More Fun With 3D Printing

Recently the students of the Systems Approaches to Biomedical Science Centre for Doctoral Training took a 2-week module on our favourite subject: structural biology! As part of this, they were given the option to create their very own 3D printed model of a protein.

This year we had some great models created, some of which are shown in the picture above. The proteins are (clockwise from top left):

  • Clathrin (PDB 1XI4) – a really interesting protein that forms cages around vesicles inside the cell. This one was mine; I wrote about clathrin as part of my undergraduate dissertation many years ago…
  • GTPase (PDB 1YZN) – a protein that can bind and hydrolyse guanosine triphosphate (GTP), involved in membrane trafficking
  • TAL effector (PDB 3UGM) – this bacterial protein binds to specific regions of DNA in a host plant to activate the expression of plant genes that aid bacterial infection. The DNA here is in blue, the orange wrapped around it is the protein.
  • Mechanotransduction ion channel (PDB 5VKQ) – converts mechanical stimuli into electrical signals in specialized sensory cells.
  • ATP synthase – this protein machine builds most of the energy storage molecule ATP, which powers our cellular processes.
  • DNA (PDB 5F9I) – a double-helix strand of DNA, 20 base pairs long.

Property based testing in Python with Hypothesis : how to break your own code before someone else does 

Traceback (most recent call last):
ZeroDivisionError: integer division or modulo by 0

We’ve all been there. You’ve written your code, tested it out on some toy data and then when you make the move to the real data, there was something you didn’t expect.

Maybe some samples have been truncated to zero. Maybe the input arrays are the wrong shape. Suddenly your code comes crashing down around you, and you’re left thinking: well how could I have known that was going to happen? I can’t test everything

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A gentle primer on quantum annealing

If you have done any computational work, you must have spent some time waiting for your program to run. As an undergraduate, I expected computational biology to be all fun and games: idyllic hours passing time while the computer works hard to deliver results… well, very different from the more typical frenetically staring at the computer, wishing the program would run faster. But there is more — there are some problems that are so intrinsically expensive that, even if you had access to all the computers on Earth, it would take more than your lifetime to solve a slightly non-trivial case of them. Some examples are full configuration interaction calculations in quantum chemistry, factorisation of prime numbers, optimal planning, and a long, long, etcetera. Continue reading →

A blog post about a blog

I thought I would make this blog post very meta by referring to another blog, written by Lior Pachter, which I think has something for many of us in it: http://liorpachter.wordpress.com (networks people, there’s a pretty scathing take-down of a quite well cited 2013 paper as one of the last posts – there seem to be a couple of posts labelled “network nonsense”!)

In particular I refer you to the list, that Lior Pachter has curated, which includes all variations of *-seq. You’ll see that practically all sequencing protocols take on this nomenclature of catchy descriptor + seq.

*Seq

You will have heard mention of Ig-seq in talks by antibody people (with all Ig-seq experiments being curated in OAS by Alex). Ig-seq comes under the “Phenotyping” section of Lior’s list.

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Not-Proteins in Parliament

Last term I took a break from folding proteins to spend three months working in Westminster at the Parliamentary Office of Science and Technology (POST).

The UK Research and Innovation (UKRI) Policy Internships Scheme gives PhD students the opportunity to spend three months in a range of policy-relevant organisations, from Government departments to the Royal Society. Applications are open to research council funded PhD students (currently including EU students). The scheme includes a three-month stipend extension, and travel/accommodation expenses are covered either by the host partner or the training grant holder.

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Journal Club: Investigating Allostery with a lot of Crystals

Keedy et al. 2018: An expanded allosteric network in PTP1B by multitemperature crystallography, fragment screening, and covalent tethering.

Allostery is defined as a conformational/activity change of an active site due to a binding event at a distant (allosteric) site.

The paper I presented in the journal club tried to decipher the underlying mechanics of allostery in PTP1B. It is a protein tyrosine phosphatase (the counter parts of kinases) and a validated drug target. Allosteric binding sites are known but so far neither active site nor allosteric site inhibitors have reached clinical use. Thus, an improved mechanistic understanding could improve drug discovery efforts.

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Some useful tools

For my blog post this week, I thought I would share, as the title suggests, a small collection of tools and packages that I found to make my work a bit easier over the last few months (mainly python based). I might add to this list as I find new tools that I think deserve a shout-out.

Biopandas

Reading in .pdb files for processing and writing your own parser (while being a good exercise to familiarize yourself with the format) is a pain and clutters your code with boilerplate.

Luckily for us, Sebastian Raschka has written a neat package called biopandas [1] which enables quick I/O of .pdb files via the pandas DataFrame class.

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Kernel Methods are a Hot Topic in Network Feature Analysis

The kernel trick is a well known method in machine learning for producing a real-valued measure of similarity between data points in any number of settings. Kernel methods for network analysis provide a way of assigning real values to vertices of the graph. These values may correspond to similarity across any number of graphical properties such as the neighbours they share, or more dynamic context, the influence that change in the state of one vertex might have on another.

By using the kernel trick it is possible to approximate the distribution of features on the vertices of a graph in a way that respects the graphical relationships between vertices. Kernel based methods have long been used, for instance in inferring protein function from other proteins within Protein Interaction Networks (PINs).

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Magnetotaxis: A Bacterial Superpower

The idea of bacterial superpowers is perhaps most associated with superbugs: the terrifying, drug-resistant bacterial strains that appear ever more frequently in news reports. While the notion of a world where antibiotics no longer work is chilling, this blog post will focus on a more positive aspect of the bacterial domain.

One of the more “niche” bacterial superpowers is magnetotaxis: the ability of certain bacteria to align their motion to the Earth’s magnetic field. This phenomenon was first reported in 1963 by Salvatore Bellini in the University of Pavia. While observing bog sediment under the microscope, he noticed a set of bacteria orienting themselves in the same direction: towards the Earth’s magnetic North pole. He dubbed these gram-negative bacteria “magnetosensitive”, or “batteri magnetosensibili”, but the discovery went largely unnoticed by the international scientific community [1]. The name “magnetotactic bacteria” (MTB) was introduced about a decade later, when Richard Blakemore reported the same phenomenon for bacteria found in marine sediments [2]. Through transmission electron microscopy, Blakemore was also able to capture the cellular feature that gives MTBs their unusual abilities: a rod-like structure of membrane-bound, iron-rich inorganic crystals, called magnetosomes. Later it was revealed that this structure is supported by a dedicated cytoskeletal system, which keeps it rod-shaped and prevents the aggregation of magnetosomes [4]. Magnetotaxis then results from the combination of the passive alignment of the cell to the Earth’s magnetic field, and flagellar motion. Continue reading →

Making the most of your CPUs when using python

Over the last decade, single-threaded CPU performance has begun to plateau, whilst the number of logical cores has been increasing exponentially.

Like it or loathe it, for the last few years, python has featured as one of the top ten most popular languages [tiobe / PYPL].   That being said however, python has an issue which makes life harder for the user wanting to take advantage of this parallelism windfall.  That issue is called the GIL (Global Interpreter Lock).  The GIL can be thought of as the conch shell from Lord of the Flies.  You have to hold the conch (GIL) for your thread to be computed.  With only one conch, no matter how beautifully written and multithreaded your code, there will still only be one thread will be executed at any point in time.

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