KiSSim: Predicting off-targets from structural similarities in the kinome

KiSSim: Predicting off-targets from structural similarities in the kinome Project description. KiSSim (Kinase Structural Similarity) is a novel fingerprint designed specifically for kinase pockets, allowing for similarity studies across the structurally covered kinome. The kinase fingerprint is based on the KLIFS pocket alignment, which defines 85 pocket residues for all kinase structures. This enables a residue-by-residue comparison without a computationally expensive alignment step. The pocket fingerprint encodes each pocket residue’s spatial and physicochemical properties. The spatial properties describe the residue’s position in relation to the kinase pocket center and important kinase subpockets, i.e. the hinge region, the DFG region, and the front pocket. The physicochemical properties encompass for each residue its size and pharmacophoric features, solvent exposure, and side chain orientation. Some datasets are not part of the `kissim_app` GitHub repository due to their size but can be downloaded from here to the respective kissim_app folders. Data. `20210902_KLIFS_HUMAN.tar.gz` --- save in `kissim_app/data/external/structures` `complete_SiteAlign.txt.gz` --- save in `kissim_app/data/external/sitealign` Results. `results.tar.bz2`--- save as `kissim_app/results` These are the KiSSim results: fingerprints, feature/fingerprint distances, kinase matrices, and kinase trees for structures in all (`all`), DFG-in (`dfg_in`), and DFG-out (`dfg_out`) conformation. In the case of the DFG-in conformation, we also have KiSSim runs with fingerprint subsets based on only residues that interact with certain ligands in KLIFS IFPs: Erlotinib (`dfg_in_IRE`), Imatinib (`dfg_in_STI`), Bosutinib (`dfg_in_DB8`), and Dopamapimod (`dfg_in_B96`). The folder contains README with a detailed file list. Usage. This dataset can be used to run the notebooks available on https://github.com/volkamerlab/kissim_app. Clone the kissim_app repository. Download the files provided here. If applicable, extract the archive content to the folders as indicated above and run the notebooks. cd /path/to/your/download
tar -xvf results.tar.bz2 -C /path/to/kissim_app/
tar -xvf 20210902_KLIFS_HUMAN.tar.bz2 -C /path/to/kissim_app/data/external/structures/

# In case you want the raw SiteAlign data
mv complete_SiteAlign.txt.gz /path/to/kissim_app/data/external/sitealign Citation. These datasets are part of the KiSSim publication: TBA