Category Archives: Proteins

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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It’s been here all along: Analysis of the antibody DE loop

In my work, I mainly look at antigen-bound antibodies and this means a lot of analysing interfaces. Specifically, I spend a lot of my time examining the contributions of complementarity-determining regions (CDRs) to antigen binding, but what about antibodies where the framework (FW) region also contributes to binding? Such structures do exist, and these interactions are rarely trivial. As such, a recent preprint I came across where the authors examined the DE loops of antibodies was a great motivator to broaden my horizons!

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PyMOL: colouring proteins by property

We all love pretty, colourful pictures of proteins. There is quite a variety of programs to produce publication-quality images of proteins, some of the most popular being VMD, PyMOL and Chimera. Each has advantages and disadvantages — for example, VMD is particularly good to deal with molecular dynamics simulations (perhaps that’s why it is called “Visual Molecular Dynamics”?), and Chimera is able to produce breathtaking graphics with very little user input. In my work, however, I tend to peruse PyMOL: a Python interface is incredibly helpful to produce quick analyses.

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Curing Dogs With Cancer: The Power of the Antibody

This blog post finally combines the two great passions of my life: antibodies and dogs. Therapeutic antibody development is a huge area and is certainly not limited to humans. In the process of developing antibodies, we often use mouse or rat antibodies, obtained by injecting the animal with the antigen of choice and then collecting the resulting antibodies. The first monoclonal antibodies (mAbs) were produced in this way, by fusing spleen B cells from an immunised mouse or rabbit with immortalised myeloma cells to form antibody-expressing hybridoma cells. However, using antibodies to treat disease in animals lags behind humans.

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C is for Cysteines (plus a fun quiz)

At group meeting a few weeks ago I presented this paper, “Landscape of Non-canonical Cysteines in Human VH Repertoire Revealed by Immunogenetic Analysis“, from Prabakaran and Chowdhury. The paper is an investigation of the frequency, location and patterns of cysteines contained in human antibody sequences. Cysteines are important amino acids found in proteins, including antibodies, which can form disulphide bonds with other cysteines due to the presence of their reactive sulfhydryl group in the side chain.

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ICML 2020: Chemistry / Biology papers

ICML is one of the largest machine learning conferences and, like many other conferences this year, is running virtually from 12th – 18th July.

The list of accepted papers can be found here, with 1,088 papers accepted out of 4,990 submissions (22% acceptance rate). Similar to my post on NeurIPS 2019 papers, I will highlight several of potential interest to the chem-/bio-informatics communities. As before, given the large number of papers, these were selected either by “accident” (i.e. I stumbled across them in one way or another) or through a basic search (e.g. Ctrl+f “molecule”).

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