LABODOCK hosts a collection of Jupyter Notebooks that provides straightforward approach to molecular docking on Google Colab with minimal coding proficiency. Through leveraging well-established cheminformatic tools and Google Colab's cloud computing capabilities, this repository aimed to streamline the entire molecular docking protocols, automating various pre- and post-docking processes for seamless, intuitive and interactive in-silico experimentation. Current available notebooks are:

• Basic Molecular Docking
• Virtual Screening

• Intuitive and user-friendly form field
• Autodock Vina-driven molecular docking operation
• PLIP-integrated binding interaction analysis with bar chart
• ✨ Automated docking result clustering: **
  • Best-Pose: Pose with best docking score from each ligand
  • LABO-RMSD: Pose with lowest LABO-RMSD from each ligand
• ✨ Six grid box defining methods:
  • LaBOX
  • eBoxSize
  • eBoxSize-Mod
  • Autodock-Grid
  • Manual-Mode
  • Defined-by-Res
• ✨ Three RMSD calculation methods:
  • LABO_RMSD: Based on RDKit's maximum common substructure search
  • HUNG_RMSD: Based on spyrmsd's Hungarian algorithm calculation
  • SYMM_RMSD: Based on spyrmsd's symmetrical-corrected calculation
• ✨ Maximum common substructure PNG generation
• ✨ 3D basic informative molecular visualization with colour scale:
  • Hydrophobicity scale (Kyte and Doolittle, 1982)
  • Isoelectric points scale

** Exclusive for virtual screening protocol.

BMD Redocked Fenebrutinib (red) Superimposed on 9AJ (gray) with PDB 5VFI

BMD Docking Scores and RMSDs of Redocked Fenebrutinib

VS Docked CHEMBL161052 in Slab View with PDB 4PH9 sagittal section

VS PLIP Binding Interaction Frequency Bar Chart

VS Top 10 Poses with Lowest LABO-RMSD from each ligands

• These notebooks are designed for Google Colab and may not work on other platform.
• These notebooks provide a simple pipeline for illustrating molecular docking and do not necessarily reflect the standard protocol.

1. Adasme, M. F., Linnemann, K. L., Bolz, S. N., Kaiser, F., Salentin, S., Haupt, V. J., & Schroeder, M. (2021). PLIP 2021: Expanding the scope of the protein–ligand interaction profiler to DNA and RNA. Nucleic Acids Research, 49(W1), Article W1. https://doi.org/10.1093/nar/gkab294
2. Feinstein, W. P., & Brylinski, M. (2015). Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. Journal of Cheminformatics, 7, 18. https://doi.org/10.1186/s13321-015-0067-5
3. Meli, R., & Biggin, P. C. (2020). spyrmsd: Symmetry-corrected RMSD calculations in Python. Journal of Cheminformatics, 12(1), 49. https://doi.org/10.1186/s13321-020-00455-2
4. O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33. https://doi.org/10.1186/1758-2946-3-33
5. Seshadri, K., Liu, P., & Koes, D. R. (2020). The 3Dmol.js learning environment: A classroom response system for 3D chemical structures. Journal of Chemical Education, 97(10), 3872–3876. https://doi.org/10.1021/acs.jchemed.0c00579
6. Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. (Journal of Computational Chemistry*, 31(2), Article 2. https://doi.org/10.1002/jcc.21334

Copyright (c) 2023 Ryan Loke
Distributed under the MIT License.
See LICENSE file for more information.