Category Archives: Proteins

The Smallest Allosteric System

Allostery is still a badly understood but very general mechanism in the protein world. In principle, an allosteric event occurs when a ligand (small or big) binds to a certain site of a protein and something (activity or function) changes at a different, distant site. A well-known example would be G-protein-coupled receptors that transport such an allosteric signal even across a membrane. But it does not have to be that far apart. As part of the Protein Folding and Dynamics series, I have recently watched a talk by Peter Hamm (Zurich) who presented work on an allosteric system that I thought was very interesting because it was small and most importantly, controllable.

PDZ domains are peptide-binding domains, often part of multi-domain proteins. For the work presented the researchers used the PDZ3 domain which is a bit special and has an additional (third) C-terminal α-helix (α3-helix) which is packing to the other side of the binding pocket. Previous work (Petit et al. 2009) had shown that removal of the α3-helix had changed ligand affinity but not PDZ structure, major changes were of an entropic nature instead. Peter Hamm’s group linked an azobenzene-derived photoswitch to that α3-helix; in its cis configuration stabilizing the α3-helix and destabilising in trans (see Figure 1).

Figure 1: PDZ3 domain (purple) and photoswitch (red) have different affinities for the peptide ligand (green), depending on the photoswitch’s isomerisation state (and temperature). From Bozovic, O., Jankovic, B. & Hamm, P. Sensing the allosteric force. Nat Commun 11, 5841 (2020). https://doi.org/10.1038/s41467-020-19689-7
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Bioinformatics Hackathon Reflection

A week ago I participated in Copenhagen Bioinformatics Hackathon 2021, a hackathon focusing on machine learning and proteins, as a mentor for a challenge proposed by our group. The whole experience was fun, but I am also sitting here contemplating over a lot of things I wish I had done differently. For this blog text, I therefore want to highlight two changes which I believe would have greatly improved my challenge and which can hopefully also work as an inspiration for others presenting a hackathon challenge. 

Going into this event I had some experience from a few hackathons I had previously attended. Based on this, I wanted to create a challenge containing two parts. First, a simple task which everyone would be able to create a solution for, and second, a more challenging addition to the first task for more experienced participants. I decided to go with the challenge of predicting which heavy and light chains can form a pair, where the additional challenge was to try to visualize which residues were relevant for this interaction. Together with OAS containing a really nice positive dataset of paired chains, I thought this was going to be an amazing challenge, but as soon as the event began I started seeing the flaws of the challenge.

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The Coronavirus Antibody Database: 10 months on, 10x the data!

Back in May 2020, we released the Coronavirus Antibody Database (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, most of which were characterised shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain…

10 months later (2nd March 2021), we now have tracked 2,673 coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These datapoints originate from 100s of independent studies reported in either the academic literature or patent filings.

The entire contents CoV-AbDab database as of 2nd March 2021.
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Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains

Shameless plug for any OPIG blog readers to take a look at our recent publication in Bioinformatics. Consider giving it a read if the below summary grabs your attention.

Many proteins are now known to fold during their synthesis through the process known as co-translational folding. Translation is an inherently non-equilibrium process – one consequence of this fact is that the speed of translation can radically influence the ability of proteins to fold and function. In this paper we compare ribosome occupancy profiles between related domains in yeast to test the hypothesis that evolutionarily related proteins with similar native folds should tend to have similar translation speed profiles to preserve efficient co-translational folding. We find strong evidence in support of this hypothesis at the level of individual protein domains and across a set of 664 pairs of related domains for which we are able to compute high-quality ribosome occupancy profiles.

To find out more, view the Advance Article at Bioinformatics.

Miniproteins – small but mighty!

Proteins come in all shapes and sizes, ranging from thousands of amino acids in length to less than 20. However, smaller size does not correlate with reduced importance. Miniproteins, which are commonly defined as being less than 100 amino acids long, are receiving increased attention for their potential roles as pharmaceuticals. A recent paper by David Baker’s group put miniproteins into the spotlight, as the study authors were able to design miniproteins that bind the SARS-CoV-2 spike protein with as strong affinity as an antibody would – but in a tiny fraction of the size (Cao et al., 2020). These miniproteins are much cheaper to manufacture than antibodies (as they can be expressed in bacteria) and can be highly stable (with melting temperatures of >90º possible, meaning they can easily be stored at room temperature). The most promising miniprotein developed by the Baker group (LCB1) is currently undergoing testing to be used as a prophylactic nasal spray that provides protection against SARS-CoV-2 infection. These promising results – and the speed in which progress was made – brings the vast potential of miniproteins in healthcare to the fore.

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An 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).

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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…

Image of a punched card, showing 80 columns and 12 rows, with particular rectangular holes representing the 1 bits of binary numbers. The upper right corner is cut at an angle, to facilitate feeding the card into a punched card reader. The column numbers are printed along the bottom. The words “IBM UNITED KINGDOM LIMITED” are printed along the very bottom. This card is line 12 from a Fortran program, “12 PIFRA=(A(JB,37)-A(JB,99))/A(JB,47) PUX 0430”. Image Credit: Pete Birkinshaw, Manchester, U.K. CC BY 2.0

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.

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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.

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BioDataScience101: 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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