Decades later, we owe Warren DeLano and his commitment to open source a great debt. Warren wrote PyMOL, an amazingly powerful and popular molecular visualization tool, but it has many hidden talents.
Perhaps its greatest strength is the use of the open source language, Python, as its control language.
Continue readingI recently completed an internship during which I spent a considerable amount of time doing software engineering. One of my main take-aways from this experience was that in industry, a lot more attention is spent on ensuring that code committed to a GitHub repo is clean and bug-free.
This is achieved through several means like code review (get other people to read your code), test-driven development (make sure your code works as you are adding functionality) or paired development (have two people work together on the same piece of code). Here, I will instead focus on a useful tool that is easy to integrate into your existing git workflow: Pre-commit hooks.
Continue readingIn preparation for remote teaching this year, I’ve spent the last few weeks converting the Doctoral Training Centre’s ‘Introduction to Computer Programming’ course into a series of Jupyter notebooks so that the course can be run entirely using Google Colaboratory.
Continue readingWe 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.
Continue readingToday is the day for another (potentially penultimate) blog post from me. Using this opportunity, I would like to introduce to you our recent update to the Observed Antibody Space (OAS) resource.
Continue readingIf you do most of your work in Jupyter notebooks, it can be convenient to have a quick visualisation tool to view the results of your latest computation from within the notebook, without having to flick between the notebook and your favourite molecule viewer.
I have recently started using NGLview, an IPython/Jupyter widget, to do this. It is based on the NGL viewer, an embeddable webapp for macromolecular visualisation. The nglvew module documentation can be found here, and in addition to handling the usual formats for molecular structure (.pdb, .mol2, .sdf, .pqr, etc.) and map density(.ccp4 and more), it supports visualising trajectories and even making movies.
Continue readingWe’ve all been there. You’ve just run an expensive computation in your Jupyter Notebook and are about to draw those conclusions which will prove that your theories were right all along (until you find the sixteen bugs in your code which render them invalid, but that’s an issue for a different time). Then at the critical moment, your flatmate begins streaming their Lord Of The Rings marathon in 4k and your already temperamental Wi-Fi severs your connection to the department servers in protest, crashing your Jupyter Notebook, leaving your hopes and dreams in tatters.
Continue readingGeneral MacroMocelecular I/O, or GEMMI, is a C++ 11 header only library for low level crystalographic .
Because its header only it is certainly the easiest to access and use low level crystalographic C++ library, however GEMMI comes with python binding via Pybind11, making it arguably the easiest low level crystalographic library to access and use in python as well!
What follows is a cookbook of useful Python code that uses GEMMI to accomplish macromolecular crystalographic tasks.
Continue readingScientific code is never fast enough. We need the results of that simulation before that pressing deadline, or that meeting with our advisor. Computational resources are scarce, and competition for a spot in the computing nodes (cough, cough) can be tiresome. We need to squeeze every ounce of performance. And we need to do it with as little effort as possible.
Continue readingAny opportunity to use rigorously tested and supported analysis tools rather than in-house code is, in my opinion, an opportunity you owe it to yourself to explore.
My preferred tool for analyzing the output of molecular dynamics (MD) simulations is MDAnalysis, a Python library that provides robust and easy-to-use tools for analyzing most common files output by MD packages (including PDB, DCD, COR, and XTC file formats). But, of course, MDAnalysis can analyze any PDB file, not just one output from an MD simulations. There may be an opportunity in your workflow to incorporate MDAnalysis to save time or to provide more robust error handling than whatever in-house code you currently use.