/ColabMDA

User friendly molecular dynamics simulation combining GROMACS and Google Colab: A complete guide

Primary LanguageJupyter NotebookMIT LicenseMIT

Update 2024 [Completed]

  1. Colab gmx Installation Open In Colab Installing GROMACS 2023x for molecular dynamics simulations in Colab.

  2. Colab MD Simulation 2024 Open In Colab Performing molecular dynamics simulation with GROMACS in Google Colab.

  3. Colab MD Analysis Open In Colab Using MDAnalysis and MDtraj to perform MD trajectory Analysis.

Summary

This Github repository is opened to facilitate Molecular Dynamics (MD) simulations using GROMACS. It serves as an adaptable jupyter notebook for conducting MD simulations and accompanies the supplimentary file for the follwoing Article:

Molecular Dynamics Simulation of Wild and Mutant PSMB8 Protein Using Google Collaboratory Framework: Implications for the Restoration of Inflammation in Experimental Autoimmune Encephalomyelitis (EAE) Pathogenesis** ([link here](link will be updated asap)).

Repository link: ColabMDA: https://github.com/paulshamrat/ColabMDA

Note and Acknowledgement:

We would like to thanks the authors who developed jupyter notebook framework for molecular dynamics simulation on google colab. Please always refer the original GROMACS manual for the simulaiton guide. We are grateful to the Authors of the follwoing article which made possible to adapt this md simulation protocol.

  • F. Engelberger, P. Galaz-Davison, G. Bravo, M. Rivera, and C. A. Ramírez-Sarmiento, “Developing and Implementing Cloud-Based Tutorials That Combine Bioinformatics Software, Interactive Coding, and Visualization Exercises for Distance Learning on Structural Bioinformatics,” J. Chem. Educ., vol. 98, no. 5, pp. 1801–1807, May 2021, doi: 10.1021/acs.jchemed.1c00022.

  • J. A. Lemkul, “From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0],” Living J. Comput. Mol. Sci., vol. 1, no. 1, pp. 5068–5068, 2019, doi: 10.33011/LIVECOMS.1.1.5068.

  • P. R. Arantes, M. D. Polêto, C. Pedebos, and R. Ligabue-Braun, “Making it Rain: Cloud-Based Molecular Simulations for Everyone.,” Journal of chemical information and modeling, vol. 61, no. 10. United States, pp. 4852–4856, Oct. 2021. doi: 10.1021/acs.jcim.1c00998.

  • R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein. MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations. In S. Benthall and S. Rostrup, editors, Proceedings of the 15th Python in Science Conference, pages 98-105, Austin, TX, 2016. SciPy, doi:10.25080/majora-629e541a-00e.

  • Gowers, R. J., Linke, M., Barnoud, J., Reddy, T. J., Melo, M. N., Seyler, S. L., ... & Beckstein, O. (2016, July). MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. In Proceedings of the 15th python in science conference (Vol. 98, p. 105). Austin, TX: SciPy.

  • 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.

All these datasets are uploaded as Dataset of Molecular Dynamics Simulations for the Upregulated Biomarker PSMB8: 3UNF and its G210V Mutant in Experimental Autoimmune Encephalomyelitis.

DOI

Last tested on: 2024-01-21