I have recently stumbled upon this paper which, quite unexpectedly, sent me down a rabbit hole reading about compression, generalisation, algorithmic information theory and looking at gifs of milk mixing with coffee on the internet. Here are some half-processed takeaways from this weird journey.
First, check out this cool video.
One of the fundamental (pitfalls) of machine learning is to ensure that you don’t train on your test set, but what if I told you that you could?
Prion are both fascinating and terrifying. They occur naturally and have a purpose, but what that purpose is we’re still not entirely sure. Gene-knockout mice which no longer code for the prion protein do live, but they ain’t born typical.
The endogenous form of the prion protein (PrPC) can, through currently unknown mechanisms, take a different conformation, the pathogenic PrPSc. PrPSc is responsible for fatal, rapidly progressing neurodegenerative disorders which in many cases can jump species.
At OPIG, we recently discussed a remarkably rigorous series of experiments outlined in the paper “A Protein Misfolding Shaking Amplification-based method for the spontaneous generation of hundreds of bona fide prions” Whilst deliberately creating new pathogenic prions may seem and odd thing to wish to achieve, the authors aimed to determine if there was a golden thread linking “infectivity determinants, interspecies transmission barriers or the structural influence of specific amino acids”.
Continue readingAs a first year PhD student, it is not an uncommon thing to be asked to write a review paper on your subject area. It is both a great way to get acquainted with your research field and to get the background portion of your thesis completed early. However, it can seem like a daunting task to go from knowing almost nothing about your research field to producing something of interest for experts who have spent years studying your subject matter.
In my first year, I was exactly in this position and I found very little online to help guide this process. Thus, here is my reflective look at writing a review paper that will hopefully help someone else in the future.
Continue readingAs so many members of the group have never attended an in-person conference, I thought it might be worth answering the question “why do people attend conferences?”
First- up, we should remember that flying around the world is not a zero cost to the planet, so all of us lucky enough to be able to travel should think hard every time before we choose to do so.
This means it’s really important to make sure that we know why we are going to any conference and maximise the benefits from attendance. Below are a few things to think about in terms of why you attend a conference and what to do when you are there, but this is definitely not a complete list, more a starter for four.
Continue readingMolecular docking helps us understand how small-molecules interact with proteins. This is especially useful in early drug development stages such as target identification and compound screening. Quick and accurate docking software allows researchers to focus their attention on a smaller set of lead molecules for further testing. Traditionally, docking software has employed first principles from physics and chemistry. Recently, deep learning has become all the rage for molecular docking, maybe motivated by the successful application of deep learning to molecular folding.
EquiBind is a deep learning unconstrained docking method which models a fixed receptor and a ligand with selected rotatable bonds. It predicts the binding pocket and the ligand’s conformation within the pocket in one go. Under the hood, EquiBind employs two great ideas from a recent ICLR 2022 Paper: a SE3-invariant graph neural network based architecture and the idea to generate fixed sets of matching key points to define a rotation and translation between receptor and ligand. In addition, the authors innovate a fast method to project a deformed ligand onto the space spanned by the rotatable bonds of a pre-generated ligand conformation.
Continue readingWhile my own research focuses mainly on what happens in an antibody before it binds its antigen, I recently came across a paper by Akbar et al [1] that examines antibody-antigen interactions using an elegant approach to identify a set of structural motifs that antibodies use to interact with their epitopes. Since I am interested in emergent properties that arise when a sequence is mapped onto an antibody structure, this paper was very exciting. I will also shamelessly admit that I’m a sucker for a pretty figure and this paper has many! Regardless, on to the findings!
In my work, I mainly look at antigen-bound antibodies and this means a lot of analysing interfaces. Specifically, I spend a lot of my time examining the contributions of complementarity-determining regions (CDRs) to antigen binding, but what about antibodies where the framework (FW) region also contributes to binding? Such structures do exist, and these interactions are rarely trivial. As such, a recent preprint I came across where the authors examined the DE loops of antibodies was a great motivator to broaden my horizons!
