Monosaccharides are added to the hydrophilic face of a self-assembled asymmetric FeII metallohelix, using CuAAC chemistry. The sixteen resulting architectures are water-stable and optically pure, and exhibit improved antiproliferative selectivity against colon cancer cells (HCT116 p53+/+ ) with respect to the non-cancerous ARPE-19 cell line. While the most selective compound is a glucose-appended enantiomer, its cellular entry is not mainly glucose transporter-mediated. Glucose conjugation nevertheless increases nuclear delivery ca 2.5-fold, and a non-destructive interaction with DNA is indicated. Addition of the glucose units affects the binding orientation of the metallohelix to naked DNA, but does not substantially alter the overall affinity. In a mouse model, the glucose conjugated compound was far better tolerated, and tumour growth delays for the parent compound (2.6 d) were improved to 4.3 d; performance as good as cisplatin but with the advantage of no weight loss in the subjects.

• Klíčová slova
• antitumor agents, glycoconjugates, metallohelices, nuclear delivery, self-assembly,
• MeSH
• glykokonjugáty chemie   MeSH
• HCT116 buňky MeSH
• hmotnostní spektrometrie s elektrosprejovou ionizací MeSH
• kovy chemie   MeSH
• lidé MeSH
• nádory patologie   MeSH
• protonová magnetická rezonanční spektroskopie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• glykokonjugáty MeSH
• kovy MeSH

BACKGROUND: The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their structures. Automatically discovered structural features are able to capture some characteristic spatial configurations of amino acids in proteins. RESULTS: Prediction based only on structural relational features already achieves competitive results to existing methods based on physicochemical properties on several protein datasets. Predictive performance is further improved when structural features are combined with physicochemical features. Moreover, the structural features provide some insights not revealed by physicochemical features. Our method is able to detect common spatial substructures. We demonstrate this in experiments with zinc finger proteins. CONCLUSIONS: We introduced a novel approach for DNA-binding propensity prediction using relational machine learning which could potentially be used also for protein function prediction in general.

• Publikační typ
• časopisecké články MeSH