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Google Colab Tutorials for IBM3202

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- - 1fmk_foldingCA_152.pdb
  - 1fmk_foldingCA_152.txt
  - 1ihv_dimer_protPBC.gro
  - 1ihv_dimer_protPBC.xtc
  - 1ihv_mon_protPBC.gro
  - 1ihv_mon_protPBC.xtc
  - 1le3_solv_ions.gro
  - 1le3_solv_ions_noPBC.xtc
  - dualAKE_traj_108.pdb
- - PF01814_rp15.txt
  - PF01814_rp35.txt
  - PF01814_rp55.txt
  - PF01814_rp75.txt
  - PF01814_uniprot.txt
- em.mdp
- ions.mdp
- maketable4.pl
- md.mdp
- mdrun_CA_v5.mdp
- npt.mdp
- nvt.mdp
- sbm_calpha_SA_v5_short.mdp
- WHAMexample.tar.gz
- cm_01.png
- cm_02.png
- cm_03.png
- cm_04.png
- dca_01.png
- dca_02.gif
- dca_03.png
- dca_04.png
- docking_01.png
- docking_02.png
- docking_03.png
- MD_01.png
- MD_02.jpg
- mpmodelling_01.jpg
- mpmodelling_02.jpg
- mpmodelling_03.png
- mpmodelling_04.png
- pdbformat_01.gif
- pdbformat_01.png
- pdbformat_02.png
- pdbformat_03.png
- pdbformat_04.png
- pdbformat_05.png
- phylo_01.png
- phylo_02.png
- phylo_03.png
- phylo_04.png
- phylo_05.png
- phylo_06.png
- pyrosetta_01.png
- pyrosetta_02.png
- pyrosetta_03.png
- pyrosetta_04.png
- pyrosetta_05.png
- pyrosetta_final.png
- sbmdca_01.png
- sbmdca_03.png
- smog_01.png
- smog_02.png
- smog_03.png
- smog_04.png
- smogdual_01.png
- smogdual_02.png
- smogdual_03.jpg
- smogdual_04.png
- smogdual_05.png
- input
- input_files_ai.zip
- input_files_mp.zip
- align2D.py
- build_profile.py
- compare.py
- dcasbm.py
- distavg
- evaluate_model.py
- model-single.py
- scripts
- configure.smog2
- gromacs-2024.tar.gz
- gromacs_sbm.tar.gz
- WARNING
- WHAM.jar
- - lab13_protDCASBM.ipynb
  - README
- - lab15_CF-GROMACS.ipynb
- - lab14_MSAtransformer.ipynb
- FrustraEvo-Colab.ipynb
- lab00_software.ipynb
- lab01_intro.ipynb
- lab02_molviz.ipynb
- lab03_phylo.ipynb
- lab04_cm.ipynb
- lab05_MP_rosetta.ipynb
- lab06_docking.ipynb
- lab07_MDsims.ipynb
- lab08_MDanalysis.ipynb
- lab09_SMOGfolding_docker.ipynb
- lab10_SMOGdual_docker.ipynb
- lab11_rnaDCA.ipynb
- lab12_abinitioRosetta.ipynb
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README.md

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# Cloud-based Tutorials on Structural Bioinformatics
### Institute for Biological and Medical Engineering [(IIBM)](http://iibm.uc.cl), Pontificia Universidad Catolica de Chile
### ANID – Millennium Science Initiative Program – Millennium Institute for Integrative Biology [(iBio)](http://ibio.cl)
##
## 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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab00_software.ipynb) | Installing Software on Google Colab for IBM3202 tutorials (**OBSOLETE**)                           | pyRosetta [1], GROMACS [2], SBM-enhanced GROMACS [3]                                                        |
| Lab.01 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab01_intro.ipynb) | Warm-up on Colab and Brief Review of Biomolecular Databases                         |                                                                                                             |
| Lab.02 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab02_molviz.ipynb) | Visualizing and Comparing Molecular Structures in Google Colab using py3Dmol        | Biopython [4], py3Dmol [5], NGL Viewer [6]                                                                  |
| Lab.03 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab03_phylo.ipynb) | Phylogenetic Analysis using biopython and RAxML                                     | Biopython [4], miniconda [7], MAFFT [8], ModelTest-ng [9], RAxML-ng [10]                                    |
| Lab.04 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab04_cm.ipynb) | Comparative Modeling using MODELLER                                                 | Biopython [4], py3Dmol [5], MODELLER [11]                                                                   |
| Lab.05 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab05_MP_rosetta.ipynb) | Membrane Protein Modelling using PyRosetta                                          | pyRosetta [1], py3Dmol [5]                                                                                  |
| Lab.06 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab06_docking.ipynb) | 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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab07_MDsims.ipynb) | Molecular Dynamics on GROMACS                                                       | GROMACS [2], Biopython [4], py3Dmol [5], NGL Viewer [6]                                                     |
| Lab.08 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab08_MDanalysis.ipynb) | Trajectory Analysis using MDanalysis                                                | py3Dmol [5], MDAnalysis [16]                                                                                |
| Lab.09 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab09_SMOGfolding_docker.ipynb) | 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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab10_SMOGdual_docker.ipynb) | 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](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab11_rnaDCA.ipynb) | Prediction of interactions from the coevolutionary analysis of sequence information | Biopython [4], py3Dmol [5], infernal [17], pyDCA [18]                      |
| Lab.12 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pb3lab/ibm3202/blob/master/tutorials/lab12_abinitioRosetta.ipynb) | 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 for Structural Biology in 2022

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

**Protein Biophysics, Biochemistry and Bioinformatics Lab [(PB<sup>3</sup>)](https://pb3.sitios.ing.uc.cl)**, 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](https://dx.doi.org/10.1021/acs.jchemed.1c00022)

## Contributions and Code of Conduct

Please **read** our rules on [Contributions and Code of Conduct](https://github.com/pb3lab/ibm3202/blob/master/contributions.md) before making any changes.

## References
1. Chaudhury S, Lyskov S, Gray JJ. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics. 2010;26:689–91. doi:10.1093/bioinformatics/btq007.
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.
5. Rego N, Koes D. 3Dmol.js: molecular visualization with WebGL. Bioinformatics. 2015;31:1322–4. doi:10.1093/bioinformatics/btu829.
6. Rose AS, Hildebrand PW. NGL Viewer: a web application for molecular visualization. Nucleic Acids Res. 2015;43:W576–9. doi:10.1093/nar/gkv402.
7. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018;15:475–6. doi:10.1038/s41592-018-0046-7.
8. Katoh K, Standley DM. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol Biol Evol. 2013;30:772–80. doi:10.1093/molbev/mst010.
9. Darriba Di, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. ModelTest-NG: A New and Scalable Tool for the Selection of DNA and Protein Evolutionary Models. Mol Biol Evol. 2020;37:291–4. doi:10.1093/molbev/msz189.
10. Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics. 2019;35:4453–5. doi:10.1093/bioinformatics/btz305.
11. Webb B, Sali A. Comparative Protein Structure Modeling Using MODELLER. Curr Protoc Bioinforma. 2014;47:5.6.1-5.6.32. doi:10.1002/0471250953.bi0506s47.
12. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3:33. doi:10.1186/1758-2946-3-33.
13. Dolinsky TJ, Czodrowski P, Li H, Nielsen JE, Jensen JH, Klebe G, et al. PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res. 2007;35 Web Server:W522–5. doi:10.1093/nar/gkm276.
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.

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