I have been using PyMOL, a molecular visualisation program, throughout my PhD and wanted to share some of the things I’ve learned.
1. Installation
Open source PyMOL can be installed using conda: conda install -c conda-forge pymol-open-source. It does not watermark figures and has full functionality, as far as I have been able to tell.
pMHCs are set to become a major target class in drug discovery; unusual peptide fragments presented by MHC can be used to distinguish infected/cancerous cells from healthy cells more precisely than over-expressed biomarkers. In this blog post, I will highlight a prototype resource: Dr. Chris Thorpe’s new database of pMHC structures, histo.fyi.
histo.fyi provides a one-stop shop for data on (currently) around 1400 pMHC complexes. Similar to our dedicated databases for antibody/nanobody structures (SAbDab) and T-cell receptor (TCR) structures (STCRDab), histo.fyi will scrape the PDB on a weekly basis for any new pMHC data and process these structures in a way that facilitates their analysis.
Continue readingIt is possible to get an installer for the crystallographer’s favourite molecular visualization tool for Windows machines, that is if you are willing to pay a fee. Fortunately, Christoph Gohlke has made available free, pre-compiled Windows versions of the latest PyMOL software, along with all of it’s requirements, it’s just not particularly straightforward to install. The PyMOLWiki offers a three-step guide on how to do this and I will break it down to make it somewhat clearer.
Download the Windows Installer (x-bit) for Python 3 from their website, x being your Windows architecture – 32 or 64.
Then, follow the instructions on how to install it. You can check if it has installed by running the following in PowerShell:
Continue readingThere’s few things I like more in our field than the opportunity to make a really nice image of a protein structure. Don’t judge me, but I’ve been known to spend the occasional evening in front of the TV with a cup of tea and PyMOL open in front of me! I’ve presented on the subject at a couple of our research group retreats, and have wanted to type it up into a blog post for a while – and this is the last opportunity I will have, since I will be leaving in just a few weeks time, after nearly eight years (!) as an OPIGlet. So, here goes – my tips and tricks for making pretty pictures with PyMOL!
Ray Tracing
set ray_trace_mode, number
I always ray trace my images to make them higher quality. It can take a while for large proteins, but it’s always worth it! My favourite setting is 1, but 3 can be fun to make things a bit more cartoon-ish.
You can also improve the quality of the image by increasing the ‘surface_quality’ and ‘cartoon_sampling’ settings.
PyMOL is a handy free way of viewing three dimensional protein structures. It allows you to toggle between different representations of the protein – such as cartoon, surface, sticks, etc. – which all have their own pros and cons.
However one thing I felt that PyMOL lacked was an easy way to visually distinguish residues based on type. Whist you can easily differentiate atom types based on colour in the colour menu, and even choose which colour you wish carbons to show up as whilst keeping heteroatoms different colours, this assigned carbon colour would be constant throughout the entire protein.
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 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 readingPutting 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.
We can all agree that typing commands into PyMOL can make pretty and publishable pictures. But your love for PyMOL lasts until you realise there is a mistake and need to re-do it. Or have to iterate over several proteins. And it takes many fiddly commands to get yourself back there (relatable rant over). Recently I was introduced to the useful tool of PML scripting, and for those who have not already discovered this gem please do read on.
These scripts can be called when you launch PyMOL (or from File>Run) and iterate through the commands in the script to adapt the image. This means all your commands can be adjusted to make the figure optimal and allow for later editing.
I have constructed and commented an example script (Joe_Example.pml) below to give a basic depiction of a T4 Lysozyme protein. Here I load the structure and set the view (the co-ordinates can be copied from PyMOL easily by clicking the ‘get view’ command). You then essentially call the commands that you would normally use to enhance your image. To try this for yourself, download the T4 Lysozyme structure from the PBD (1LYD) and running the script (command line: pymol Joe_Example.pml) in the same directory to give the image below.
The image generated by the attached PML script of the T4 Lysozyme (PDB: 1LYD)
#########################
### Load your protein ###
#########################
load ./1lyd.pdb, 1lyd
##########################
### Set your viewpoint ###
##########################
set_view (\
-0.682980239, 0.305771887, -0.663358808,\
-0.392205656, 0.612626553, 0.686194837,\
0.616211832, 0.728826880, -0.298486710,\
0.000000000, 0.000000000, -155.216171265,\
4.803394318, 63.977561951, 106.548652649,\
123.988197327, 186.444198608, 20.000000000 )
#################
### Set Style ###
#################
hide everything
set cartoon_fancy_helices = 1
set cartoon_highlight_color = grey70
bg_colour white
set antialias = 1
set ortho = 1
set sphere_mode, 5
############################
### Make your selections ###
############################
select sampleA, 1lyd and resi 1-20
colour blue, 1lyd
colour red, sampleA
show cartoon, 1lyd
###################
### Save a copy ###
###################
ray 1000,1500
png Lysozyme_Example_Output.png
Enjoy!
MOSAICS is suite of sampling methods for molecular simulations of motion of nucleic acid and protein structures. It’s applicability has been demonstrated in simulating large ensembles of nucleic acids (Sim 2012, Minary 2014) and proteins.
Starting with a protein/dna/rna structure you would like to examine, the basic modus operandi of MOSAICS is divided into three parts:
The energy function defines the energy surface with respect to your degrees of freedom (DoFs) and the sampling methodology is supposed to explore the conformational space along DoFs.
One of the main fortes of MOSAICS lies in the ability of defining hierarchichal natural moves. Defining regions of collective motion introduces experimental knowledge and intuition into the simulations, greatly accelerating sampling. Ability to define such regions was one of the main reasons to start the development of the graphical user interface (GUI) for MOSAICS — preliminary version can be seen in the Figure below.
Overview of PymoSAICS in its current form.
Since we are developing the GUI as a plugin for Pymol, we called it PymoSAICS. The initial focus of the project is on nucleic acids due to our interests in the structural effects of epigenetic modifications. As demonstrated in the Figure above, the GUI is divided into three main panels:
Users can upload their favorite structure via PymoSAICS or Pymol and play with the available parameters. The GUI is also an ongoing effort to streamline the available protocols in MOSAICS to shield the user from the many parameters that are available but perhaps not relevant to the simulation at hand.
We are currently starting beta tests of the application which (if you don’t mind not getting any support just yet) is available here, Therefore, if you are interested in becoming a tester please let me know, and you will receive a version around Easter (April-ish)! Contact me via konrad.krawczyk at dtc.ox.ac.uk