Author Archives: Lucy Vost

ISMB/ECCB conference feedback 

The ISMB/ECCB conference took place in Liverpool this year. So, a couple of OPIGlets took the train up north to attend this biyearly joint conference. Here we will give some general feedback on the conference and highlight some interesting talks/posters. 

General feedback 

ISMB/ECCB is a 4.5 day conference starting on the Sunday evening and running until Thursday evening. The conference is attended by around 2500 people, mostly from academic groups around the world. With more than 20 different tracks, it is a broad conference with lots of tracks happening at the same time. As always, it is thus recommended to have a look at the schedule beforehand to not get too overwhelmed. Each day there is one keynote, two poster sessions, and three blocks of talks. These talks are often given by PIs, but also PostDocs and PhD students get the opportunity to present. There are also some smaller slots for highlighting posters which are presented that day. 

This year there was a very interesting line-up of Distinguished Keynote speakers. The conference was kicked off by John Jumper talking about AlphaFold2, with a focus on how the team went about the various problems during the process of going from the initial AlphaFold model to AlphaFold2. On Monday Prof. Amos Bairoch talked about biocuration and importance and challenges of public databases. He discussed the FAIR principles for Findable, Accessible, Interoperable, and Reusable for data management [1]. The next Keynote was by Prof. James Zou about computational biology in the age of AI agents (later more). On Wednesday we had our own Prof. Charlotte Deane (woo!) talking about structure-based drug discovery with a focus on the importance of baselines and benchmarking. The conference was ended by a short interview with Prof. David Baker, followed by a talk from Prof. Fabian Theis on decoding cellular systems. He discussed Cellflow [2], an AI tool that predicts how perturbations like drugs effect the cellular phenotype. 

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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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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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Roche Continents 2024

This July I had the opportunity to be part of the Roche Continents programme [1]. The programme was organised by Roche and LUMA Arles and took place in the beautiful city of Arles in the south of France. Together with 40 students from various disciplines and European universities we discussed and explored the connection between arts, science, and sustainability. The theme of the week was resourcefulness.  

For students considering applying to Roche Continents next year, I’d like to offer some insights on what to expect, as well as share a few of my personal highlights from the experience. 

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Environmentally sustainable computing 

Did you know that it is approximated that you, a scientist, have a carbon footprint which is between 2 and 12 times higher than the set carbon budget per person to keep global warming below 1.5 °C [1]? 

Background

Global temperatures are rising. This has direct effects on the planet and contributes to increasing humanitarian emergencies. These include more frequent and intense heatwaves, wildfires, and floods [2]. The impact of climate change is already severe, with around 20 million internal displaced persons in 2023 alone due to those disasters [3]. 

Global warming and climate change are caused by the emissions of carbon dioxide and methane, known as carbon emissions. There are different ways in which you could minimise your carbon footprint. For example, I try to reduce the energy usage in the house, try eating mainly plant-based, and travel by train instead of by plane to family and for holidays and conferences. However, up until organising a Green Lecture with the Department of Statistics Green Team I never thought of my computational PhD as a major contributor to my carbon footprint. That doesn’t mean the work I, and all other scientists, do is not important and necessary. But the lecture on principles for environmentally sustainable research given by Loic Lannelongue made me aware of carbon costs of computing, which I would like to share with you. 

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The Antibody Dictionary

Similar to getting lost in a language when moving country, you might encounter a language barrier when moving research fields. This dictionary will guide you in the complex world of immunoinformatics, with a focus on antibodies. Whether your main research will be in this field, you want to apply your machine learning model on antibodies, or you just want to understand the research performed in OPIG, this dictionary will get you started.

The Antibody Dictionary:

Affinity maturation: The optimisation process of naive antibodies to memory antibodies such that the antibody is optimised for a specific antigen. 

Antibody: (immunoglobulin) a Y-shaped molecule important in the adaptive immune system. A canonical antibody consists of two identical heavy chains and two identical smaller light chains. 

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