Category Archives: Protein Structure

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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Making Pretty Pictures with PyMOL

There’s few things I like more in our field than the opportunity to make a really nice image of a protein structure. Don’t judge me, but I’ve been known to spend the occasional evening in front of the TV with a cup of tea and PyMOL open in front of me! I’ve presented on the subject at a couple of our research group retreats, and have wanted to type it up into a blog post for a while – and this is the last opportunity I will have, since I will be leaving in just a few weeks time, after nearly eight years (!) as an OPIGlet. So, here goes – my tips and tricks for making pretty pictures with PyMOL!

Ray Tracing

set ray_trace_mode, number

I always ray trace my images to make them higher quality. It can take a while for large proteins, but it’s always worth it! My favourite setting is 1, but 3 can be fun to make things a bit more cartoon-ish.

You can also improve the quality of the image by increasing the ‘surface_quality’ and ‘cartoon_sampling’ settings.

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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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PyMOL: colour by residue

PyMOL is a handy free way of viewing three dimensional protein structures. It allows you to toggle between different representations of the protein – such as cartoon, surface, sticks, etc. – which all have their own pros and cons.

However one thing I felt that PyMOL lacked was an easy way to visually distinguish residues based on type. Whist you can easily differentiate atom types based on colour in the colour menu, and even choose which colour you wish carbons to show up as whilst keeping heteroatoms different colours, this assigned carbon colour would be constant throughout the entire protein.

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Speaking about Sequence and Structure at a Summit

A couple of weeks ago I was lucky enough to be asked to speak at the 5th Computational Drug Discovery & Development for Biologics Summit. This was my first virtual conference – it was a shame I didn’t get to visit Boston, and presenting to my empty room was slightly bizarre, but it was great to hear what people have been working on, and there’s definitely something to be said for attending a conference in fluffy socks…

A, antibody structure. An antibody is made up of four chains: two light (orange) and two heavy (blue). Each chain is made up of a series of domains—the variable domains of the light and heavy chains together are known as the Fv region (shown on the right; PDB entry 12E8). The Fv features six loops known as complementarity determining regions or CDRs (shown in dark blue); these are mainly responsible for antigen binding. B, example sequences for the VH and VL, highlighting the CDR regions and the genetic composition. It is estimated that the human antibody repertoire contains up to 1013 unique sequences, enabling the immune system to respond to almost any antigen. This is possible through the recombination of V, D and J gene segments, junctional diversification, and somatic hypermutation.
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Constrained docking for bump and hole methodology

Selectivity is an important trait to consider when designing small molecule probes for chemical biology. If you wish to use a small molecule to study a particular protein, but that small molecule is fairly promiscuous in its binding habits, there are risks that any effects you observe may be due to it binding other proteins with similarly shaped binding pockets, instead of your protein of interest.

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