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.
Continue readingAuthor Archives: Joseph Bluck
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.
Young Entrepreneurs Scheme (YES) Competition
Fair warning: I’m going to use this BLOPIG post to promote the YES competition and talk about how semi-amazingly we did at it!
For those who don’t know, the YES competition runs yearly and is designed to develop the entrepreneurial spirit amongst graduates and post-graduates. The YES workshops come in two parts, the first being an intensive crash course in small business start-ups. These are delivered by financial experts, successful start-ups, and intellectual property teams. Carefully mixing theory with useful anecdotes, these talks were hugely insightful and all entusiastically given by people passionate about science start-ups. We were lucky to have many of these speakers mentoring for the second part: the development of our own business plan.
Our team, the fantastically named Team SolOx, developed a licence-selling business for a theoretical catalyst, which mimicked photosynthesis. Our product produced lightweight hydrocarbons from atmospheric gases quickly and efficiently. The comic value of our idea aside, we designed a 10 year business plan that saw SolOx develop and licence our catalyst. This process was eye-opening, with the mentors highlighting the hurdles we would face and taught us how to overcome them. Our pitch landed us a place in the final at the Royal Society in London, as one of the winners of the YES Industrial Challenges workshop 2017. Although the final judging panel didn’t find our plan as financially sound as others, we had a fantastic experience and would thoroughly recommend it to anyone interested in business start-ups.
Team SolOx: Winners of the YES Industrial Challenges 2017 Workshop. Left to right: Natasha Rhys, Tom Dixon, Joe Bluck, Sarah-Beth Amos and Alex Skates.
Finally, I would like to thank the Systems Approaches to Medical Science Centre for Doctoral Training for their financial support and their focus on promoting entrepreneurial skills.
Bitbucket and PyCharm – Tools to make a DPhil less problematic
I find Git a wonderful tool for my work, with version control providing much needed damage control to my projects. I also find Git incredibly powerful at making my working life easier, with the ability to use git push and git pull to synchronise my code between the various computers that I use for my DPhil. Via a BitBucket account, providing a remote Git repository, I am able to move my code around to wherever I am working and allow more room for either more procrastination or staring at my screen in confusion.
As simple as GIT is, it can be a fiddle entering the git commands in command line as well as remembering to do this as you rush to leave the building. This has all been made much easier with PyCharm, from JetBrains. This IDE (integrated development environment) has many tools including version control such as support for a variety of file types, PEP8 checks to ensure good quality code and its ability to work with ipython notebooks.
I’ve put the following mini-tutorial together for those who want to make or bring in an existing repository to PyCharm and get version control working:
Using PML Scripts to generate PyMOL images
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.pngEnjoy!
Physical-chemical property predictors as command line tools
The Instant JChem Suite, from ChemAxon, is a fantastic set of software designed for Chemists. It allows easy and simple database management to store both chemical and non-chemical data. It also contains a plethora of physical-chemical prediction and visualisation tools that can be utilised by chemists and computational based scientists alike.
I personally believe that the hidden gem within the suite is the availability of these predictive tools in the command line, found within the ChemAxon Calculator (cxcalc). In addition, calculator plug ins have also been developed by external developers. This allows you to incorporate the powerful predictive tools of ChemAxon into your larger workflows, with a little scripting.
For example, it can be used to predict the dominant protonation state of a ligand before use in MD or docking studies, with the majormicrospecies tool. You can input and output all major file types including SDF, PDB and MOL2 using commands such as:
cxcalc [Input_File].sdf -o [Output_File].sdf majormicrospecies -H [pH] -f [Output_File_Type]:H
You can easily find the calculator plugins available and how to construct input commands using the cxcalc –h command, or the available online information. I would thoroughly recommend looking at the tools available and how you could incorporate them into your workflows.