Course material for the Computational Graphics course ("Computergrafik") at the University of Vienna, taught by Stefan Boresch.
The material consists of Jupyter notebooks (R kernel) plus the input files, force-field parameters, and coordinate files needed to run the accompanying CHARMM calculations and VMD exercises on the course's JupyterHub server.
If you're a University of Vienna student enrolled in this course: use the link provided on the course's Moodle page instead of the instructions below -- it handles both the initial setup and later updates automatically, with no git commands needed at all.
The instructions below are for anyone else who wants to get this material directly from GitHub.
Open a terminal in JupyterHub and run:
cd ~/work
git clone https://github.com/sboresch/comp-grafik.git
No GitHub account or SSH key is needed for this — it's a plain, read-only download.
This creates ~/work/comp-grafik/, containing the folders below. All paths
given inside the notebooks assume the repository was cloned there.
If I push fixes or additions later in the semester, see GETTING_UPDATES.md for how to pull them -- and what to do if git pull refuses, which can happen even without you having changed anything (Jupyter does that on its own sometimes).
- Work through the VMD tutorial (at least "Working with a single molecule" up to "Creating multiple representations").
more_vmd/— a few warm-up exercises (on triosephosphate isomerase, PDB 5TIM) that test your comprehension of the tutorial material and prepare you for the final exercise below.glutamate_switch/— the final VMD exercise: visualizing two conformations of a real protein (a voltage-dependent phosphatase). Document your solution (pictures, script/state files, a short write-up).
Occasionally you'll download files from here for visualization with VMD.
charmm-stuff/— a basic introduction to CHARMM scripting, with first exercises in energy minimization. Do this one first.
The remaining four can be done in whatever order suits you:
mc_tests/— illustrative examples of Monte Carlo move types.implicit_solvent_md/— a basic MD exercise (implicit-solvent simulation of a protein, with standard trajectory analyses).ala_dipeptide/— minimization-based dihedral (Φ/Ψ) scan of alanine dipeptide, then the same system revisited with MC and MD.glutamate_switch_2/— revisiting the glutamate switch: estimating the pKa of Glu411 in the two structures and building a solvated simulation system with CHARMM-GUI.
Each folder's own notebook has the detailed instructions and the exact TODO list for that task. (Deadlines and upload instructions for a given course run are posted on Moodle, not here.)
This material is licensed under CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike) -- see LICENSE. In short: share and adapt freely with credit, not for commercial use, and release any adaptations under the same license.