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Cloud-based Tutorials on Structural Bioinformatics

Institute for Biological and Medical Engineering (IIBM), Pontificia Universidad Catolica de Chile

ANID – Millennium Science Initiative Program – Millennium Institute for Integrative Biology (iBio)

Introduction

This is a set of twelve (12) tutorials on protein folding, function, structure, dynamics and evolution for distance learning using the Google Colab free cloud-computing environment.

These tutorials were created between Jun-Sep 2018 as part of the IBM3202 Molecular Modelling and Simulation module for execution of standalone computers and then fully redesigned between Jun-Jul 2020 for full execution over Google Colab and remote accesibility via web browsers due to the COVID-19 pandemic.

Each tutorial includes a brief introduction of the activities to be performed, installation instructions of the open-source software to be used in each session and several programming, visualization and data analysis activities to be achieved during the tutorial.

Description of the Tutorials

The following is a brief description of each tutorial, along with the open-source software used for each task:

Tutorial Description Software
Lab.00 Open In Colab Installing Software on Google Colab for IBM3202 tutorials (OBSOLETE) pyRosetta [1], GROMACS [2], SBM-enhanced GROMACS [3]
Lab.01 Open In Colab Warm-up on Colab and Brief Review of Biomolecular Databases
Lab.02 Open In Colab Visualizing and Comparing Molecular Structures in Google Colab using py3Dmol Biopython [4], py3Dmol [5], NGL Viewer [6]
Lab.03 Open In Colab Phylogenetic Analysis using biopython and RAxML Biopython [4], miniconda [7], MAFFT [8], ModelTest-ng [9], RAxML-ng [10]
Lab.04 Open In Colab Comparative Modeling using MODELLER Biopython [4], py3Dmol [5], MODELLER [11]
Lab.05 Open In Colab Membrane Protein Modelling using PyRosetta pyRosetta [1], py3Dmol [5]
Lab.06 Open In Colab Molecular Docking on Autodock Biopython [4], py3Dmol [5], miniconda [7], Open Babel [12], pdb2pqr [13], MGLTools [14], Autodock Vina [15]
Lab.07 Open In Colab Molecular Dynamics on GROMACS GROMACS [2], Biopython [4], py3Dmol [5], NGL Viewer [6]
Lab.08 Open In Colab Trajectory Analysis using MDanalysis py3Dmol [5], MDAnalysis [16]
Lab.09 Open In Colab Folding Simulations using Structure-Based Models SMOG2 Docker, udocker, SBM-enhanced GROMACS [3], Biopython [4], py3Dmol [5], NGL Viewer [6]
Lab.10 Open In Colab Conformational changes using Structure-Based Models SMOG2 Docker, udocker, SBM-enhanced GROMACS [3], Biopython [4], py3Dmol [5], NGL Viewer [6]
Lab.11 Open In Colab Prediction of interactions from the coevolutionary analysis of sequence information Biopython [4], py3Dmol [5], infernal [17], pyDCA [18]
Lab.12 Open In Colab Protein folding ab initio using Rosetta pyRosetta [1], Biopython [4], py3Dmol [5]

Tutorials – 2021 & 2023

The following is a brief description of each tutorial generated in 2021 & 2023, along with the open-source software used for each task:

Tutorial Description Software
Lab.13 Open In Colab Combining DCA and SBM to predict protein structures SMOG2, SBM-enhanced GROMACS [3], Biopython [4], py3Dmol [5], pyDCA [18]
Lab.14 Open In Colab Combining MSA Transformer and SBM to predict protein structures SMOG2, SBM-enhanced GROMACS [3], Biopython [4], py3Dmol [5], MSA Transformer

Tutorial for Structural Biology in 2022

Tutorial Description Software
Lab.15 Open In Colab Combining ColabFold and GROMACS to predict and simulate protein structures GROMACS [2], Biopython [4], py3Dmol [5], NGL Viewer [6], ColabFold [19]

Authors

Felipe Engelberger, Pablo Galaz-Davison, Graciela Bravo, Maira Rivera and César A. Ramírez Sarmiento.

Protein Biophysics, Biochemistry and Bioinformatics Lab (PB3), Institute for Biological and Medical Engineering (IIBM) / Millenium Institute for Integrative Biology (iBio)

Cite us!

If you use these tutorials in your research/teaching, please cite us!:

Engelberger F, Galaz-Davison P, Bravo G, Rivera M, Ramírez-Sarmiento CA (2021) Developing and Implementing Cloud-Based Tutorials that Combine Bioinformatics Software, Interactive Coding and Visualization Exercises for Distance Learning on Structural Bioinformatics. J Chem Educ 98(5): 1801-1807. doi: 10.1021/acs.jchemed.1c00022

Contributions and Code of Conduct

Please read our rules on Contributions and Code of Conduct before making any changes.

References

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  2. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25. doi:10.1016/j.softx.2015.06.001.
  3. Noel JK, Levi M, Raghunathan M, Lammert H, Hayes RL, Onuchic JN, et al. SMOG 2: A Versatile Software Package for Generating Structure-Based Models. PLOS Comput Biol. 2016;12:e1004794. doi:10.1371/journal.pcbi.1004794.
  4. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25:1422–3. doi:10.1093/bioinformatics/btp163.
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  14. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785–91. doi:10.1002/jcc.21256.
  15. Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61. doi:10.1002/jcc.21334.
  16. Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J Comput Chem. 2011;32:2319–27. doi:10.1002/jcc.21787.
  17. Nawrocki EP, Kolbe DL, Eddy SR. Infernal 1.0: inference of RNA alignments. Bioinformatics. 2009;25:1335–7. doi:10.1093/bioinformatics/btp157.
  18. Zerihun MB, Pucci F, Peter EK, Schug A. pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences. Bioinformatics. 2020;36:2264–5. doi:10.1093/bioinformatics/btz892.
  19. Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nature Methods. 2022 May 30:1-4. doi:10.1038/s41592-022-01488-1.