Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning.
Binding site comparison is one of the methods useful to make such predictions and works in three steps:
- For a target of interest, the binding site is detected and encoded,
- this binding site is subsequently compared to a database of pre-encoded binding sites of structurally available macromolecules, and
- the most similar binding sites (ranked by a scoring function) are proposed as potential off-targets or polypharmacological targets.
How to probe and validate a potential target remains one of the key questions in basic research in life sciences. In the DFG-project Ratar (Read-Across the Targetome), we will use binding site similarity to predict off-targets and to extrapolate compound information from one target to another. This similarity-based knowledge transfer can suggest tool compounds (chemical probes) and off-targets for proteins of interest using the ever-growing amount of available target and compound data.
Kinases are important and well studied drug targets for cancer and inflammatory diseases. Due to the highly conserved structure of kinases, especially at the ATP binding site, the main challenge when developing kinase inhibitors is achieving selectivity, which requires a comprehensive understanding of kinase similarity. In our KiSSim (Kinase Structural Similarity) project, we developed a subpocket-focused kinase fingerprint to investigate kinome-wide pocket similarity.