Molecular Modelling and Simulation on Google Colab

Overview

Welcome to Molecular Modelling and Simulation on Google Colab! This course offers hands-on training in computational techniques used to study the structure, dynamics, and interactions of biological macromolecules. By leveraging the power of the free, cloud-based Google Colab platform, this course makes advanced molecular modelling and simulation methods accessible to everyone, regardless of local computing resources. We will explore a wide range of essential bioinformatics tools and workflows through a series of practical tutorials.

---

Target Audience

This course is designed for undergraduate and graduate students, researchers, and professionals in structural biology, bioinformatics, computational chemistry, biophysics, pharmacy, and related life science fields who wish to gain practical skills in molecular modelling and simulation. Some familiarity with basic biological and chemical concepts is recommended. While programming experience (especially Python) is helpful, the tutorials are designed to be accessible via the user-friendly Colab interface.

---

Learning Objectives

Upon successful completion of this course, participants will be able to:

• Set up and manage common bioinformatics software within the Google Colab environment.
• Visualize, manipulate, and compare biomolecular structures using tools like py3Dmol and NGL Viewer.
• Perform phylogenetic analyses using standard bioinformatics pipelines (MAFFT, ModelTest-NG, RAxML-NG).
• Build 3D protein models using comparative modelling techniques (MODELLER).
• Utilize PyRosetta for modelling tasks, including membrane proteins and ab initio folding.
• Conduct molecular docking studies to predict ligand binding using AutoDock Vina.
• Set up, run, and analyze conventional molecular dynamics (MD) simulations using GROMACS.
• Analyze MD simulation trajectories effectively using MDAnalysis.
• Employ structure-based models (SBMs) with SMOG2 to simulate protein folding and conformational changes.
• Use coevolutionary sequence analysis (DCA) to predict molecular interactions.
• Integrate cutting-edge deep learning methods (ColabFold, MSA Transformer) with simulation and modelling pipelines.

---

Course Content

The course is structured as a series of hands-on labs covering fundamental and advanced topics:

1. Foundations: Introduction to Google Colab, accessing biomolecular databases, and fundamentals of molecular visualization and structural comparison (Labs 01, 02). (Note: Lab 00 on specific installation methods is obsolete, but software setup is integrated into relevant labs).
2. Sequence Analysis & Evolution: Building phylogenetic trees from sequence data (Lab 03).
3. Structure Prediction & Modelling: Comparative modelling with MODELLER (Lab 04), membrane protein modelling (Lab 05) and ab initio folding with PyRosetta (Lab 12).
4. Molecular Interactions: Predicting protein-ligand binding through molecular docking (Lab 06) and inferring interactions from sequence coevolution (Lab 11).
5. Molecular Simulation: Running and analyzing classical MD simulations with GROMACS and MDAnalysis (Labs 07, 08). Simulating folding and conformational transitions using structure-based models with SMOG2 (Labs 09, 10).
6. Integrative & Advanced Approaches: Combining coevolutionary data (DCA) or deep learning embeddings (MSA Transformer) with structure-based models (Labs 13, 14). Integrating state-of-the-art structure prediction (ColabFold) with MD simulations (Lab 15).

---

Methodology

This course employs a practical, hands-on approach using interactive Google Colab notebooks. Each tutorial guides participants through specific tasks, running analyses in real-time and visualizing results directly within the browser. We utilize widely adopted, open-source software packages prevalent in the field.

---

Key Software Utilized

GROMACS, PyRosetta, MODELLER, AutoDock Vina, MDAnalysis, SMOG2, ColabFold, Biopython, py3Dmol, NGL Viewer, MAFFT, ModelTest-NG, RAxML-NG, Open Babel, PDB2PQR, MGLTools, pyDCA, Infernal, and others via Miniconda/Bioconda.

---

Outcome

Participants will gain valuable practical experience in applying diverse molecular modelling and simulation techniques using accessible, state-of-the-art tools within the Google Colab framework, empowering them to tackle complex biological questions computationally.

---

Contents

The course consists of a series of tutorials, each focusing on a specific aspect of molecular modelling and simulation. The tutorials are designed to be run in Google Colab, allowing for easy access and execution without the need for local installations. Each tutorial is self-contained, providing step-by-step instructions and explanations.

Labs	Description	Software
Lab.00	Installing Software on Google Colab	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 Docker, udocker, SBM-enhanced GROMACS [3], Biopython [4], py3Dmol [5], NGL Viewer [6]
Lab.10	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]
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
Lab.15	Combining ColabFold and GROMACS to predict and simulate protein structures	GROMACS [2], Biopython [4], py3Dmol [5], NGL Viewer [6], ColabFold [19]

---

Software 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;19:679–682. doi:10.1038/s41592-022-01488-1.

---

Tutorial References

1. Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1, 19-25. doi:10.1016/j.softx.2015.06.001.
2. Arantes, P. R., Polêto, M. D., Pedebos, C., & Ligabue-Braun, R. (2021). Making it Rain: Cloud-Based Molecular Simulations for Everyone. Journal of Chemical Information and Modeling, 61(10), 4852–4856. doi:10.1021/acs.jcim.1c00998.
3. Engelberger, F., Galaz-Davison, P., Bravo, G., Rivera, M., & Ramírez-Sarmiento, C. A. (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.
4. Gowers, R. J., Linke, M., Barnoud, J., Reddy, T. J. E., Melo, M. N., Seyler, S. L., Dotson, D. L., Domanski, J., Buchoux, S., Kenney, I. M., & Beckstein, O. (2016). MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations. In S. Benthall & S. Rostrup (Eds.), Proceedings of the 15th Python in Science Conference (pp. 98-105). Austin, TX: SciPy. doi:10.25080/majora-629e541a-00e.
5. Lemkul, J. A. (2019). From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0]. Living J. Comput. Mol. Sci., 1(1), 5068. doi:10.33011/LIVECOMS.1.1.5068.