KinFragLib: Kinase-focused fragment library

Table of contents

• Description
• Repository content
• Quick start
• Contact
• License
• Citation

Repository content

This repository holds the following resources:

1. Fragment library data and a link to the combinatorial library data.
2. Quick start notebook explaining how to load and use the fragment library.
3. Notebooks covering the full analyses regarding the fragment and combinatorial libraries as described in the corresponding paper.

Please find detailed description of files in data/ and notebooks/ in the folders' README files.

Description

Exploring the kinase inhibitor space using subpocket-focused fragmentation and recombination

Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. Fragment-based drug design has proven useful as one approach to develop novel kinase inhibitors. Usually, fragment-based methods follow a knowledge-driven approach, i.e., optimizing a focused set of fragments into molecular hits.

We present here KinFragLib, a data-driven kinase-focused fragment library based on the structural kinome data retrieved from the KLIFS database. Each kinase binding pocket (for DFG-in structures with non-covalent ligands) is automatically divided in KinFragLib into six subpockets, i.e. the adenine pocket (AP), front pocket (FP), solvent-exposed pocket (SE), gate area (GA) as well as back pocket 1 and 2 (B1 and B2), based on defined pocket-spanning residues. Each co-crystallized ligand is fragmented using the BRICS algorithm and its fragments are assigned to the respective subpocket they occupy. Following this approach, a fragment library is created with respective subpocket pools. This fragment library enables an in-depth analysis of the chemical space of known kinase inhibitors, and can be used to enumerate recombined fragments in order to generate novel potential inhibitors.

Quick start

1. Clone this repository.

   git clone https://github.com/volkamerlab/KinFragLib.git

2. Create the kinfraglib conda environment.

   # Change to KinFragLib directory
   cd /path/to/KinFragLib   
   
   # Create and activate environment
   conda env create -f environment.yml
   conda activate kinfraglib
   
   # Link the conda environment to the Jupyter notebook
   python -m ipykernel install --user --name kinfraglib
   
   # Optionally, add TOC extension for Jupyter Lab
   jupyter labextension install @jupyterlab/toc
   
   # Install klifs_utils package
   pip install https://github.com/volkamerlab/klifs_utils/archive/master.tar.gz

3. Open the notebook quick_start.ipynb for an introduction on how to load and use the fragment library.

   # Change to KinFragLib directory (if you have not already)
   cd /path/to/KinFragLib
   
   # Start jupyter lab to explore the notebooks
   jupyter lab

Contact

Please contact us if you have questions or suggestions.

• Open an issue on our GitHub repository: https://github.com/volkamerlab/KinFragLib/issues
• Or send us an email: andrea.volkamer@charite.de

We are looking forward to hearing from you!

License

This resource is licensed under the MIT license, a permissive open source license.

Citation

Sydow, D., Schmiel, P., Mortier, J., and Volkamer, A. KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination. J. Chem. Inf. Model. 2020. https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839

@article{doi:10.1021/acs.jcim.0c00839,
author = {Sydow, Dominique and Schmiel, Paula and Mortier, Jérémie and Volkamer, Andrea},
title = {KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination},
journal = {Journal of Chemical Information and Modeling},
volume = {60},
number = {12},
pages = {6081-6094},
year = {2020},
doi = {10.1021/acs.jcim.0c00839},
note ={PMID: 33155465},
URL = {https://doi.org/10.1021/acs.jcim.0c00839}
}