AIRR community meeting

Hi everyone,

Today is the day for another blog post from me. Last month I attended an AIRR conference in Genoa, Italy (https://www.antibodysociety.org/airrc/meetings/communityiv/). It was the fourth AIRR conference, and I was nice to see lots of field-leading people participating. Compared to the last AIRR meeting almost 2 years ago, the agenda of the conference was dominated by machine learning and big data topics. In my short blog post, I will discuss two talks that covered these two exciting topics.

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Oxford Maths Festival ‘19

The Oxford Maths Festival returned this year and it was tons of fun, at least for this volunteer! I failed to take pictures, but a few opiglets were involved: Flo and company took their VR work for the Ashmolean Dimensions exhibit and demonstrated it at Templars Square, and Conor did a spectacular job pretending to be a police constable for the maths escape room.

Last year Mark blogged about how we demonstrated the German Tank Problem at the festival. I thought this time round I’d share another of the Mathematical Mayhem activities: a game illustrating biased sampling.

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What is the hydrophobic-polar (HP) model?

Proteins are fascinating. They are ubiquitous in living organisms, carrying out all kinds of functions: from structural support to unbelievably powerful catalysis. And yet, despite their ubiquity, we are still bemused by their functioning, not to mention by how they came to be. As computational scientists, our research at OPIG is mostly about modelling proteins in different forms. We are a very heterogeneous group that leverages approaches of diverse scale: from modelling proteins as nodes in a complex interaction network, to full atomistic models that help us understand how they behave.

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Alchemistry Free Energy Workshop 2019, Göttingen

I thought I would use this blog to summarise the recent Alchemistry Free Energy workshop in Göttingen, Germany. This event, organised by MPI BPC and BioExcel, brought together academics and industrialists who work with alchemical MM methods to calculate free energies. This was a very successful successor to a similar event organised two year ago in London and now looks to be repeated yearly, alternating between Europe and Boston.

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Dimensions: The Mathematics of Symmetry and Space

Update: The exhibition was Highly Commended in Oxford’s The Vice-Chancellor’s Public Engagement with Research Awards 2019! [Link]

As part of outreach efforts, I have been involved with the exhibition “Dimensions: The Mathematics of Symmetry and Space” at the Ashmolean Museum. This exhibition is a great opportunity to explore a selection of the Ashmolean’s impressive collection from a mathematical (but very accessible) point of view.

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Should scientists learn C++?

Conventional wisdom dictates that compiled languages are slow to develop, can be slow to compile, but are fast to run. Interpreted languages are easy to use and do not require compilation but have sluggish performance. Like most people in scientific computing, the first two languages I learned were C++ and Python; I use Python every day but when, if ever, would I use C++?

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Spring 2019 ACS National Meeting (Orlando)

This blog post is jointly written by Lucian, Joe and Susan who recently attended the Spring ACS National Meeting 2019.

Susan, Joe and Lucian at the ACS conference.

The Spring ACS National Meeting was held in sunny Orlando, Florida and was a five day event (29th March – 4th April). The temperature averaged 25°C , which was amazing compared to the UK (sorry) and meant we all got a lovely tan. We all presented our work in the form of talks in the divisions of COMP or CINF but in this blog post we write about our highlights of the conference.

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Quick Python tricks

It’s always fun when you stumble across something in your programming toolkit that you had never noticed. Here are three things I’ve recently enjoyed learning.

  • Ternary syntax 
    a = int(raw_input())
is_even = True if a % 0 == 0 else False
  • Enumerate

I’ve been looping over the length of my list, all these years, like a chump. It turns out you can do this:

for index, item in enumerate(some_list):
    # now the index of each item is available as well as the item    

# Don't do do this
for index in range(len(some_list)):
    item = some_list[index]
  • for… else

Every so often, you really need to know that a for loop has run to completion. That’s what for…else is for!

for item in iterable:
    if item % 0 == 0:
       first_even_number = item
else:
    raise ValueError('No even numbers')

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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