I recently had the opportunity to present my work on antibody virtual screening at the 2021 ISMB/ECCB virtual conference. In this blogpost, I want to summarise two research projects presented in the 3DSIG immunoinformatics session (in which I also presented my work) highlighting two different avenues of approaching epitope prediction (and immunoinformatics questions in general): Structure-based (Epitope3D) and sequence-based (SeRenDIP-CE).
Continue readingUniformly sampled 3D rotation matrices
It’s not as simple as you’d think.
If you want to skip the small talk, the code is at the bottom. Sampling 2D rotations uniformly is simple: rotate by an angle from the uniform distribution . Extending this idea to 3D rotations, we could sample each of the three Euler angles from the same uniform distribution
. This, however, gives more probability density to transformations which are clustered towards the poles:
In Fast Random Rotation Matrices (James Avro, 1992), a method for uniform random 3D rotation matrices is outlined, the main steps being:
Continue readingMaking your python tool as easy to install as possible
Have you ever tried to use someone else’s code and spent a whole day trying to install it? Have you ever decided not to use a tool because installing it was a massive pain? Both of those have happened to me and, to be honest, it is a massive shame. The authors may spend large amounts of time developing these tools and in the end, no one uses them because they can’t get them to work. So I have decided to try and make all code I develop as easy and painless as possible to install and use.
Continue readingLinux Horror Stories and Protection Spells (Volume I)
Don’t get me wrong. I love Linux. After many years of using it, I ended up appreciating how flexible, potent, and even beautiful it is. However, using Linux has never been a bed of roses and every single Linux user that I know has had to deal with many problems since the very beginning. Indeed, I still remember how frustrating installing my first Linux machine was, especially after realizing that my network card was not working. Had I given up, I would never have written this post.
Although many of the problems that I faced while using Linux are related to updates and drivers (how painful NVidia drivers updates can be, I will write another post about that in the future), I must recognize that on many other occasions I was the only one responsible for such problems. Consequently, I want to warn the reader against a couple of those mistakes I made in the past and provide some tips about how to deal with them.
My worst nightmare: rm –r *
AlphaFold 2 is here: what’s behind the structure prediction miracle
Nature has now released that AlphaFold 2 paper, after eight long months of waiting. The main text reports more or less what we have known for nearly a year, with some added tidbits, although it is accompanied by a painstaking description of the architecture in the supplementary information. Perhaps more importantly, the authors have released the entirety of the code, including all details to run the pipeline, on Github. And there is no small print this time: you can run inference on any protein (I’ve checked!).
Have you not heard the news? Let me refresh your memory. In November 2020, a team of AI scientists from Google DeepMind indisputably won the 14th Critical Assessment of Structural Prediction competition, a biennial blind test where computational biologists try to predict the structure of several proteins whose structure has been determined experimentally but not publicly released. Their results were so astounding, and the problem so central to biology, that it took the entire world by surprise and left an entire discipline, computational biology, wondering what had just happened.
Continue readingOPIGlets go Kayaking
The 1st of July was the day that the OPIGlets went kayaking!
Brennan very kindly offered to guide a kayaking session from the Oxford University Canoeing and Kayaking Club (OUCKC). There was great uptake from the group, with 10 members joining for a paddle.
The first task was to find a kayak long enough for Jack’s legs. Once he managed to wedge himself in to the largest kayak available, we moved onto being pushed down the ramp one by one, hoping that this would not lead to an immediate capsize.
Continue readingA to Z of Alternative Antibody Formats: Next-Generation Therapeutics
Do you know your diabodies from your zybodies?
Antibodies are a highly important class of therapeutics used to treat a range of diseases. Given their success as therapeutics, a wide variety of alternative antibody formats have been developed – these are driving the next generation of antibody therapeutics.
To note, this is not an exhaustive list but rather intended to demonstrate the range of existing antibody formats.
Inspired by this article in The Guardian: “Rachel Roddy’s A-Z of pasta“

Many of these figures were adapted from Spiess et al., 2015. Additionally, some of these formats have multiple variations or further possible forms (e.g., trispecific antibodies) – in these cases, one example is given here.

A – Antibodies
Antibodies – a fitting place to start this post. Antibodies are proteins produced by our immune systems to detect and protect against foreign pathogens. The ability of antibodies to bind molecules strongly and specifically – properties essential to their role in our immune defence – also make them valuable candidates for therapeutics. Antibody therapies have been developed for the treatment of various diseases, including cancers and viruses, and form a market estimated at over $100 billion1.
Continue readingOne of my other hats – Covid-19 Response Director for UK research and innovation
The group asked me if I would tell them a little bit about one of my other hats at our regular Tuesday meeting, and this blog is about that.
In October 2019 I was seconded part-time to UKRI as the Deputy Executive Chair of the Engineering and Physical Sciences Research council (EPSRC). What is UKRI (UK research and Innovation)? It’s a non-departmental public body that funds research and innovation. It is made up of the seven disciplinary research councils (acronyms to please Tom – AHRC, BBSRC, EPSRC, ESRC, NERC, STFC and MRC), Research England, and the UK’s innovation agency, Innovate UK.
As Deputy Executive Chair of EPSRC I was helping with UKRI strategy, learning how a spending review round works, visiting universities to talk about how they could work better with UKRI – pretty much everything I was expecting to be doing. But like everyone, my world changed in early 2020.
Continue readingHow fast can a protein fold?
A protein’s folding time is the time required for it to reach its unique folded state starting from its unfolded ensemble. Globular, cytosolic proteins can only attain their intended biological function once they have folded. This means that protein folding times, which typically exceed the timescales of enzymatic reactions that proteins carry out by several orders of magnitude, are critical to determining when proteins become functional. Many scientists have worked tirelessly over the years to measure protein folding times, determine their theoretical bounds, and understand how they fit into biology. Here, I focus on one of the more interesting questions to fall out of this field over the years: how fast can a protein fold? Note that this is a very different question than asking “how fast do proteins fold?”
Continue readingOut-of-distribution generalisation and scaffold splitting in molecular property prediction
The ability to successfully apply previously acquired knowledge to novel and unfamiliar situations is one of the main hallmarks of successful learning and general intelligence. This capability to effectively generalise is amongst the most desirable properties a prediction model (or a mind, for that matter) can have.
In supervised machine learning, the standard way to evaluate the generalisation power of a prediction model for a given task is to randomly split the whole available data set into two sets – a training set
and a test set
. The model is then subsequently trained on the examples in the training set
and afterwards its prediction abilities are measured on the untouched examples in the test set
via a suitable performance metric.
Since in this scenario the model has never seen any of the examples in during training, its performance on
must be indicative of its performance on novel data
which it will encounter in the future. Right?

