High-Field NMR Spectroscopy Reveals Aromaticity-Modulated Hydrogen Bonding in Heterocycles.

From DNA base pairs to drug-receptor binding, hydrogen (H-)bonding and aromaticity are common features of heterocycles. Herein, the interplay of these bonding aspects is explored. H-bond strength modulation due to enhancement or disruption of aromaticity of heterocycles is experimentally revealed by comparing homodimer H-bond energies of aromatic heterocycles with analogs that have the same H-bonding moieties but lack cyclic π-conjugation. NMR studies of dimerization in C6 D6 find aromaticity-modulated H-bonding (AMHB) energy effects of approximately ±30 %, depending on whether they enhance or weaken aromatic delocalization. The attendant ring current perturbations expected from such modulation are confirmed by chemical shift changes in both observed ring C-H and calculated nucleus-independent sites. In silico modeling confirms that AMHB effects outweigh those of hybridization or dipole-dipole interaction.

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.