Most scientists working in the biological sciences or an overlapping field have encountered various ways of identifying genes and proteins. There are many different types of identifiers. For example, searching for the PDB ID: 2IW3 (which represents elongation factor 3 in yeast strain S288C) on UniProt gives us a results column labeled “Gene names” that includes no less than six (!) ways to refer to the gene that produces this particular protein. This can be frustrating – it is easy to get into trouble when you think you have a consistent gene naming scheme when you do not, especially if you want to cross-reference gene lists.
Monthly Archives: January 2020
Functional Programming in Python
Introduction
The difficulty of reasoning about the behaviour of stateful programs, especially in concurrnent enviroments, has led to increased in intrest in a programming paradigm called functional programming. This style emphasises the connection between programs and mathematics, encouraging code that is easy to understand and, in some critical cases, even possible to prove properties of.
Continue readingAutoDock 4 and AutoDock Vina
A recently just-released publication from Ngyuen et al. ing JCIM pointed out that while AutoDock Vina is faster, AutoDock 4 tends to have better correlation with experimental binding affinity.1
[This post has been edited to provide more information about the cited paper, as well as providing additional citations.]
Ngyuyen et al. selected 800 protein-ligand complexes for 47 protein targets that had both experimental PDB structures complexed with a ligand, as well as their associated binding affinity values.
Continue readingGreen politics, the left, and Brexit
For my first non-technical blogpost, I thought I’d go in for something that we can all agree on and is entirely devoid of controversy: Brexit. Is that growning I hear from the back of the room?
One of my uncles is a professor of sociology; he returned to the UK for the first time in 10 years over Christmas 2019, and naturally we had plenty to talk about. He had left with two kids when I was a lanky, goofy teenager and had returned with four to a lanky, goofy adult. What were most interesting, though, were his views on green politics and their relationship with the traditional left-right spectrum.
Continue readingParallelising antigen-specific B-cell isolation with LIBRA-seq
Today is the day when I write a blog about an exciting research paper in the field of B-cell receptor (BCR) repertoires analysis. At OPIG, we (antibody people) are working hard to model and characterise antibody 3D configuration from its sequence. Significant progress has been made in modelling software development, so that we can predict antibody structures with high confidence. This task becomes considerably harder when we model the entirety of BCR repertoire sequences. Current methods of BCR repertoire sequencing operate primarily on the heavy chain only. This limits our capacity to generate refined 3D antibody models to just approximation of shapes of complementarity determining regions(CDRs).
Continue readingB-Cell Bispecificity?!
Happy New Year, Blopiggers!
Just a quick one from me this time around, to draw your attention to this intriguing paper by Shi et al., published in Nature Cell Discovery late last year.
“More than one antibody of individual B cells revealed by single-cell immune profiling“
Zhan Shi, Qingyang Zhang, Huige Yan, et al.
Nature Cell Discovery (2019) 5:64
Single-cell transcriptomics (e.g. using TenX sequencing) is beginning to yield fascinating insights into the inner workings of our immune system. It has long been thought that a single B cell can only express one antibody variable domain on its surface, accounted for by theories such as allelic exclusion and isotype exclusion.
Continue readingThe evolution of contact prediction – a new paper
I’m so pleased to be able to write about our work on The evolution of contact prediction: evidence that contact selection in statistical contact prediction is changing (Bioinformatics btz816). Contact prediction – the prediction of parts of the amino-acid chain that are close together – has been critical to improving the ability of scientists to predict protein structures over the last decade. Here we look at the properties of these predictions, and what that might mean for their use.
The paper begins with a question. If contact prediction methods are based on statistical properties of sequence alignments, and those alignments are generated in the presence of ecological and physical constraints, what effect do the physical constraints have on the statistical properties of real sequence alignments? More concisely: when we predict contacts, do we predict particularly important contacts?
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