Author Archives: Patrick Brennan

The Boltzmann Distribution and Gender Stereotypes

Journalist Caitlin Moran recently tweeted the following:

“I feel like every day now, I read/hear something saying “We don’t talk about what’s POSITIVE about masculinity; what’s GOOD about men and boys.” So: what IS the best stuff about boys, and men? Honest, celebratory question.”

What followed was a collection of replies acknowledging and celebrating various traits seen typically as ‘male’, including certain activities, such as knowing about sports or cars, or a desire to do DIY type work, and characteristics such as physical strength, no-nonsense attitudes and a ‘less complicated’ style of friendship between men.

Whilst I condone Moran’s efforts to turn recent discussions surrounding masculinity on their head and frame it in a positive light, to me the the responses offered and discussion that followed felt somewhat stifling. I am biologically male and identify as male, but do not feel like I personally adhere to most of these stereotypes. I am not physically strong, I know very little about cars and sports, and find there be just as much nuance and drama in male-male friendships as there is in friendships between other genders. 

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Some Musings on AI in Art, Music and Protein Design

When I started my PhD in late 2018, AI hadn’t really entered the field of de novo protein design yet – at least not in a big way. Rosetta’s approach of continually ranking new side chain rotamers on a fixed backbone was still the gold standard for the ‘structure-to-sequence’ problem. And of course before long we had AI making waves in the structure prediction field, eventually culminating in the AlphaFold2 we all know and love. 

Now, towards the end of my PhD, we are seeing the emergence of new generative models that learn from existing pdb structures to produce sequences that will (or at least should) fold into viable, sensible and crucially natural-looking shapes. ProtGPT2 is a good example (https://www.nature.com/articles/s41467-022-32007-7), but there are several more. How long before these models start reliably generating not only shapes but functions too? Jury’s out, but it’s looking more and more feasible. Safe to say the field as a whole has evolved massively during my time as a graduate student.

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A quantitative way to measure targeted protein degradation

Whenever we order consumables in the Chemistry department, the whole lab gets an email notification once they arrive. So I can understand why I got some puzzled reactions from my colleagues when one such email arrived saying that my ‘artichoke’ was ready to collect from stores. Had I been sneakily doing my grocery shopping on a university research budget?

Artichoke is, in fact, the name of a plasmid designed by the Ebert lab (https://www.addgene.org/73320/), which I have been using in some of my research on targeted protein degradation. The premise is simple enough: genes for two different fluorescent proteins, one of which is fused to a protein-of-interest.

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Targeted protein degradation phenotypic studies using HaloTag CRISPR/Cas9 endogenous target tagging and HaloPROTAC

Biologists currently have several options in their arsenal when it comes to gene silencing. if you want to completely vanquish the gene in question, you can use CRISPR to knock the gene out completely. This is a great way to completely eliminate the gene, and hence compare cell phenotypes with and without the gene, but it’s less good if the gene is essential and the cells won’t grow without it in the first place. 

Otherwise you can use RNA interference, where small pieces of RNA that complement the mRNA for that gene are introduced to the cell, with the overall effect of blocking transcription of that gene’s mRNA, hence silencing it. However, this method suffers from side effects and varying levels of gene knockdown efficiency. Moreover, it does not vanquish existing protein, it just stops more from being produced.

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Safety and sexism: the heroic stubbornness of Frances Oldham Kelsey

With covid-19 vaccine rollouts well underway the world over, the subject of clinical trials has been a focal point of discussion lately. Of course clinical trials are applicable to every drug, not just vaccines, and the class of molecules on which my own work focuses includes perhaps one of the most famous case studies of why clinical trials are necessary: thalidomide.

The teratogenic effects in unborn infants of this seemingly innocuous small molecule are well documented and infamous. But at the time of its initial use a treatment for morning sickness in the mid twentieth century, little was known about its mechanism of action. Only within the last 20 years has the molecular glue-type nature of thalidomide and its analogues (collectively known as immunomodulatory imide drugs, or IMIDs) become apparent. Armed with this knowledge, we know not only understand how thalidomide works in useful situations (such as curing cancer), but also how it exhibits its less desirable effects (recruiting SALL4 to the E3 ligase cereblon, leading to SALL4’s degradation and subsequent embryogenesis havoc).

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PyMOL: colour by residue

PyMOL is a handy free way of viewing three dimensional protein structures. It allows you to toggle between different representations of the protein – such as cartoon, surface, sticks, etc. – which all have their own pros and cons.

However one thing I felt that PyMOL lacked was an easy way to visually distinguish residues based on type. Whist you can easily differentiate atom types based on colour in the colour menu, and even choose which colour you wish carbons to show up as whilst keeping heteroatoms different colours, this assigned carbon colour would be constant throughout the entire protein.

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Constrained docking for bump and hole methodology

Selectivity is an important trait to consider when designing small molecule probes for chemical biology. If you wish to use a small molecule to study a particular protein, but that small molecule is fairly promiscuous in its binding habits, there are risks that any effects you observe may be due to it binding other proteins with similarly shaped binding pockets, instead of your protein of interest.

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