LncRNA FBXO18-AS promotes gastric cancer progression by TGF-β1/Smad signaling

For the digestive system, there exists one common malignant tumor, known as gastric cancer. It is the third most prevalent type of tumor among different tumors worldwide. It has been reported that long noncoding RNAs (lncRNAs), participate in various biological processes of gastric cancer. However, there are still many lncRNAs with unknown functions, and we discovered a novel lncRNA designated as FBXO18-AS. Whether lncRNAFBXO18-AS participates in gastric cancer progression is still unknown. Bioinformatic analysis, immunohistochemistry, Western blotting, and qPCR were carried out to explore FBXO18-AS and TGF-β1 expression. In addition, EdU, MTS, migration and transwell assays were performed to investigate the invasion, proliferation and migration of gastric cancer in vitro. We first discovered that FBXO18-AS expression was upregulated in gastric cancer and linked to poorer outcomes among patients with gastric cancer. Then, we confirmed that FBXO18-AS promoted the proliferation, invasion, migration, and an EMT-like process in gastric cancer in vivo and in vitro. Mechanistically, FBXO18-AS was found to be involved in the progression of gastric cancer by modulating TGF-β1/Smad signaling. Therefore, it might offer a possible biomarker for gastric cancer diagnosis and an effective strategy for clinical treatment

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (ΔG≠ off). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect thismethodology to be widely applicable to drug discovery and development