Carlo Camilloni, Department of Biosciences, University of Milano, Italy
Structural Bioinformatics is an elective course offered within the Master's programmes in Molecular Biotechnology and Bioinformatics (MBB) and Quantitative Biology (QB). The course introduces a range of computational approaches for modeling and designing biomolecular structures, dynamics, and functions. This repository provides both lecture notes and laboratory exercises, while course updates and announcements will be posted on the ARIEL platform.
- Structures visualisation and analysis
- Biomolecules structure prediction
- Molecular dynamics simulations
- Integrative modelling and protein design
All practicals run as Google Colab notebooks, so no local installation is required — just a Google account. Click any "Open in Colab" badge below to launch a notebook directly.
If you prefer to run a practical locally, you will need VMD for T01 and a working conda/pip environment with the packages imported at the top of each notebook (typically numpy, matplotlib, MDAnalysis, and biopython). The Data/ folder contains all input files referenced by the notebooks.
Notes : Slides of the lectures in PDF format
Notebooks : Colab Notebooks for the practicals (t0X_*.ipynb) and their report templates (report_X_*.ipynb)
Data : Additional input files needed for the practicals
docking/ : protein and ligand structures for the docking exercise (T04)
martini/ : Martini coarse-grained force field and topology files (T07)
md/ : GROMACS .mdp parameter files and PLUMED metadynamics inputs (T05–T07)
qm/ : small-molecule structures and reference output for the QM exercise (T08)
stats/ : datasets for the statistical analysis exercise (T02)
For each academic year, a snapshot of the repository is saved as a release/tag (see Previous years below).
| # | Topic | Slides | Notes | Last Updated |
|---|---|---|---|---|
| 0 | Introduction: information about the course |  |
09/2026 | |
| 1 | Structural Biology beyond static structures |  |
|
09/2026 |
| 2 | A Statistical Mechanics view of Biomolecular Dynamics | |
10/2025 | |
| 3 | Machine Learning (by T. Giorgino) | |
10/2025 | |
| 4 | Structures Prediction and Molecular Docking | |
10/2025 | |
| 5 | Molecular Dynamics simulations: force-fields, algorithms, analysis | |
11/2025 | |
| 6 | Enhanced Sampling Techniques in MD | |
11/2025 | |
| 7 | Markov State Models (by T. Giorgino) | |
11/2025 | |
| 8 | Quantum Chemistry, QM/MM, and simplified models | |
12/2025 | |
| 9 | Integrative Modelling and Protein Design | |
12/2025 |
| Task | Instructions | Report | Last Updated |
|---|---|---|---|
| T01: Biomolecular Structures Visualisation | |
 |
10/2025 |
| T02: Basic statistical analysis | |
|
10/2025 |
| T03: Protein Structure Prediction | |
|
10/2025 |
| T04: Molecular Docking | |
|
10/2025 |
| T05: Molecular Dynamics simulations | |
|
10/2025 |
| T06: More on the analysis of MD simulations | |
|
11/2025 |
| T07: Enhanced sampling and simplified models | |
|
11/2025 |
| T08: Basic DFT simulations | |
|
11/2025 |
| T09: Protein Design | |
|
11/2025 |
The following publications are to be considered as part of the course and should be read before the exam.
- Seeing the PDB: Richardson J.S., Richardson R.C., Goodsell D.S. (2021) J. Biol. Chem. 296:100742. https://doi.org/10.1016/j.jbc.2021.100742
- Biomolecular Simulation: A Computational Microscope for Molecular Biology: Dror R.O., et al. (2012) Annu. Rev. Biophys. 41:429-452. https://doi.org/10.1146/annurev-biophys-042910-155245
- Toward the solution of the protein structure prediction problem: Pearce R., Zhang Y. (2021) J. Biol. Chem. 297:100870. https://doi.org/10.1016/j.jbc.2021.100870
The exam consists of a PowerPoint presentation (max 10 minutes) of a scientific paper from the list below, followed by a few questions on the paper and the methods we have covered in the lectures. Lab reports will also contribute to the final grade. See the introductory slide above for more information.
List of papers (academic year 2026/2027): To be announced
Each past edition of the course is preserved as a git tag:
This repository is released under the MIT License. For questions about the course, please use the ARIEL platform's discussion board or contact Carlo Camilloni directly.