Various coarse-grained (CG) models have become increasingly common in studies of antibody-antibody interactions in solution. These models appear poised to enter development pipelines in the near future to help predict and understand how antibody-antibody interactions influence the suitability of a given monoclonal antibody (mAb) for mass production and delivery as an antibody therapy. This blog post is a non-exhaustive summary of some of the highlights I found during a recent literature search.
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Musings on Digital Nomaddery from Seoul
The languorous, muggy heat of the Korean afternoon sun was what greeted me after 13 hour cattle-class flight from a cool, sensible Helsinki night. The goings-on in Ukraine, and associated political turmoil, meant taking the scenic route – avoiding Russia and instead passing over Turkey, Kazakstan and Mongolia – with legs contorted into unnatural positions and sleep an unattainable dream. Tired and disoriented, I relied less on Anna’s expert knowledge of the Korean language than her patience for my jet-lag-induced bad mood and brain fog. We waited an hour for a bus to take us from Incheon airport to Yongsan central station in the heart of the capital. It was 35 °C.
I’ve been here for a month. Anna has found work, starting in November; I have found the need to modify my working habits. Gone are the comfortable, temperate offices on St Giles’, replaced by an ever-changing diorama of cafés, hotel rooms and libraries. Lugging around my enormous HP Pavilion, known affectionately by some as ‘The Dominator’, proved to be unsustainable.
It’s thesis-writing time for me, so any programming I do is just tinkering and tweaking and fixing the litany of bugs that Lucy Vost has so diligently exposed. I had planned to run Ubuntu on Parallels using my MacBook Air; I discovered to my dismay that a multitude of Conda packages, including PyTorch, are not supported on Apple’s M1 chip. It has been replaced by a combination of Anna’s old Intel MacBook Pro and rewriting my codebase to install and run without a GPU – adversity is the great innovator, as the saying goes.
Continue readingTidbits from YouGov Polls
Some recent verdicts from the British public on YouGov polls
- The Queen (97%) is less well known than her husband Prince Philip (98%)
- Liz Truss’ UK popularity rating (21%) is lower than George W. Bush’s (22%)
- The most popular British dish is ‘Chips’ (84%) followed by ‘Fish and Chips’ (83%)
- Oxford (55%) is a less popular university than Cambridge (58%)
So much for Aristotle’s ‘wisdom of crowds’!
Tackling horizontal and vertical limitations
A blog post about reviewing papers and preparing papers for publication.
We start with the following premise: all papers have limitations. There is not a single paper without limitations. A method may not be generally applicable, a result may not be completely justified by the data or a theory may make restrictive assumptions. To cover all limitations would make a paper infinitely long, so we must stop somewhere.
A lot of limitations fall into the following scenario. The results or methods are presented but they could have extended them in some way. Suppose, we obtain results on a particular cell type using an immortalized cell-line. Are the results still true, if we performed the experiments on primary or patient-derived cells? If the signal from the original cells was sufficiently robust then we would hope so. However, we can not be one hundred percent sure. A similar example is a method that can be applied to a certain type of data. It may be possible to extend the method to be applied to other data types. However, this may require some new methodology. I call this flavor of limitations vertical limitations. They are vertical in the sense that they build upon an already developed result in the manuscript. For certain journals, they will require that you tackle vertical limitations by adapting the original idea or method to demonstrate broad appeal or that idea could permeate multiple fields. Most of the time, however, the premise of an approach is not to keep extending it. It works. Leave it alone. Do not ask for more. An idea done well does not need more.
Continue readingSharing Data Responsibly: The FAIR Principles
So you’ve submitted your paper, made your code publicly available, and maybe even provided documentation to ensure somebody can reproduce your work. But what about the data your work is based on? Is that readily available to your readers, too?
Maybe it’s too large to put on GitHub alongside your code. Maybe it’s sensitive, or subject to GDPR restrictions, so you can’t just stick a download link on your website. Maybe it’s in a proprietary format that needs non-open software to read. There are many reasons sharing data can be less straightforward than sharing code, and often it’s not entirely clear what ‘best practices’ are for a given situation. Data management is a complicated topic, and to do it justice would require far more than a quick blog post. Instead, I’d like to focus on a single source of guidance that serves as a useful starting point for thinking about responsible data management: the FAIR principles.
