In this blogpost, I want to highlight the excellent work by Jahnke and collaborators. For the past 5 years, they have published an annual perspective covering fragment-to-lead success stories from the previous year. Very helpfully, their work includes a table detailing the hit fragment(s) and lead molecule, together with key experimental results and parameters.
Continue readingAIRR Community Meeting V – December 2020
We attended the virtual Adaptive Immune Receptor Repertoire (AIRR) Community Meeting in early December. The three day conference is usually held every 18 months and covered a range of research talks, software demonstrations and poster presentations on the latest TCR and BCR (antibody) research. While we missed certain elements that were present at the last AIRR community meeting (namely focaccia), it was a really interesting meeting with technology all running very smoothly.
Given our current research on SARS-CoV-2 antibodies, we particularly enjoyed the work presented by Armita Nourmohammad from the University of Washington on “Dynamics of BCR in Covid”, based on the preprint on medRxiv. The research identified 34 significantly expanded rare clonal lineages shared among patients with SARS-CoV-2, which are potential candidates for covid response. In particular, the analysis includes an assessment of whether an antibody sequence identified in different individuals (known as a shared or public sequence) is likely to be found due to inherent biases in antibody recombination. Shared antibody sequences which are calculated as unlikely to be shared are potentially a response to a shared exposure such as SARS-CoV2, rather than randomly found in the antibody repertoire. In this way, Nourmohammed and colleagues identified ‘rare’ antibodies which were identified in more individuals than would statistically be expected, and therefore might be worthy of further experimental analysis.
A theme common across a short talk and poster by Hadas Neuman (Bar-Ilan) and a poster by Kenneth Hoehn (Yale), was class-switching dynamics revealed by phylogenetic inference (from IgM to IgA in the human gut in the former, and IgE and IgG4 in a paediatric patient with peanut allergy in the latter). Kenneth Hoehn’s poster also looked at B-cell differentiation during HIV infection – this can all be read about in this preprint. The methods developed in the paper for discrete trait analysis of differentiation, isotype switching and B-cell migration are implemented in the new R package dowser (https://bitbucket.org/kleinstein/dowser) which is part of the Immcantation suite (http://immcantation.org).
It was also really nice to see evidence of the burgeoning use of single-cell sequencing for immune repertoire profiling, with posters by Igor Snapkov (UiO), Indu Khatri (Leiden University Medical Centre), Nick Borcherding (Washington University in St. Louis) all using single-cell technologies, and a talk by Ivelin Georgiev on LIBRA-seq.
If you missed the conference and have had your interest piqued, some of the conference talks are available at the AIRRC youtube channel.
We look forward to AIRRC6, Dec 7 – 11, 2021!
Sarah and Eve
OPIGmas 2020, Pandemic Edition
Not even a global pandemic can halt our annual celebrations. Festivus, move over. OPIGmas is here.
We were all lucky enough to have electricity; computers with webcams and microphones (Dan’s dalek incantations notwithstanding); and network connections; and somehow (for some of us) the time, to gather together around our twenty-first century electronic hearths and celebrate: Zoom, Gather Town, Among Us, Skribbl.io, and Codenames.
The much-awaited Secret Santa often reveals how naughty or nice the sender is, and sometimes surprising details about the relationship of the sender and recipient (I’m looking in the general direction of Dominik and Brennan). The rules are simple: spend up to £10 GBP, and don’t buy anything the boss wouldn’t buy for someone… But despite the hypothesis that the longer someone had been in OPIG, the more ‘pointed’ the gift would be, exceptions could still be found.
Armed with her new Easy Learning “Times Tables Bumper Book”, the boss was anointed “CEO of ******* Everything”, with her new desk name plate. Without coordinating, the boss’ PA independently received a desk name plate as “Fixer of Everything”. Perfect, on both counts.
Continue readingAn in vivo force sensor reveals varied mechanisms of co-translational force generation
This blog post comments on the results published by Fujiwara and co-workers in the 2020 Cell Reports article “Proteome-wide capture of co-translational protein dynamics in Bacillus subtilis using TnDR, a transposable protein-dynamics reporter.”
