AutoDock 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.

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Green 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.

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Parallelising 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).

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B-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.

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The 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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Transforming Parliament – Training and deploying speech generation transformers for parliamentary speakers

Introduction

I recently wanted to explore areas of machine learning that I do not usually interact with as part of my DPhil research on antibody drug discovery. This post explores how to train and deploy a speech generation model for parliamentary speeches in the style of Jeremy Corbyn and Boris Johnson. You can play around with the resulting model at https://con-schneider.github.io/theytalktoyou.html.

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Journal Club: Is our data biased, and should it be?

Jia, X., Lynch, A., Huang, Y. et al. Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature 573, 251–255 (2019) doi:10.1038/s41586-019-1540-5 https://www.nature.com/articles/s41586-019-1540-5

Last week I presented the above paper at group meeting. While a little different from a typical OPIG journal club paper, the data we have access to almost certainly suffers from the same range of (possible) biases explored in this paper.

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IEEEGor Knows What You’re Thinking…

Last month, I, EEGor, took part in the Brain-Computer Interface Designers Hackathon (BR41N.IO), the opening event of the IEEE Systems, Man and Cybernetics Conference in Bari, Italy. Brain-Computer Interfaces (BCIs) are a class of technologies designed to translate brain activity into machine actions to assist (currently in clinical trials) as well as (one day) enhance human beings. BCIs are receiving more and more media attention, most recently with the launch of Elon Musk’s newest company, Neuralink which aims to set up a two-way communication channel between man and machine using a tiny chip embedded in the brain. With the further aim of one-day perhaps making our wildest transhumanist dreams come true…

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