pb3lab/ibm3202

Google Colab Tutorials for IBM3202

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. The only exception to this description is constituted by the installation of software for MD simulations and protein structure prediction, which have to be installed before starting the tutorials. Therefore, we created an additional tutorial for installation of this software.

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

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.

ibm3202's Issues

Colab Crashed during running the lab 6:

This course error during run
!conda install -c conda-forge -c bioconda mgltools openbabel zlib --yes

I think there is issue between python 2.7 and python 3 in colab notebook: I tried not unable to resolve: Showing following error

/bin/bash: /usr/local/lib/libtinfo.so.5: no version information available (required by /bin/bash)
Traceback (most recent call last):
  File "/usr/local/bin/pip", line 7, in <module>
    from pip import main
ImportError: No module named pip

Collab can't run Gromacs

I have done a handful of MD simulations with this method that you have kindly provided for us, and I was doing the last one that this thing happened. And I don't know how to solve it. I really need this. I'm doing my thesis with it, It would be so great if you could help me.
the error is:
gmx: error while loading shared libraries: libhwloc.so.5: cannot open shared object file: No such file or directory

Trying to run the conda environment on the cluster (PBS Pro)

Hello!
Thanks for making the tutorials available!

I'm trying to make a pipeline in python that will make use of MGL Tools as part of the process.
I'm trying to:
(A) Model mutations in a protein
(B) Energy minimize the protein
(A and B in schrodinger).
(C) Prepare the protein in MGL Tools (to add gasteiger charges)
(D) Dock ligands to the mutated proteins using Autodock Vina.

So far, I've managed (A) and (B).
But for C, I'm trying to use the code you have from this tutorial, where you install the conda package of MGLTools in your conda envrionment.
I was able to do that.

However, when I try to run a job on the cluster (PBS Pro), I'm not able to access my conda environment that I created.
Therefore, I can't run the python scripts from MGL Tools.

Any thoughts/ advice?

Regards

Jeremy Burgess

How did you compile gromacs in colab?

May I please know the procedure to compile gromacs in colab?
Because I wanted to compile it in gradient or kaggle.
Thank you