Continue readingBoth the beauty and the downfall of learning-based methods is that the data used for training will largely determine the quality of any model or system.
While there have been numerous algorithmic advances in recent years, the most successful applications of machine learning have been in areas where either (i) you can generate your own data in a fully understood environment (e.g. AlphaGo/AlphaZero), or (ii) data is so abundant that you’re essentially training on “everything” (e.g. GPT2/3, CNNs trained on ImageNet).
This covers only a narrow range of applications, with most data not falling into one of these two categories. Unfortunately, when this is true (and even sometimes when you are in one of those rare cases) your data is almost certainly biased – you just may or may not know it.
Continue readingViruses are small infectious agents, which possess genetic code but have no independent metabolism. They propagate by infecting host cells and hijacking their machinery, often killing the cells in the process. One of the key challenges in developing effective antiviral therapies is the high mutation rate observed in viral genomes. A way to circumvent this issue is to target host proteins involved in virion assembly (also known as essential host factors, or EHFs), rather than the virion itself.
In their recent paper, Lu Han et al. [1] consider human virus protein-protein interactions in order to explore possible host drug targets, as well as drugs which could potentially be re-purposed as antivirals. Their study focuses on enterovirus 71 (EV71), one of the leading causes of hand, foot, and mouth disease.
EHFs are typically detected by knocking out genes in the host organism and determining which of the knockouts result in virus control. Low repeat rates and high costs make this technique unsuitable for large scale studies. Instead, the authors use an extensive yeast two-hybrid screen to identify 37 unique protein-protein interactions between 7 of the 11 virus proteins and 29 human proteins. Pathway enrichment suggests that the human proteins interacting with EV71 are involved in a wide range of functions in the cell. Despite this range in functionality, as many as 17 are also associated with other viruses, either through known physical interactions, or as EHFs (Fig 1).
Fig. 1. Interactions between viral and human proteins (denoted as EIPs), and their connection to different viruses.
One of these is ATP6V0C, a subunit of vacuole ATP-ase. It interacts with the EV71 3A protein, and is a known essential host factor for five other viruses. The authors analyse the interaction further, and show that downregulating ATP6V0C gene expression inhibits EV71 propagation, while overexpressing it enhances virus propagation. Moreover, treating cells with bafilomycin A1, a selective inhibitor for vacuole ATP-ase, inhibits EV71 infection in a dose-dependent manner. The paper suggests that therefore ATP6V0C may be a suitable drug target, not only against EV71, but also perhaps even for a broad-spectrum antiviral. While this is encouraging, bafilomycin A1 is a toxic antibiotic used in research, but not suitable for human or drug use. Rather than exploring other compounds targeting ATP6V0C, the paper shifts focus to re-purposing known drugs as antivirals.
A potential antiviral will ideally disturb most or all interactions between host cell and virion. One way to do this would be to inhibit the proteins known to interact with EV71. In order to check whether any known compounds already do so, the authors apply gene set enrichment analysis (GSEA) to data from the connectivity map (CMap). CMap is a database of gene expression profiles representing cellular response to a set of 1309 different compounds. Enrichment analysis of the database reveals 27 potential EV71 drugs, of which the authors focus on the top ranking result, tanespimycin.
Tanespimycin is an orphan cancer drug, originally designed to target tumor cells by inhibiting HSP90. Its complex effects on the cell, however, may make it an effective antiviral. Following their CMap analysis, the authors show that tanespimycin reduces viral count and virus-induced cytopathic effects in a dose-dependent manner, without evidence of cytotoxicity.
Overall, the paper presents two different ways to think about target investigation and drug choice in antiviral therapeutics — by integrating different types of known host virus protein-protein interactions, and by analysing cell response to known compounds. While extensive further study is needed to determine whether the results are directly clinically relevant to the treatment of EV71, the paper shows how interaction data analysis can be employed in drug discovery.
References:
[1] Han, Lu, et al. “Human enterovirus 71 protein interaction network prompts antiviral drug repositioning.” Scientific Reports 7 (2017).