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Structural Bioinformatics course by 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

Notes:

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

Practicals:

Task Notebook Topic Software
Lab.01 Open In Colab Warm-up on Colab and Structures Visualisation VMD
Lab.02 Open In Colab Simple Molecular Dynamics simulations GROMACS
Lab.03 Open In Colab More analysis of MD simulations MDanalysis
Lab.04 Open In Colab Enhanced sampling and simplified models PLUMED, Martini
Lab.05 Open In Colab Simple DFT simulations CP2K
Lab.06 Open In Colab Protein Structure Prediction psipred, ColabFold
Lab.07 Open In Colab Molecular Docking Autodock VINA
Lab.08 Open In Colab Protein Design ColabDesign

Reference Papers:

The following publications are to be considered as part of the course and apart from the optional one should be studied for the exam. The one indicated as optional are left to the students that want to get more into the details of a specific topic.

  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. (optional, learn more) Unsupervised Learning Methods for Molecular Simulation Data: Glielmo A., et al. (2021) Chem. Rev. 121:9722–9758 https://doi.org/10.1021/acs.chemrev.0c01195
  4. (optional, learn more) QM/MM Methods for Biomolecular Systems: Senn H.M., Thiel W. (2009) Angew. Chem. Int. 48:1198–1229 https://doi.org/10.1002/anie.200802019
  5. 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
  6. 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
  7. (optional, learn more) Principles of protein structural ensemble determination: Bonomi M., et al. (2017) Curr. Op. Struct. Biol. 42:106–116 http://dx.doi.org/10.1016/j.sbi.2016.12.004

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