Category Archives: Proteins

Inverse Vaccines

One of the nice things about OPIG, is that you can talk about something which is outside of your wheelhouse without feeling that the specialists in the group are going to eat your lunch. Last week, I gave an overview of the Hubbell group‘s Nature paper Synthetically glycosylated antigens for the antigen-specific suppression of established immune responses. I am not an immunologist by any stretch of the imagination, but sometimes you come across a piece of really interesting science and just want to say to people: Have you seen this, look at this, it’s really clever!

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The stuff MDAnalysis didn’t implement: CPU Parallel HOLE conductance analysis

Some time ago, I needed to find a way to computationally estimate conductance values for every protein frame from several molecular dynamics (MD) trajectories.

In a previous post, I wrote about how to clean the resulting instant conductance timeseries from outliers. But, I never described how I generated these timeseries.

In this post, I will show how you can parallelise the computation of instant conductance given an MD trajectory. I will touch on the difficulties of this process. And why I had to implement a custom tool for it given that MDAnalysis seems to already have implemented a routine of this sort. Finally, I will provide two Python scripts that you can easily adapt to run your parallel calculations – for which I’ll provide some important notes you don’t wanna skip.

Violin plots of conductance distributions from 64 molecular dynamic trajectories with 1000 frames each.
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What can you do with the OPIG Immunoinformatics Suite? v3.0

OPIG’s growing immunoinformatics team continues to develop and openly distribute a wide variety of databases and software packages for antibody/nanobody/T-cell receptor analysis. Below is a summary of all the latest updates (follows on from v1.0 and v2.0).

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9th Joint Sheffield Conference on Cheminformatics

Over the next few days, researchers from around the world will be gathering in Sheffield for the 9th Joint Sheffield Conference on Cheminformatics. As one of the organizers (wearing my Molecular Graphics and Modeling Society ‘hat’), I can say we have an exciting array of speakers and sessions:

  • De Novo Design
  • Open Science
  • Chemical Space
  • Physics-based Modelling
  • Machine Learning
  • Property Prediction
  • Virtual Screening
  • Case Studies
  • Molecular Representations

It has traditionally taken place every three years, but despite the global pandemic it is returning this year, once again in person in the excellent conference facilities at The Edge. You can download the full programme in iCal format, and here is the conference calendar:

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Can AlphaFold predict protein-protein interfaces?

Since its release, AlphaFold has been the buzz of the computational biology community. It seems that every group in the protein science field is trying to apply the model in their respective areas of research. Already we are seeing numerous papers attempting to adapt the model to specific niche domains across a broad range of life sciences. In this blog post I summarise a recent paper’s use of the technology for predicting protein-protein interfaces.

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Happy 10th Birthday, Blopig!

OPIG recently celebrated its 20th year; and on 10 January 2023 I gave a talk just a day before the 10th anniversary of BLOPIG’s first blog post. It’s worth reflecting on what’s stayed the same and what’s changed since then.

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histo.fyi: A Useful New Database of Peptide:Major Histocompatibility Complex (pMHC) Structures

pMHCs are set to become a major target class in drug discovery; unusual peptide fragments presented by MHC can be used to distinguish infected/cancerous cells from healthy cells more precisely than over-expressed biomarkers. In this blog post, I will highlight a prototype resource: Dr. Chris Thorpe’s new database of pMHC structures, histo.fyi.

histo.fyi provides a one-stop shop for data on (currently) around 1400 pMHC complexes. Similar to our dedicated databases for antibody/nanobody structures (SAbDab) and T-cell receptor (TCR) structures (STCRDab), histo.fyi will scrape the PDB on a weekly basis for any new pMHC data and process these structures in a way that facilitates their analysis.

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An evolutionary lens for understanding cancer and its treatment

I recently found myself in the Oxford Blackwells’ Norrington Room browsing the shelves for some holiday reading. One book in particular caught my eye, a blend of evolution — a topic that has long interested me — and cancer biology, a topic I’m increasingly exposed to in immune repertoire analysis collaborations but on which I am assuredly “non-expert”!

Paperback cover of “The Cheating Cell” by Athene Aktipis.

The Cheating Cell by Athene Aktipis provides a theoretical framework for understanding cancer by considering it as a logical sequitor of the advent of successful multicellular life.

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CryoEM is now the dominant technique for solving antibody structures

Last year, the Structural Antibody Database (SAbDab) listed a record-breaking 894 new antibody structures, driven in no small part by the continued efforts of the researchers to understand SARS-CoV-2.

Fig. 1: The aggregate growth in antibody structure data (all methods) over time. Taken from http://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab/stats/ on 25th May 2022.

In this blog post I wanted to highlight the major driving force behind this curve – the huge increase in cryo electron microscopy (cryoEM) data – and the implications of this for the field of structure-based antibody informatics.

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