Introduction: Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.Methods: In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.Results and discussion: The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.

• An computational method was developed to identify Cyclin Protein. • Several kinds of features were proposed to formulate protein samples. • A fusion feature set could produce a high accuracy.

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

Bladder cancer remains one of the most common malignant tumors that threatens human health worldwide. It imposes a heavy burden on patients and society due to the high medical costs associated with its easy metastasis and recurrence. Although several treatment options for bladder cancer are available, their clinical efficacy remains unsatisfactory. Therefore, actively exploring new drugs and their mechanisms of action for the clinical treatment of bladder cancer is very important. Scabertopin is one of the major sesquiterpene lactones found in Elephantopus scaber L. Sesquiterpene lactones are thought to have fairly strong anti-cancer efficacy. However, the anticancer effect of sesquiterpenoid scabertopin on bladder cancer and its mechanism are still unclear. The aim of this study is to evaluate the antitumor activity of scabertopin in bladder cancer and its potential molecular mechanism in vitro. Our results suggest that scabertopin can induce RIP1/RIP3-dependent necroptosis in bladder cancer cells by promoting the production of mitochondrial reactive oxygen species (ROS), inhibit the expression of MMP-9 by inhibiting the FAK/PI3K/Akt signaling pathway, and ultimately inhibit the migration and invasion ability of bladder cancer cells. At the same time, we also demonstrated that the half-inhibition concentration (IC50) of scabertopin on various bladder cancer cell lines (J82, T24, RT4 and 5637) is much lower than that on human ureteral epithelial immortalized cells (SV-HUC-1). The above observations indicate that scabertopin is a potential therapeutic agent for bladder cancer that acts by inducing necroptosis and inhibiting metastasis.

Heteroatom-doped graphene quantum dots (GQDs) have attracted considerable attention due to their potential applications as luminescent materials and in biology. In this work, we developed a solvent-free gram-scale mechanochemical method for the preparation of nitrogen-doped graphene quantum dots (N-GQDs) with the highest solubility (31 mg mL⁻¹) in water reported to date. Commercial graphite was sheared and cut through grinding with solid melamine and then ground with solid KOH to get sub-5 nm-sized, 1–3-layered N-GQDs. Notably, these N-GQDs exhibit white-light emission and broad excitation-dependent full-color photoluminescence from 463 nm to 672 nm. When the excitation light ranged from 325 nm to 485 nm, these mechanochemically obtained N-GQDs exhibited bright white-light emission. Intriguingly, the change in the emission wavelength has two-stage linear relationships with the change in the excitation wavelength, and the inflection point is at 580 nm (excited at 550 nm). The difference between the emission and excitation wavelengths decreases from 138 to 12 nm, which also shows two-stage linear relationships with the change in the excitation wavelength. It is notable that their PL quantum yields are high, up to 26.6%. Furthermore, we studied the inhibitory effect of as-obtained N-GQDs on bladder cancer cells (UMUC-3); as a result, with the increase of the concentration of N-GQDs, the proliferation of cancer cells was obviously prohibited.