Silmitasertib, a casein kinase 2 inhibitor, induces massive lipid droplet accumulation and nonapoptotic cell death in head and neck cancer cells.

BACKGROUND: Accumulating evidence shows that high expression of casein kinase 2 (CK2) and phosphorylated acetyl CoA carboxylase (pACC) in patients with squamous cell carcinoma of the head and neck (SCCHN) correlates with decreased survival rates. Computational analysis has shown that ACC is a potential substrate for CK2, and its inhibition can suppress ACC phosphorylation in vitro. CX-4945, also known as silmitasertib, is an orally administered, highly specific, ATP-competitive inhibitor of CK2 and is under clinical investigation as a treatment for malignancies. We hypothesize that inhibition of CK2 by CX-4945 can reduce CK2-downstream phosphorylation of ACC as a therapeutic strategy against SCCHN. METHODS: Three aggressive SCCHN cell lines (OSC-19, FaDu and HN31) were cultured to investigate the anticancer mechanism of the CK2 inhibitor, CX-4945. Cell cycle analysis, Annexin V/PI staining, and cleavage of PARP were performed to detect apoptosis. Western blot, electron microscopy and analysis of acidic vesicular organelle development were used to detect autophagy. Interference with cellular metabolism by CX-4945 treatment was determined by Seahorse XF24 Extracellular Flux Analyzer and mass spectrometry. RESULTS: Cellular metabolism was impeded by CX-4945 in aggressive SCCHN cells by Seahorse XF24 Extracellular Flux Analyzer and mass spectrometry, and consequently time- and dose-dependent lipid droplet accumulation and non-apoptotic cell death were observed. The lipogenic enzyme ACC was demonstrated to be associated with CK2, and its repressive phosphorylation could be removed by the CK2 inhibitor CX-4945. Overexpression of ACC resulted in impaired cell survival following transient transfection. CONCLUSION: The findings demonstrate that CK2 inhibition impairs normal cellular energy metabolism and may be an attractive therapy for treating aggressive SCCHN.

Identification of pan-kinase-family inhibitors using graph convolutional networks to reveal family-sensitive pre-moieties.

BACKGROUND: Human protein kinases, the key players in phosphoryl signal transduction, have been actively investigated as drug targets for complex diseases such as cancer, immune disorders, and Alzheimer's disease, with more than 60 successful drugs developed in the past 30 years. However, many of these single-kinase inhibitors show low efficacy and drug resistance has become an issue. Owing to the occurrence of highly conserved catalytic sites and shared signaling pathways within a kinase family, multi-target kinase inhibitors have attracted attention. RESULTS: To design and identify such pan-kinase family inhibitors (PKFIs), we proposed PKFI sets for eight families using 200,000 experimental bioactivity data points and applied a graph convolutional network (GCN) to build classification models. Furthermore, we identified and extracted family-sensitive (only present in a family) pre-moieties (parts of complete moieties) by utilizing a visualized explanation (i.e., where the model focuses on each input) method for deep learning, gradient-weighted class activation mapping (Grad-CAM). CONCLUSIONS: This study is the first to propose the PKFI sets, and our results point out and validate the power of GCN models in understanding the pre-moieties of PKFIs within and across different kinase families. Moreover, we highlight the discoverability of family-sensitive pre-moieties in PKFI identification and drug design.

Discovery of moiety preference by Shapley value in protein kinase family using random forest models.

BACKGROUND: Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). RESULTS: In this study, we used "merged moiety-based interpretable features (MMIFs)," which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. CONCLUSIONS: In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.

Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.

Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein-protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

A unique coordination-driven route for the precise nanoassembly of metal sulfides on metal-organic frameworks.

Incorporating different materials, such as metal sulfides, with metal-organic frameworks (MOFs) to develop MOF-based multifunctional composites with enhanced performance is an important area of research. However, the intrinsically high interfacial energy barrier significantly restricts the heterogeneous nucleation and nanoassembly of metal sulfides onto MOFs during the wet chemistry synthesis process. Herein, taking advantage of the natural tailorability of MOFs, the precise and controllable growth of metal sulfide nanoparticles (NPs) (CdS, ZnS, CuS and Ag2S) at the coordinatively unsaturated metal sites (CUSs) of MOFs to form MOF@metal sulfide composites under mild conditions is achieved via a cysteamine-assisted coordination-driven route. During the process, the molecular linker of cysteamine, possessing one amino group for chelating with the CUSs of the MOF and one thiol group as a docking site to anchor metal ions, plays a prominent role in enhancing interfacial interactions between the MOF and metal ions. The subsequent S2- anion exchange process leads to intimate surface-attached nucleation and epitaxial growth of metal sulfide NPs on the surface of the MOF. The as-formed composites exhibit enhanced charge separation and transfer capability, and thus boost photocatalytic performance. This general and simple approach provides a new avenue for the design of MOF-metal sulfide hybrids.