Completed Projects

Team SKI: Selective Kinase Inhibitors

Protein kinases are established drug targets for cancer and inflammatory diseases. Given that they are among the most frequently mutated proteins in tumors, it is not surprising that they are estimated to account for 30% of all drug discovery projects. However, the human genome codes for 560 kinases, and a large number of these kinases – including potential cancer targets – is still unexplored. Furthermore, the ATP-binding site of kinases is highly conserved, which hinders the design of selective kinase inhibitors. Even most of the approved drugs that target kinases are inhibiting a large number of kinases. The interaction with multiple targets can be synergistic by increasing its effect on a particular pathway or by affecting alternative pathways in the case of a resistance mechanism. However, unintended drug-target interactions can cause toxic side-effects and are often a driving cause for the termination of drug development programs. Designing selective compounds is therefore of high practical value for drug discovery efforts.

The driving questions for our research are:

  • Which kinase structures should be exploited as drug targets?
  • What are the determinants of binding processes?
  • How to design selective protein kinase inhibitors?

To get new insights into these questions, we develop and apply computational methods that are grounded in bio- and cheminformatics and use the wealth of available profiling data and crystal structures as input. Furthermore, we develop visualization tools, such as the kinome tree viewer KinMap, that help visualizing kinase centric data.

Publications

  1. Volkamer, A., Eid, S., Turk, S., Jaeger, S., Rippmann, F., Fulle, S.
    The Pocketome of Human Kinases: Prioritizing the ATP binding sites of (yet) untapped protein kinases for drug discovery. J. Chem. Inf. Model. 2015, 55, 538–549.
  2. Volkamer, A., Eid, S., Turk, S., Rippmann, F., Fulle, S.
    Identification and visualization of kinase-specific subpockets. J. Chem. Inf. Model. 2016, 56, 335-346.
  3. Merget, B., Turk, S., Eid, S., Rippmann, F., Fulle, S.
    Profiling prediction of kinase inhibitors: toward the virtual assay. J. Med. Chem. 2017, 60, 474−485.
  4. Eid, S., Turk, S., Volkamer, A., Rippmann, F., Fulle, S.
    KinMap: a web-based tool for interactive navigation through human kinome data. BMC Bioinformatics, 2017, 18:16.
  5. Fulle, S., Merget, B.
    In silico Design selektiver Kinase-Inhibitoren rückt in greifbare Nähe. Systembiologie.de 2016, 11, 40-43.
  6. Fulle, S., Merget, B., Turk. S
    Towards in silico design of selective kinase inhibitors. Systembiologie.de Int. Ed. 2017, 11, 40-43.
  7. Sorgenfrei, F.A., Fulle, S., Merget, B.
    Kinome-wide profiling prediction of small molecules. ChemMedChem, accepted.
The research of this team was kindly sponsored by Merck.

Former Members

  • Dr. Simone Fulle (Group-Leader)
  • Dr. Sameh Eid (Postdoctoral Researcher)
  • Dr. Benjamin Merget (Postdoctoral Researcher)
  • Dr. Samo Turk (Postdoctoral Researcher)
  • Dr. Andrea Volkamer (Postdoctoral Researcher)
  • Guillaume Roellinger (Research Associate)