Continue readingCryoEM is now the dominant technique for solving antibody structures
Last year, the Structural Antibody Database (SAbDab) listed a record-breaking 894 new antibody structures, driven in no small part by the continued efforts of the researchers to understand SARS-CoV-2.
In this blog post I wanted to highlight the major driving force behind this curve – the huge increase in cryo electron microscopy (cryoEM) data – and the implications of this for the field of structure-based antibody informatics.
Continue readingMake your code do more, with less
When you wrangle data for a living, you start to wonder why everything takes so darn long. Through five years of introspection, I have come to conclude that two simple factors limit every computational project. One is, of course, your personal productivity. Your time of focused work, minus distractions (and yes, meetings figure here), times your energy and mental acuity. All those things you have little control over, unfortunately. But the second is the productivity of your code and tools. And this, in principle, is a variable that you have full control over.
This is a post about how to increase your productivity, by helping you navigate all those instances when the progress bar does not seem to go fast enough. I want to discuss actionable tools to make your code run faster, and generate more results, with less effort, in less time. Instructions to tinker less and think more, so you can do the science that you truly want to be doing. And, above all, I want to give out advice that is so counter-intuitive that you should absolutely consider following it.
Continue readingFrom code to molecules: The future of chemical synthesis
In June, after I finish my PhD, I will be joining Chemify, a new startup based in Glasgow that aims to make chemical synthesis universally accessible, reproducible and fully automated using AI and robotics. After previously talking about “Why you should care about startups as a researcher” and a quick guide on “Commercialising your research: Where to start?” on this blog, I have now joined a science-based startup fresh out of university myself.
Chemify is a spinout from the University of Glasgow originating from the group of Prof. Lee Cronin. The core of the technology is the chemical programming language χDL (pronounced “chi DL”) that, in combination with a natural language processing AI that reads and understands chemical synthesis procedures, can be used to plan and autonomously executed chemical reactions on robotic hardware. The Cronin group has also already build the modular robotic hardware needed to carry out almost any chemical reaction, the “Chemputer”. Due to the flexibility of both the Chemputer and the χDL language, Chemify has already shown that the applications go way beyond simple synthesis and can be applied to drug formulation, the discovery of new materials or the optimisation of reaction conditions.
Armed with this transformational software and hardware, Chemify is now fully operational and is hiring exceptional talent into their labs in Glasgow. I am excited to see how smart, AI-driven automation techniques like Chemify will change how small scale chemical synthesis and chemical discovery more broadly is done in the future. I’m super excited to be part of the journey.
Feeding a drove of hungry OPIGlets
In preparing for battle I have always found that plans are useless, but planning is indispensable.
Dwight D. Eisenhower
Following the previous post about OPIG retreat 2022, and having received numerous requests for recipes, I thought I’d document the process of ensuring that 24 people are kept fed and happy. Recipes at the foot of the post.
Disclaimer – these recipes are entirely my own interpretations, adapted where necessary to suit a range of dietary requirements. They are in no way authentic to any national cuisines and are not intended to be.
Disclaimer II: The Disclaiming – all measurements are approximate. I rarely write down recipes or use precise measurements. Taste as you go, and don’t be afraid to add more salt.
Continue readingWomen in Computing: past, present and what we can do to improve the future.
Computing is one of the only scientific fields which was once female-dominated. In the 30s and 40s, women made up the bulk of the workforce doing complex, tedious calculations in the fields including ballistics, astrophysics, aeronautics (think Hidden Figures) and code-breaking. Engineers themselves found that the female computers were far more reliable than themselves in doing such calculations [9]. As computing machines became available, there was no precedent set for the gender of a computer operator, and so the women previously doing the computing became the computer operators [10].
However, this was not to last. As computing became commercialised in the 50s, the skill required for computing work was starting to be recognised. As written in [1]:
“Software company System Development Corp. (SDC) contracted psychologists William Cannon and Dallis Perry to create an aptitude assessment for optimal programmers. Cannon and Perry interviewed 1,400 engineers — 1,200 of them men — and developed a “vocational interest scale,” a personality profile to predict the best potential programmers. Unsurprisingly given their male-dominated test group, Cannon and Perry’s assessment disproportionately identified men as the ideal candidates for engineering jobs. In particular, the test tended to eliminate extroverts and people who have empathy for others. Cannon and Perry’s paper concluded that typical programmers “don’t like people,” forming today’s now pervasive stereotype of a nerdy, anti-social coder.”
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