https://github.com/bioinfomachinelearning/tulip
Template-based modeling for accurate prediction of ligand-protein complex structures (TULIP)
https://github.com/bioinfomachinelearning/tulip
Last synced: 2 months ago
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Template-based modeling for accurate prediction of ligand-protein complex structures (TULIP)
- Host: GitHub
- URL: https://github.com/bioinfomachinelearning/tulip
- Owner: BioinfoMachineLearning
- Created: 2022-08-26T14:26:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-06-05T05:20:05.000Z (almost 2 years ago)
- Last Synced: 2025-09-09T16:34:17.570Z (8 months ago)
- Language: Python
- Size: 14.4 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## TULIP
The template-based modeling for accurate ligand-protein complex structure prediction (TULIP).
``Input`` : Query protein, Ligand molecule (``.smiles`` ), Protein Template Hits
``Output`` : ``.sdf`` file of ligand molecule that potentially binds to input query protein
## Setup Environment
1. The required packages are found in ``environment.yml``
``conda env create -f environment.yml``
2. Once the installation is completed, activate the conda environment
``conda activate tulip``
3. The pipeline makes use of UCSF Chimera and PyRosetta. Please install them through their websites
[UCSF Chimera Download](https://www.cgl.ucsf.edu/chimera/download.html) , this link will take users to: ```https://www.cgl.ucsf.edu/chimera/download.html```
[PyRosetta Download](https://www.pyrosetta.org/downloads) , this link will take users to: ```https://www.pyrosetta.org/downloads```
To adjust the target ligand's binding pose and orientation by rotation and translation, TULIP uses LS-align to align the target ligand with template ligands of higher similarity by both flexible and rigid alignments.
The LS-align tool can be downloaded and compiled into local machine from the below link:
[LS-align](https://zhanggroup.org/LS-align/) , this link will take users to: ```https://zhanggroup.org/LS-align/```
## Run
To run TULIP pipeline run the following:
```
git clone https://github.com/BioinfoMachineLearning/tulip.git
cd ProteinLigand
python3 complex_generator.py --config=../configs/configs.yml
```
``configs/configs.yml`` contains the configuration of the pipeline. Enter the correct paths in config file.
## Post Processing
Once the ligand templates are identified using the above script, use the script below to run the post-processing for multi-atom ligands. Update the ``ls_align_path`` variable in the script to the path where LS-align is installed.
``python3 post_process.py ``
For ions and small molecules run:
``python3 process_small_mol.py ``
## Clustering
To perform clustering in case we have same ligand SMILES for a protein.
``python3 cluster_files.py``
## Outputs
TULIP returns the identified ligand molecules in ranks. Example : `rank_0.sdf`, `rank_1.sdf`, and so on for each protein.