The study of mechanical force generation and its influence on biological systems has expanded in recent years. In the realm of nascent protein folding, we now know that both unstructured and folded nascent proteins generate forces on the order of piconewtons that propagate down the nascent chain. These forces can distort the functional site of the ribosome and may influence the rate of translation (PMIDs: 30824598, 29577725). It has also been shown that translational arrest can be relieved by mechanical force (PMID: 25908824). Much study has focused on so-called arrest peptides, short peptide sequences that interact so strongly with the ribosome exit tunnel that they can completely stall translation (e.g., SecM, MifM).
Prediction of Parkinson subtypes at COXIC 2020
Last week I attended the COXIC seminar (joint seminar Oxford – Imperial focused on networks and complex systems) organised by Florian Klimm from Imperial College London (and former OPIG member!). We had several interesting at the seminar. However, one of them caught my eye more than the rest. It was the talk of Dr Sanjukta Krishnagopal (UCL) titled Predicting Parkinson’s Sub-types through Trajectory Clustering in Bipartite Networks, of which I will give a quick insight. Hope you like it (at least) as much as I did!
This blogpost is based on these two articles:
- Sanjukta Krishnagopal, Rainer Von Coelln, Lisa Shulman, Michelle Girvan. “Identifying and predicting Parkinson’s disease subtypes through trajectory clustering via bipartite networks” PloS one (2020)
- Sanjukta Krishnagopal. “Multi-later Trajectory Clustering Network Algorithm for Disease Subtyping” Biomedical Physics & Engineering Express (2020)
Curious About the Origins of Computerized Molecules? Free Webinar Dec 22…
After the stunning announcement at CASP14 that DeepMind’s AlphaFold 2 had successfully predicted the structures of proteins from their sequence alone, it’s hard to believe we began this journey by representing molecules with punched cards…
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Tales of carrying stacks of punched cards to the computer centre with a line drawn diagonally on the side of the stack, to help put them back in order should you trip and fall—seem like another universe—but this is what passed for the human-computer interface in much of the mid-20th century.
Continue readingUMAP Visualization of SARS-CoV-2 Data in ChEMBL
CASP14: what Google DeepMind’s AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics
Disclaimer: this post is an opinion piece based on the experience and opinions derived from attending the CASP14 conference as a doctoral student researching protein modelling. When provided, quotes have been extracted from my notes of the event, and while I hope to have captured them as accurately as possible, I cannot guarantee that they are a word-by-word facsimile of what the individuals said. Neither the Oxford Protein Informatics Group nor I accept any responsibility for the content of this post.
You might have heard it from the scientific or regular press, perhaps even from DeepMind’s own blog. Google ‘s AlphaFold 2 indisputably won the 14th Critical Assessment of Structural Prediction competition, a biannual blind test where computational biologists try to predict the structure of several proteins whose structure has been determined experimentally — yet not publicly released. Their results are so incredibly accurate that many have hailed this code as the solution to the long-standing protein structure prediction problem.
Continue readingDrawing Wavy Lines That Match Your Data, or, An Introduction to Kernel Density Estimation
One of the fundamental questions of statistics is “How likely is it that event X will occur, given what we’ve observed already?”. It’s a question that pops up in all sorts of different fields, and in our daily lives as well, so it’s well worth being able to answer rationally. Under the statistician’s favourite assumption that the observed data are independent and identically distributed (i.i.d.), we can use the data to construct a probability distribution; that is, if we’re about to observe a new data point, x*, we can say how likely it is that x* will take a specific value.
Continue readingBioDataScience101: a fantastic initiative to learn bioinformatics and data science
Last Wednesday, I was fortunate enough to be invited as a guest lecturer to the 3rd BioDataScience101 workshop, an initiative spearheaded by Paolo Marcatili, Professor of Bioinformatics at the Technical University of Denmark (DTU). This session, on amino acid sequence analysis applied to both proteomics and antibody drug discovery, was designed and organised by OPIG’s very own Tobias Olsen.
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