Vina: Difference between revisions
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|download = https://raw.githubusercontent.com/Pymol-Scripts/Pymol-script-repo/refs/heads/master/plugins/vina.py | |download = https://raw.githubusercontent.com/Pymol-Scripts/Pymol-script-repo/refs/heads/master/plugins/vina.py | ||
|author = Pedro Sousa Lacerda | |author = Pedro Sousa Lacerda | ||
|email = pslacerda@gmail.com | |||
|license = Free Software | |license = Free Software | ||
}} | }} | ||
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= Author = | = Author = | ||
Pedro Sousa Lacerda. | Pedro Sousa Lacerda <pslacerda@gmail.com> | ||
Universidade Federal da Bahia |
Revision as of 10:29, 19 January 2025
Type | PyMOL Plugin |
---|---|
Download | https://raw.githubusercontent.com/Pymol-Scripts/Pymol-script-repo/refs/heads/master/plugins/vina.py |
Author(s) | Pedro Sousa Lacerda |
License | Free Software |
This plugin enables virtual screening with the AutoDock Vina software stack. It uses Meeko and Scrubber to prepare molecular ligands, and PLIP and Matplotlib to analyze the results.
It was tested on PyMOL 3.1 with Python 3.10. Currently supports only Linux and probably Mac.
Input
Supports only multi-smi SMILES files (e.g. mols.smi). Make sure to store your chemical libraries so it can be reused.
Cc1c(Cl)cccc1Nc1ncccc1C(=O)OC[C@H](O)CO ZINC000000000171 CN(C)CC[C@@H]1c2ccc(Cl)cc2CCc2cccnc21 ZINC000000000179 CC(C)NC[C@H]1CCc2cc(CO)c([N+](=O)[O-])cc2N1 ZINC000000000570 Cc1nc(NCc2ccccc2)c2cc[nH]c2n1 ZINC000000002036
Guided interface
- Preparing step: While on the preparation step, should fill the target receptor and box center selections like presented on picture. Shold also fill the the input file and output folder. The flexible selection is optional.
- Running step: Follow the graphical interface to go over this step.
- Analyzing step: View screening results.
- PLIP: Check the molecular generated interaction poses.
- Histogram: Check the most interacting residues within the chemical library.
Caveats
It requires internet access on plugin installation and may take time with PyMOL freezed. Docking is as fast as base programs, but PLIP and histogram analyses are slow.
Author
Pedro Sousa Lacerda <pslacerda@gmail.com>
Universidade Federal da Bahia