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Natural redox‐regulated channel proteins often utilize disulfide bonds as redox sensors for adaptive regulation of channel conformations in response to diverse physiological environments. In this study, we developed novel synthetic ion channels capable of reversibly switching their ion‐transport capabilities by incorporating multiple disulfide bonds into artificial systems. X‐ray structural analysis and electrophysiological experiments demonstrated that these disulfide‐bridged molecules possess well‐defined tubular cavities and can be efficiently inserted into lipid bilayers to form artificial ion channels. More importantly, the disulfide bonds in these molecules serve as redox‐tunable switches to regulate the formation and disruption of ion‐permeation pathways, thereby achieving a transition in the transmembrane transport process between the ON and OFF states. [ABSTRACT FROM AUTHOR]

The advancement of an intelligent automobile sound switching system has the potential to elevate the market standing of automotive products, with the pivotal prerequisite being the selection of automobile sounds based on the driver's subjective perception. The subjective responses of diverse individuals to sounds can be objectively manifested through EEG signals. Therefore, EEG signals are employed herein to attain the recognition of automobile sounds. A subjective evaluation and EEG signal acquisition experiment are designed involving the stimulation of three distinct types of automobile sounds, namely comfort, power, and technology sounds, and a comprehensive database of EEG signals corresponding to these three sound qualities is established. Then, a specific transfer learning model based on a convolutional neural network (STL-CNN) is formulated, where the method of training the upper layer parameters with the fixed bottom weights is proposed to adaptively extract the EEG features related to automobile sounds. These improvements contribute to improving the generalization ability of the model and realizing the recognition of automobile sounds fused with EEG signals. The results of the comparison with traditional support vector machine (SVM) and convolutional neural network (CNN) models demonstrate that the accuracy of the test set of the STL-CNN model reaches 91.5%. Moreover, its comprehensive performance, coupled with the ability to adapt to individual differences, surpasses that of both SVM and CNN models. The demonstrated method in the recognition of automobile sounds based on EEG signals is of significance for the future implementation of switching driving sound modes fused with EEG signals. [ABSTRACT FROM AUTHOR]

The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users' requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm self-supervised hypergraph embedding (SHE) model to predict users' interests by hypergraph convolution and self-supervised discrimination. SHE builds a hypergraph with multiple interest clues to alleviate the interaction sparsity issue and performs interest propagation to embed CF signals in hybrid learning on the hypergraph. It follows an auxiliary local view by similar hypergraph construction and interest propagation to restrain unnecessary propagation between user swarms. Besides, interest contrast further inserts self-discrimination to deal with long-tail bias issue and enhance interest modeling, which aid recommendation by a multi-task learning optimization. Experiments on public datasets show that the proposed SHE outperforms the state-of-the-art models demonstrating the effectiveness of hypergraph-based interest propagation and swarm-aware interest contrast to enhance embedding for recommendation. [ABSTRACT FROM AUTHOR]

Protein–ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein–ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein–ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein–ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel. [ABSTRACT FROM AUTHOR]

In the context of peptide drug development, glycosylation plays a pivotal role. Accordingly, L-type peptides were synthesized predicated upon the PD-1/PD-L1 blocker DPPA-1. Subsequent glycosylation resulted in the production of two distinct glycopeptides, D-glu-LPPA-1 and D-gal-LPPA-1, by using D-glucose (D-glu) and D-galactose (D-gal), respectively, during glycosylation. Both glycopeptides significantly inhibited the interaction between PD-1 and PD-L1, and the measured half maximal inhibitory concentrations (IC50s) were 75.5 μM and 101.9 μM for D-glu-LPPA-1 and D-gal-LPPA-1, respectively. Furthermore, D-gal-LPPA-1 displayed a pronounced ability to restore T-cell functionality. In an MC38 tumor-bearing mouse model, D-gal-LPPA-1 demonstrated a significant inhibitory effect. Notably, D-gal-LPPA-1 substantially augmented the abundance and functionality of CD8+ T cells in the tumor microenvironment. Additionally, in the lymph nodes and spleens, D-gal-LPPA-1 significantly increased the proportion of CD8+ T cells secreting interferon-gamma (IFN-γ). These strong findings position D-gal-LPPA-1 as a potent enhancer of the antitumor immune response in MC38 tumor-bearing mice, underscoring its potential as a formidable PD-1/PD-L1 blocking agent. [ABSTRACT FROM AUTHOR]