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.
Continue readingCategory Archives: Proteins
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.
Continue readinghisto.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.
Continue readingAn 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”!
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.
Continue readingCryoEM 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.
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.
Continue readingOpenMM Setup: Start Simulating Proteins in 5 Minutes
Molecular dynamics (MD) simulations are a good way to explore the dynamical behaviour of a protein you might be interested in. One common problem is that they often have a relatively steep learning curve when using most MD engines.
What if you just want to run a simple, one-off simulation with no fancy enhanced sampling methods? OpenMM Setup is a useful tool for exactly this. It is built on the open-source OpenMM engine and provides an easy to install (via conda) GUI that can have you running a simulation in less than 5 minutes. Of course, running a simulation requires careful setting of parameters and being familiar with best practices and while this is beyond the scope of this post, there are many guides out there that can easily be found. Now on to the good stuff: using OpenMM Setup!
When you first run OpenMM Setup, you’ll be greeted by a browser window asking you to choose a structure to use. This can be a crystal structure or a model. Remember, sometimes these will have problems that need fixing like missing density or charged, non-physiological termini that would lead to artefacts, so visual inspection of the input is key! You can then choose the force field and water model you want to use, and tell OpenMM to do some cleaning up of the structure. Here I am running the simulation on hen egg-white lysozyme:
Fragment Based Drug Discovery with Crystallographic Fragment Screening at XChem and Beyond
Disclaimer: I’m a current PhD student working on PanDDA 2 for Frank von Delft and Charlotte Deane, and sponsored by Global Phasing, and some of this is my opinion – if it isn’t obvious in one of the references I probably said it so take it with a pinch of salt
Fragment Based Drug Discovery
Principle
Fragment based drugs discovery (FBDD) is a technique for finding lead compounds for medicinal chemistry. In FBDD a protein target of interest is identified for inhibition and a small library, typically of a few hundred compounds, is screened against it. Though these typically bind weakly, they can be used as a starting point for chemical elaboration towards something more lead-like. This approach is primarily contrasted with high throughput screening (HTS), in which an enormous number of larger, more complex molecules are screened in order to find ones which bind. The key idea is recognizing that the molecules in these HTS libraries can typically be broken down into a much smaller number of common substructures, fragments, so screening these ought to be more informative: between them they describe more of the “chemical space” which interacts with the protein. Since it first appeared about 25 years ago, FBDD has delivered four drugs for clinical use and over 40 molecules to clinical trials.
Continue readingA quantitative way to measure targeted protein degradation
Whenever we order consumables in the Chemistry department, the whole lab gets an email notification once they arrive. So I can understand why I got some puzzled reactions from my colleagues when one such email arrived saying that my ‘artichoke’ was ready to collect from stores. Had I been sneakily doing my grocery shopping on a university research budget?
Artichoke is, in fact, the name of a plasmid designed by the Ebert lab (https://www.addgene.org/73320/), which I have been using in some of my research on targeted protein degradation. The premise is simple enough: genes for two different fluorescent proteins, one of which is fused to a protein-of-interest.
Continue readingWhat is a plantibody?
Plants can be genetically engineered to express non-native proteins, for example, crops can be engineered to produce insect toxins in order to improve disease-resistance. However, I was not aware of their ability to express antibodies until, inspired by my expanding collection of house plants, I googled ‘plant immune systems’.
Plants don’t naturally produce antibodies – they do not possess an adaptive immune system or any circulating immune defence cells. Despite this, plants can be made to express and assemble full length antibody heavy chains and light chains. This was first published back in 1989, when Hiatt et al. [1] successfully introduced mouse immunoglobulin genes to tobacco plants and produced functional antibodies with reasonable efficiency. The excellent term ‘plantibody‘ was coined soon after, to refer to antibodies and fragments of antibodies produced by plants transformed with antibody-coding genes.
Continue readingNew Antibody Therapeutic INNs will no longer end in “-mab”!
Happy 2022, Blopiggers!
My first post of the year is about another major change to the way the World Health Organisation will be assigning “International Non-proprietary Name”s (INNs) to antibody-based therapeutics. I haven’t seen this publicised widely, so I thought I’d share it here as it is an important consideration for anyone mining or exploiting this data.