Category Archives: Proteins

OpenMM Setup: Start Simulating Proteins in 5 Minutes

Molecular dynamics (MD) simulations are a good way to explore the dynamical behaviour of a protein you might be interested in. One common problem is that they often have a relatively steep learning curve when using most MD engines.

What if you just want to run a simple, one-off simulation with no fancy enhanced sampling methods? OpenMM Setup is a useful tool for exactly this. It is built on the open-source OpenMM engine and provides an easy to install (via conda) GUI that can have you running a simulation in less than 5 minutes. Of course, running a simulation requires careful setting of parameters and being familiar with best practices and while this is beyond the scope of this post, there are many guides out there that can easily be found. Now on to the good stuff: using OpenMM Setup!

When you first run OpenMM Setup, you’ll be greeted by a browser window asking you to choose a structure to use. This can be a crystal structure or a model. Remember, sometimes these will have problems that need fixing like missing density or charged, non-physiological termini that would lead to artefacts, so visual inspection of the input is key! You can then choose the force field and water model you want to use, and tell OpenMM to do some cleaning up of the structure. Here I am running the simulation on hen egg-white lysozyme:

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Fragment Based Drug Discovery with Crystallographic Fragment Screening at XChem and Beyond

Disclaimer: I’m a current PhD student working on PanDDA 2 for Frank von Delft and Charlotte Deane, and sponsored by Global Phasing, and some of this is my opinion – if it isn’t obvious in one of the references I probably said it so take it with a pinch of salt

Fragment Based Drug Discovery

Principle

Fragment based drugs discovery (FBDD) is a technique for finding lead compounds for medicinal chemistry. In FBDD a protein target of interest is identified for inhibition and a small library, typically of a few hundred compounds, is screened against it. Though these typically bind weakly, they can be used as a starting point for chemical elaboration towards something more lead-like. This approach is primarily contrasted with high throughput screening (HTS), in which an enormous number of larger, more complex molecules are screened in order to find ones which bind. The key idea is recognizing that the molecules in these HTS libraries can typically be broken down into a much smaller number of common substructures, fragments, so screening these ought to be more informative: between them they describe more of the “chemical space” which interacts with the protein. Since it first appeared about 25 years ago, FBDD has delivered four drugs for clinical use and over 40 molecules to clinical trials.

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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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What is a plantibody?

Plants can be genetically engineered to express non-native proteins, for example, crops can be engineered to produce insect toxins in order to improve disease-resistance. However, I was not aware of their ability to express antibodies until, inspired by my expanding collection of house plants, I googled ‘plant immune systems’. 

Plants don’t naturally produce antibodies – they do not possess an adaptive immune system or any circulating immune defence cells. Despite this, plants can be made to express and assemble full length antibody heavy chains and light chains. This was first published back in 1989, when Hiatt et al. [1] successfully introduced mouse immunoglobulin genes to tobacco plants and produced functional antibodies with reasonable efficiency. The excellent term ‘plantibody‘ was coined soon after, to refer to antibodies and fragments of antibodies produced by plants transformed with antibody-coding genes. 

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New Antibody Therapeutic INNs will no longer end in “-mab”!

Happy 2022, Blopiggers!

My first post of the year is about another major change to the way the World Health Organisation will be assigning “International Non-proprietary Name”s (INNs) to antibody-based therapeutics. I haven’t seen this publicised widely, so I thought I’d share it here as it is an important consideration for anyone mining or exploiting this data.

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Antibody Engineering and Therapeutics Conference

I was invited to speak at the Antibody Engineering and Therapeutics Conference (presenting mine and Matt’s recently published epitope profiling paper), in San Diego (December 12th – 16th). Unfortunately, the pandemic had other ideas so I decided not to travel but luckily the conference was hybrid. 

The conference included 1 day of pre-conference workshops and 4 days of presentations from academic and industry, with livestreaming of the initial keynotes (including one from Charlotte). Remaining talks were recorded and made available after the conference. I’ve highlighted a few of my favourite talks and conference themes, with links to papers where available.

Naturally, a lot of the presented research related to covid-19. I was speaking in the ‘Antibody Repertoires and Covid-19’ session, where there were interesting presentations from Professor Eline Luning Prak from the University of Pennsylvania and Elaine Chen from Vanderbilt University analysing antibody responses in covid-recovered individuals, and comparing vaccine responses in covid-recovered vs covid-naiive individuals. Other talks around SARS-CoV-2 vaccines included Dr Laura Walker from Adimab/Adagio Therapeutics comparing BCR repertoire responses to different types of vaccinations, and the effect of using different booster types.

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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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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.

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A 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“

Figure 1. Alternative Antibody Formats
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.

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How 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?”

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