PyConDE & PyData Berlin 2024

Proteins are one of the main building blocks of the living world. They are largely responsible for the amazing diversity that we witness in the nature around us. Although proteins are composed of sequences of just 20 amino acids, clever nature’s design has endowed them with an incredibly diverse set of functions. It is not an overstatement to say that this diversity and the myriad of ways proteins interact with each other is at the very heart of life. Therefore it is of utmost importance to understand their structure and function.
Proteins are very large molecules, composed of thousands up to even millions of atoms connected in a giant hairball like structures. But still they are too tiny to be seen by any sort of microscope, even the most powerful ones. That is why in order to “see” how they look we use X-rays and shine them on crystals made entirely of single proteins species in the fascinating method of X-ray crystallography. It then gives us the picture of how the proteins look to unprecedented atomic detail.
In order to do their function proteins also move their parts, but unfortunately this motion is too quick to be seen by any device. X-ray crystallography alone, although mighty in giving us the details, gives us only one static image. It is a bit like trying to tell a story of a movie just by seeing a movie poster. Therefore we have to simulate the motion of the protein by so-called molecular dynamics (MD) simulations. Basically we give the computer the initial positions of all the atoms that we know from X-ray crystallography and then kick them and see how the protein moves in time, in very tiny steps. This results in so-called MD trajectories which contain all atom positions in millions of steps. Needles to say that this results in super heavy data that usually contains hundreds of GB of data that needs to be processed somehow.
In the project called “Allosteric communication pathways in oligomeric enzymes” (https://alokomp.irb.hr/) we have faced that very problem. How to extract information about protein movement from such enormous quantities of data? Of course the answer was using marvelous Python suite of tools available. Python has established itself as a de facto standard programming language in data science, and with already available plethora of options for X-ray crystallography and MD analysis it was a logical choice (not to mention its awesomeness and being our favourite anyway). The whole project really displays how mature and diverse Python is to be able to tackle every single aspect of such a specialized problem. To begin with, we have centered the entire project around a web page built using Django. It serves both as a front-end wih general information, but also as a web app for diving into the data. Behind it is a PostgreSQL relational database containing all the structural and derived data from a family of proteins, called PNPs, which serve as sort of proof of concept (https://alokomp.irb.hr/pdbase/structures/). It also contains data derived from MD simulations and analysed with MDanalysis tool (https://www.mdanalysis.org/). It is hard to mention all the Python tools we have used for analysis of the data in the database. Of course the backbone of it are indispensable Pandas, Numpy, Scipy, Dask, Jupyther, NetworkX, Bokeh, HoloViz to name but a few. More specifically we have developed a special approach (“avocado” plots, example https://alokomp.irb.hr/md/avocados/1458/A) to visualize the motion of protein as a whole in time, as a series of snapshots each containing plots of millions of points, using awesome Datashader library (https://datashader.org). We have also used Ruptures (https://github.com/deepcharles/ruptures) library to detect changes in the positions of protein and to detect correlations. Everything is wrapped up in a form of interactive web app which can be used to visually browse vast amounts of data, giving a whole new perspective on a highly complex multidimensional data.