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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Non-alcoholic fatty liver disease

In my new research project, I investigate Non-alcoholic fatty liver disease (NAFLD). This term describes a variety of conditions that are associated with fatty livers. While the early stages of this disease are not harmful, it can lead to cirrhosis (Cirrhosis is the scaring of liver tissue that prevents a liver to function properly). Ultimately, if a liver stops working it can be fatal unless treated, for example, with a liver transplant. NAFLD is the most common liver disease in developed countries and is expected to become the leading cause of liver transplant by 2020 [1].

The disease progresses in four stages: Continue reading →

Graph-based Methods for Cheminformatics

In cheminformatics, there are many possible ways to encode chemical data represented by small molecules and proteins, such as SMILES, fingerprints, chemical descriptors etc. Recently, utilising graph-based methods for machine learning have become more prominent. In this post, we will explore why representing molecules as graphs is a natural and suitable encoding. Continue reading →

Automated testing with doctest

One of the ways to make your code more robust to unexpected input is to develop with boundary cases in your mind. Test-driven code development begins with writing a set of unit tests for each class. These tests often includes normal and extreme use cases. Thanks to packages like doctest for Python, Mocha and Jasmine for Javascript etc., we can write and test codes with an easy format. In this blog post, I will present a short example of how to get started with doctest in Python. N.B. doctest is best suited for small tests with a few scripts. If you would like to run a system testing, look for some other packages!

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Because not all interesting biology is health-related!

Nowadays, biological research science spins around health: Cancer. Neuroscience. Immunology. Pharmacology. And many more health-related areas which are being deeply studied. It seems that everyone is keen to spend their lives looking for the cure of cancer or Alzheimer. What a drag! For this reason (and also to show that research in less popular and less founded sectors can also improve significantly human lives), I have decided to write about something completely different: plant microbiome!

Indeed, I am going to write about bacteria. And no, they are not related to health at all. These bacteria live the soil and infect plants. However, they are not “bad”. Actually, they favour the plant’s growth and development. This is possible thanks to a fascinating process which finishes (ALERT SPOILER!!) with the bacteria transforming the atmospheric nitrogen into ammonia that can be used by the plant (nitrogen fixation).

The process starts with some kind of small talk between Rhizobium (the bacteria) and the legume (the plant): Legumes secrete compounds through their roots that the bacteria living close by can detect. In response to this stimulus, bacteria approach the root hairs of the plant and attach and secrete lipo-chitooligosaccharides known as Nod factors.

It continues with some action: The plants sense the Nod factors, which induce the root hairs curling and trapping the bacteria. The bacteria continue to grow and eventually form an infection thread whose growth allows the bacteria to reach other plant cells.

And it finishes with a happily ever after ending: A structure called a nodule is formed. The bacteria in the nodule form an organelle called the symbiosome, within which the bacteria differentiate to a state called bacteroid. In this stage, the bacteroid fixes nitrogen for the plant.

I know… Everything has happened too fast (the process can take 1 – 2 weeks). And I have not been bothered to explain it in detail so you can enjoy reading this amazing review: https://www.ncbi.nlm.nih.gov/pubmed/23493145

But wait! I almost forget to say why is worth studying this… The point is that plants need nitrogen to grow and they cannot use atmospheric nitrogen. Therefore, the more nitrogen they receive from the bacteria, the more they will grow. Consequently, we may increase the quantity of food available by improving this process.

Turning MD Trajectories into Movies using PyMOL

Putting movies into your presentations is the perfect way to cover up a terrible underlying presentation help the audience visualise the systems you are discussing. Static protein movies can enhance an introduction or help users understand important interactions between proteins and ligands. PyMOL plugins, such as emovie.py, help you move beyond the ‘rock’ and ‘roll’ scenes in PyMOL’s movie tab. But there ends the scope for your static structures.

If you want to take your PyMOL movie making skills to the next level, you should start adding some dynamics data. This allows your audience to visualise how your protein dynamics evolve over time and a much easier way to explain your results (because, who likes 10,000 graphs in a presentation!? Even if your R plots look super swish.). For example: understanding binding events, PPIs over time or even loop motion.

The following tutorial shows you how to turn a static PDB structure into a dynamic one, by adding a GROMACS trajectory. Most of the commands you will encounter while making a static structure movie, so should not be too alien.

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