How Python helped us uncover secrets of protein motion [PyCon DE & PyData Berlin 2024]

Python enabled analysis of complex protein motion data through molecular dynamics (MD) simulations, generating ~500GB of data per simulation

Key Python libraries used included:

• DataShader for handling massive point plotting (400k points per plot)
• Ruptures for detecting state changes in protein motion
• NetworkX for correlation analysis and graph visualization
• MD Analysis for processing simulation data
• Django for web application interface

Each protein simulation:

• Runs for 10 days on modern GPU
• Simulates 1 microsecond of protein motion
• Generates 400,000 timesteps
• Produces ~500GB of raw data

Protein motion analysis focused on:

• Tracking phi/psi angles of amino acids over time
• Identifying correlated movements between different amino acids
• Visualizing state changes and conformational shifts
• Compressing massive datasets into interpretable visualizations

Novel visualization approach:

• Used Ramachandran plots for each amino acid over time
• Created time-series GIFs showing protein motion
• Implemented interactive browsing of amino acid correlations
• Automated detection of conformational changes

Project demonstrated Python’s versatility in:

• Processing large scientific datasets
• Creating interactive visualizations
• Building web interfaces for data exploration
• Integrating multiple specialized scientific libraries
• Handling relational and graph databases

Study focused on Helicobacter pylori protein PMP:

• Hexameric structure
• Important pathogen affecting 50% of world population
• Shows complex conformational changes during substrate binding