Structural Bioinformatics

Carlo Camilloni, Department of Biosciences, University of Milano, Italy

Structural Bioinformatics is an elective course for the master degrees in Molecular Biotecnology and Bioinformatics (MBB) and Quantitative Biology (QB). The aim is that of providing a general introduction to different computational approaches related to computational structural biology and biochemistry. This repository includes both the notes and the practicals. News about the course are published on the ARIEL web page.

The main topics covered are:

1. Structures visualisation and analysis
2. Molecular Dynamics simulations (including QM)
3. Biomolecules Structure Prediction (including docking)
4. Advanced topics: integrative structural biology and protein design

The repository is organised as follow:

Notes     : Slides of the lectures in PDF format
Notebooks : Colab Notebooks for the practicals
Data      : Additional files needed for the practicals

For each academic year, a snapshot of the repository is saved as a release.

Notes:

Lecture	Topic	Last Updated
	Introduction: information about the course	09/2023
	Structural Biology and Structure Visualisation	09/2023
	A Statistical Mechanics view of Biomolecular Dynamics	09/2023
	Molecular Dynamics simulations: force-fields, algorithms, analysis	10/2023
	Enhanced Sampling Techniques in MD	10/2023
	Markov State Models (by T. Giorgino)	11/2023
	Quantum Chemistry, QM/MM, and simplified models	11/2023
	Machine Learning (by T. Giorgino)	2022
	Structures Prediction and Molecular Docking	2022
	Integrative Modelling and Protein Design	2022

Practicals:

Task	Instructions	Report	Last Updated
T01: Biomolecular Structures Visualisation			09/2023
T02: Basic statistical analysis			10/2023
T03: Molecular Dynamics simulations			10/2023
T04: More on the analysis of MD simulations			10/2023
T05: Enhanced sampling and simplified models			11/2023
T06: Basic DFT simulations			11/2023
T07: Protein Structure Prediction			2022
T08: Molecular Docking			2022
T09: Protein Design			2022

Reference Papers:

The following publications are to be considered as part of the course and should be studied for the exam.

1. 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
2. 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
3. Toward the solution of the protein structure prediction problem: Pearce R., Zhang Y. (2021) J. Biol. Chem. 297:100870 https://doi.org/10.1002/anie.200802019
4. Principles for Integrative Structural Biology Studies: Rout P.M., Sali A. (2019) Cell 177:1384-1403 https://doi.org/10.1016/j.cell.2019.05.016

Exam:

The exam consists of a powerpoint presentation (max 10 minutes) of a scientific paper from a list provided below, this will be followed by few questions regarding the paper and the methodologies that we have dealt with in the lectures. Finally we will go through one of the Tasks performed in the lab by looking at the materials you produced.

Papers list: (Updated Jan 2023)

1. Structural Basis of AZD9291 Selectivity for EGFR T790M: Yan X., et al. (2020) J. Med. Chem. 63, 8502−8511 https://dx.doi.org/10.1021/acs.jmedchem.0c00891
2. Breathing and Tilting: Mesoscale Simulations Illuminate Influenza Glycoprotein Vulnerabilities: Casalino L., et al. (2022) ACS Cent. Sci. 8, 1646−1663 https://doi.org/10.1021/acscentsci.2c00981
3. Ion Conduction Mechanism as a Fingerprint of Potassium Channels: Domene C., et al. (2021) J. Am. Chem. Soc. 143, 12181−12193 https://doi.org/10.1021/jacs.1c04802
4. Dynamic and Electrostatic Effects on the Reaction Catalyzed by HIV‐1 Protease: Krzeminśka A., et al. (2016) J. Am. Chem. Soc. 138, 16283−16298 https://dx.doi.org/10.1021/jacs.6b06856
5. AI-based structure prediction empowers integrative structural analysis of human nuclear pores: Mosalaganti S., et al. (2022) Science 376, eabm9506 https://doi.org/10.1126/science.abm9506
6. Molecular Basis of Small-Molecule Binding to α‐Synuclein: Robustelli P., et al. (2022) J. Am. Chem. Soc. 144, 2501−2510 https://doi.org/10.1021/jacs.1c07591
7. De novo protein design by deep network hallucination: Anishchenko I., et al. (2021) Nature 600, 547–552 https://doi.org/10.1038/s41586-021-04184-w
8. Multi-eGO: An in silico lens to look into protein aggregation kinetics at atomic resolution: Scalone E., et al. (2022) PNAS, e2203181119 https://doi.org/10.1073/pnas.2203181119