Trajectory Analysis & Scientific Python
Turn raw MD trajectories into real scientific results with MDAnalysis, NumPy, and Matplotlib.
About this course
A hands-on follow-on to Introduction to Molecular Dynamics focused on the analysis side of the pipeline: taking a completed trajectory and extracting the quantitative, publication-style results a real research question demands. You'll work with MDAnalysis to load and select from trajectories, compute RMSD/RMSF and hydrogen bond persistence, and use PCA and clustering to reduce a high-dimensional trajectory down to its dominant conformational states - closing with a full capstone analysis report.
Who it's for
Graduates of Introduction to Molecular Dynamics (or anyone who passes its qualifying exam) who want to move beyond running simulations into rigorously analyzing them - the scientific Python skills that turn a trajectory file into a defensible result.
Prerequisites
Requires Introduction to Molecular Dynamics with GROMACS and CHARMM-GUI - OR solid prior background and hands-on practical experience with MD simulation using GROMACS, proven by scoring at least 85% on the qualifying exam.
Either of the following qualifies you:
- Completed Introduction to Molecular Dynamics with GROMACS and CHARMM-GUI
- Or: solid prior background and hands-on experience with MD simulation using GROMACS, proven by scoring at least 85% on the qualifying exam
Learning outcomes
By the end of this course you will be able to load and select from trajectories with MDAnalysis, compute and correctly interpret RMSD and RMSF, quantify hydrogen bond persistence, run PCA to identify dominant conformational motions, cluster a trajectory into representative states, and assemble the results into a coherent, reproducible analysis report.
Weekly structure
Scientific Python Foundations for MD
Loading a trajectory with MDAnalysis, working with atom selections, and building your first NumPy/Matplotlib analysis plot.
RMSD, RMSF, and Hydrogen Bonds
The three most common quantitative measures of structural stability and flexibility, and how to compute each correctly - including the alignment step RMSD depends on.
Principal Component Analysis and Clustering
Reducing a high-dimensional trajectory down to its dominant modes of motion with PCA, then grouping frames into representative conformational clusters.
- Duration
- 3 weeks
- Commitment
- Approximately 5–7 hours per week, including one scheduled live laboratory session.
- Certificate
- Included
- Cost
- Free
Cohorts
No cohort has been scheduled yet.
No cohort is open for applications right now.