 1.5 Å, along with additional checks on interaction accuracy (lDDT-PLI > 0.8) and physical validity (PoseBusters validity checks). I’m particularly excited to see how docking, cofolding, and other approaches compare!

If you work on protein-ligand structure prediction, I’d strongly encourage you to join the challenge. You can find all the details, including how to register and join the challenge Discord, in the OpenBind announcement.

Update: We’re now live! Check out the challenge on Hugging Face.

AI can remove typing. It cannot remove thinking.​

Students new to programming may wonder why they even need to learn to code any more. They can ask the reasonable question: “Why can’t we just use AI for all our programming?” It’s a fair question. The Genie is out of the bottle. We aren’t putting it back. It absolutely does have its uses. But it also can’t be trusted.

For a new programmer, the important skill is no longer remembering syntax, nor is it learning how to make an AI produce code. It’s learning how to solve problems in a systematic way that they understand well enough to trust.

When getting started with AI, use it to help think through the problem, break the work into manageable pieces, elaborate how the pieces interact and only then start to code.

There’s the old adage “Garbage in, garbage out”. If you aren’t able to articulate your problem in an AI-friendly way, we’re well into garbage out territory. The other part of the problem is whilst AI will certainly generate you some code, if you don’t understand what it’s giving back, you can’t trust it to be correct. If you carry on just accepting what it’s saying, it’s like going on a date in a foreign country and putting all your faith in your translation app. Sooner or later you’re unwittingly going to say something that will earn you a slap.

So, instead of just telling it what you want, try:

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Finally solving drug discovery with a Fly

As I was sitting down this morning, trying to enjoy my rather excellent breakfast, I was suddenly attacked by a fly. Unfortunately for me, my lazy hand swats proved no match for the nimble little thing, who was evidently pretty hungry too. And as I watched the acrobatics this tiny creature could effortlessly pull off in pursuit of my sandwich, I was left wondering: if only we could put these skills to better use.

Open-sourcing a fly

Luckily, I’m not the only one who has been staring at a fly and wondering. A couple of weeks ago, HHMI Janelia, the University of Cambridge and Google Research published a paper in the Cell describing the complete wiring diagram of a male fruit fly’s central nervous system: the brain and the nerve cord, the whole thing, down to individual synapses. To be fair, this isn’t the first time someone has done this. The FlyWire project gave us a complete female fly brain back in 2024, and Janelia had already released half a female brain in 2020. But this one comes with the body wiring attached, and, for whatever reason, this is the one the internet decided to notice.

What the internet did with it

Now, the video games. My question at the top was whether these skills could be put to better use, so here they are:

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One Year Down: Wisdom from a Former OPIG Newbie

With a new cohort of PhD students arriving imminently and my first year coming to a close, I am about to achieve an important milestone: I will no longer be the newest full-time OPIGlet in the group!

Naturally, this makes me extremely qualified to impart wisdom.

In all seriousness, the first year of a PhD has been a strange and wonderful adjustment. I have learned a lot about nanobodies, of course, but I have also learned quite a bit about how to actually do a PhD. So, for the incoming OPIGlets—or anyone else embarking on their first year—here are a few things I wish I had fully appreciated when I started.

Give some structure to all that freedom

One of the strangest things about starting a PhD is suddenly having an almost completely self-guided schedule. It is not something most of us have had to deal with before. Undergrad comes with lectures, tutorials, deadlines, and exams. Most jobs come with meetings, working hours, and someone telling you what needs to be done.

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