Including three Jupyter Notebooks using three different binding site predictors (PDBe REST API, Cluster90 binding site and Fpocket).
This tutorials aim to illustrate the process of protein-ligand docking, step by step, using the BioExcel Building Blocks library (biobb). The particular examples used are based on the Mitogen-activated protein kinase 14 (p38-α) protein (PDB code 3HEC), a well-known Protein Kinase enzyme, in complex with the FDA-approved Imatinib (PDB Ligand code STI, DrugBank Ligand Code DB00619) and Dasatinib (PDB Ligand code 1N1, DrugBank Ligand Code DB01254), small kinase inhibitors molecules used to treat certain types of cancer.
The tutorials will guide you through the process of identifying the active site cavity (pocket) without previous knowledge, and the final prediction of the protein-ligand complex.
git clone https://github.com/bioexcel/biobb_wf_virtual-screening.git
cd biobb_wf_virtual-screening
conda env create -f conda_env/environment.yml
conda activate biobb_wf_virtual-screening
jupyter-notebook biobb_wf_virtual-screening/notebooks/ebi_api/biobb_wf_virtual-screening_ebi_api.ipynb
jupyter-notebook biobb_wf_virtual-screening/notebooks/cluster_bs/biobb_wf_virtual-screening_cluster_bs.ipynb
jupyter-notebook biobb_wf_virtual-screening/notebooks/fpocket/biobb_wf_virtual-screening_fpocket.ipynb
Click here to view tutorials in Read the Docs
2024.1 Release
This software has been developed in the MMB group at the BSC & IRB for the European BioExcel, funded by the European Commission (EU H2020 823830, EU H2020 675728).
Licensed under the Apache License 2.0, see the file LICENSE for details.