Novel curcumin- and emodin-related compounds identified by in silico 2D/3D conformer screening induce apoptosis in tumor cells

BACKGROUND: Inhibition of the COP9 signalosome (CSN) associated kinases CK2 and PKD by curcumin causes stabilization of the tumor suppressor p53. It has been shown that curcumin induces tumor cell death and apoptosis. Curcumin and emodin block the CSN-directed c-Jun signaling pathway, which results in diminished c-Jun steady state levels in HeLa cells. The aim of this work was to search for new CSN kinase inhibitors analogue to curcumin and emodin by means of an in silico screening method. METHODS: Here we present a novel method to identify efficient inhibitors of CSN-associated kinases. Using curcumin and emodin as lead structures an in silico screening with our in-house database containing more than 10(6 )structures was carried out. Thirty-five compounds were identified and further evaluated by the Lipinski's rule-of-five. Two groups of compounds can be clearly discriminated according to their structures: the curcumin-group and the emodin-group. The compounds were evaluated in in vitro kinase assays and in cell culture experiments. RESULTS: The data revealed 3 compounds of the curcumin-group (e.g. piceatannol) and 4 of the emodin-group (e.g. anthrachinone) as potent inhibitors of CSN-associated kinases. Identified agents increased p53 levels and induced apoptosis in tumor cells as determined by annexin V-FITC binding, DNA fragmentation and caspase activity assays. CONCLUSION: Our data demonstrate that the new in silico screening method is highly efficient for identifying potential anti-tumor drugs

Docking without docking: ISEARCH--prediction of interactions using known interfaces.

The increasing number of solved protein structures provides a solid number of interfaces, if protein-protein interactions, domain-domain contacts, and contacts between biological units are taken into account. An interface library gives us the opportunity to identify surface regions on a target molecule that are similar by local structure and residue composition. If both unbound components of a possible protein complex exhibit structural similarities to a known interface, the unbound structures can be superposed onto the known interfaces. The approach is accompanied by two mathematical problems. Protein surfaces have to be quickly screened by thousands of patches, and similarity has to be evaluated by a suitable scoring scheme. The used algorithm (NeedleHaystack) identifies similar patches within minutes. Structurally related sites are recognized even if only parts of the template patches are structurally related to the interface region. A successful prediction of the protein complex depends on a suitable template of the library. However, the performed tests indicate that interaction sites are identified even if the similarity is very low. The approach complements existing ab initio methods and provides valuable results on standard benchmark sets