Discovery of Potent Selective HDAC6 Inhibitors with 5-Phenyl-1H-indole Fragment: Virtual Screening, Rational Design, and Biological Evaluation.

Among the HDACs family, histone deacetylase 6 (HDAC6) has attracted extensive attention due to its unique structure and biological functions. Numerous studies have shown that compared with broad-spectrum HDACs inhibitors, selective HDAC6 inhibitors exert ideal efficacy in tumor treatment with insignificant toxic and side effects, demonstrating promising clinical application prospect. Herein, we carried out rational drug design by integrating a deep learning model, molecular docking, and molecular dynamics simulation technology to construct a virtual screening process. The designed derivatives with 5-phenyl-1H-indole fragment as Cap showed desirable cytotoxicity to the various tumor cell lines, all of which were within 15 µM (ranging from 0.35 to 14.87 µM), among which compound 5i had the best antiproliferative activities against HL-60 (IC50 = 0.35 ± 0.07 µM) and arrested HL-60 cells in the G0/G1 phase. In addition, 5i exhibited better isotype selective inhibitory activities due to the potent potency against HDAC6 (IC50 = 5.16 ± 0.25 nM) and the reduced inhibitory activities against HDAC1 (selective index ≈ 124), which was further verified by immunoblotting results. Moreover, the representative binding conformation of 5i on HDAC6 was revealed and the key residues contributing 5i's binding were also identified via decomposition free-energy analysis. The discovery of lead compound 5i also indicates that virtual screening is still a beneficial tool in drug discovery and can provide more molecular skeletons with research potential for drug design, which is worthy of widespread application.

SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction.

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The graph neural network-multilayer perceptron (GNN-MLP) module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6 M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp = 0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.

LncRNA POU6F2-AS2 contributes to malignant phenotypes and paclitaxel resistance by promoting SKP2 expression in stomach adenocarcinoma.

This study aimed to investigate the role and mechanism of POU6F2-AS2 in the development of gastric cancer. POU6F2-AS2 expression was considerably higher in clinical stomach adenocarcinoma (STAD) tissues and gastric cancer cell lines (MKN-28 and MGC-803) than in neighbouring normal tissues and gastric mucosa epithelial cells (GES-1). POU6F2-AS2 overexpression resulted in a low overall survival probability, progression-free survival probability and post progression survival probability, as well as increased cell viability, migration and invasion of gastric cancer cells, thereby inhibiting apoptosis. Based on RNA pull-down, cycloheximide and MG132 incubation experiments, POU6F2-AS2 promoted SKP2 by stabilizing NONO expression. In addition, in vivo silencing of POU6F2-AS2 in gastric cancer cells can inhibit tumour progression and produce a synergistic antitumour effect when combined with paclitaxel. POU6F2-AS2 is overexpressed in STAD, which is attributed to a bad prognosis. In vitro and in vivo experiments have confirmed that the POU6F2-AS2/NONO/SKP2 axis promotes STAD progression, and that the silencing of POU6F2-AS2 plays a synergistic antitumour effect when combined with paclitaxel. Therefore, POU6F2-AS2 may be potentially developed as a target to inhibit STAD and reduce chemoresistance.

Long noncoding RNA TUG1 upregulates VEGFA to enhance malignant behaviors in stomach adenocarcinoma by sponging miR-29c-3p.

BACKGROUND: Long noncoding RNA (lncRNA) TUG1 has been reported to display a pivotal role in the tumorigenesis and malignant progression of various types of cancers, including stomach adenocarcinoma (STAD). However, the contribution of aberrant expression of TUG1 and the mechanism by which it serves as a competing endogenous RNA (ceRNA) in STAD remains largely obscure. METHODS: The human STAD cell lines (MGC-803 and AGS), human normal gastric epithelial cell line (GES-1), human umbilical vein endothelial cells (HUVECs), and human embryonic kidney cells (HEK293T) were purchased and cultured to investigate the roles of TUG1 in STAD. Twenty BALB/c nude mice were purchased to establish a xenograft model to explore the roles of TUG1 in vivo. RESULTS: Bioinformatics analysis revealed that TUG1 was upregulated in STAD, of which expression was negatively and positively correlated with miR-29c-3p and VEGFA, respectively. Functional analyses indicated that TUG1 functioned as an oncogene to promote malignant behaviors (proliferation, migration, and angiogenesis) of STAD cells; whereas miR-29c-3p exerted the opposite role. Mechanistically, the interaction between miR-29c-3p with TUG1 and VEGFA was demonstrated. It was observed that miR-29c-3p could reverse the TUG1-induced promotion effect on cell proliferation, migration, and angiogenesis in STAD. Furthermore, TUG1 overexpression promoted STAD cell proliferation, metastasis, and angiogenesis, whereas VEGFA silence restored these effects, both in vitro and in vivo. CONCLUSION: This finding confirmed that lncRNA TUG1 acts as a ceRNA for miR-29c-3p to promote tumor progression and angiogenesis by upregulating VEGFA, indicating TUG1 as a therapeutic target in STAD management.

MicroRNA 32 promotes cell proliferation, migration, and suppresses apoptosis in colon cancer cells by targeting OTU domain containing 3.

Colorectal cancer is considered as the fourth leading reason of cancer-linked deaths worldwide. However, our knowledge about its pathogenic mechanism remains inadequate. MicroRNA 32 (miR-32), a member of small noncoding RNAs, has been found vital roles in tumorigenesis. This study studied its functions and underlying mechanism in colorectal cancer. The experiment revealed the obvious upregulation of miR-32 in colorectal cancer tissues and six cancer cell lines, compared with normal tissues and cells. Moreover, miR-32 upregulation reduced cell apoptosis and promoted cell proliferation and migration, while its downregulation displayed opposite effects. Dual luciferase reporter assays proved that miR-32 bound to the 3'-untranslated region (3'-UTR) of OTU domain containing 3 (OTUD3), suggesting that miR-32 directly targeted OTUD3. Further experiments demonstrated that overexpression of miR-32 could reduce the expression level of OTUD3. Furthermore, OTUD3 silence promoted proliferation and motility and decreased apoptosis for HCT116 cells and restored partly miR-32-mediated cell proliferation, migration, and antiapoptosis for colon cancer. Therefore, our study indicated that miR-32 enhanced cell proliferation and motility abilities, and inhibited apoptosis by directly targeting OTUD3 in colon cancer cells, which implied that miR-32 was hopeful to be a biomarker or target used for diagnosis and therapy of colon cancer.

Optimum Matching Model Using Long-Term Computing on Safer Rural Domestic Water Supply Based on Rainwater Harvesting.

A safe rural domestic water supply project has been initiated based on different consumption uses. Long-term computation method and the water balance principle are used to analyze the yearly water demand. Water supply and demand balance is achieved through regulated planning of the rainwater collection surface area and water storage capacity. The best combination of collection area and storage capacity is then determined for various rainfall zones in order to satisfy safe domestic water needs. Ultimately, an optimum matching model is developed to utilize rainwater harvesting for providing safe domestic water in rural areas.