Cathepsin B degrades RbcL during freezing-induced programmed cell death in Arabidopsis.

Cathepsin B plays an important role that degrades the Rubisco large subunit RbcL in freezing stress. Programmed cell death (PCD) has been well documented in both development and in response to environmental stresses in plants, however, PCD induced by freezing stress and its molecular mechanisms remain poorly understood. In the present study, we characterized freezing-induced PCD and explored its mechanisms in Arabidopsis.

In silico prediction of immune-escaping hot spots for future COVID-19 vaccine design.

The COVID-19 pandemic has had a widespread impact on a global scale, and the evolution of considerable dominants has already taken place. Some variants contained certain key mutations located on the receptor binding domain (RBD) of spike protein, such as E484K and N501Y. It is increasingly worrying that these variants could impair the efficacy of current vaccines or therapies.

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.

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.

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.

BYPASS1-LIKE regulates lateral root initiation via exocytic vesicular trafficking-mediated PIN recycling in Arabidopsis.

Auxin and auxin-mediated signaling pathways are known to regulate lateral root development. Although exocytic vesicle trafficking plays an important role in recycling the PIN-FORMED (PIN) auxin efflux carriers and in polar auxin transport during lateral root formation, the mechanistic details of these processes are not well understood. Here, we demonstrate that BYPASS1-LIKE (B1L) regulates lateral root initiation via exocytic vesicular trafficking-mediated PIN recycling in Arabidopsis thaliana.

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).

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.