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Cancer is one of the leading causes of death across all countries and its diagnosis still yields fear for the affected patient. Although treatment of cancer has made marvelous progress compared to the agents available thirty years ago, a cure for cancer, however, is still a distant prospect. Modern therapy still is a burden for many patients due to heavy side effects. With the development of agents targeting specific molecular targets on cancer cells, a new field of cancer therapy was opened and a small success story in the history of cancer began.
Aurora kinases represent a relatively new target in cancer therapy. The kinase is a essential part of mitosis and cell cycle progression and its overexpression has been shown to be related to many kinds of malignancies. Allosteric inhibition of a kinase is an increasing pre-clinical approach not yet established in the treatment of patients. In this thesis, we combine allostery with another innovative approach that is drug repurposing. If repurposed, a drug can be permitted to fast track drug admission to clinical trials.
I set up a screening of 1280 FDA approved drugs to identify small molecule compounds that affect the binding of Aurora kinase A and its main physiologic binding partner, TPX2. Further, I characterized the positive hits in vitro for their capabilities to displace TPX2 from Aurora A, to inhibit Aurora kinase activity, to thermally stabilize the protein and performed assays to determine their dissociation constant. Last but not least, I tested the compounds in cells for their effect on the cell viability and cell cycle via flow cytometry. Comparing the hit-compounds with controls I found that ATP-competitive AurA inhibitor MLN 8237 strongly displaces the interaction of Aurora A with TPX2.
Summarized, we identified eight hit compounds allosterically affecting Aurora A, but no compound proved to be active in all assays. Just one compound, PS 731, identified in another screening performed by our group and further characterized in this thesis remains interesting, especially when put in context with recent publications released in the time between the start of experiments for this thesis and its finalization.
Allostery is a phenomenon observed in many proteins where binding of a macromolecular partner or a small-molecule ligand at one location leads to specific perturbations at a site not in direct contact with the region where the binding occurs. The list of proteins under allosteric regulation includes AGC protein kinases. AGC kinases have a conserved allosteric site, the phosphoinositide-dependent protein kinase 1 (PDK1)-interacting fragment (PIF) pocket, which regulates protein ATP-binding, activity, and interaction with substrates. In this study, we identify small molecules that bind to the ATP-binding site and affect the PIF pocket of AGC kinase family members, PDK1 and Aurora kinase. We describe the mechanistic details and show that although PDK1 and Aurora kinase inhibitors bind to the conserved ATP-binding site, they differentially modulate physiological interactions at the PIF-pocket site. Our work outlines a strategy for developing bidirectional small-molecule allosteric modulators of protein kinases and other signaling proteins.
An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.