Structure of a fungal 1,3-ß-glucan synthase.

1,3-ß-Glucan serves as the primary component of the fungal cell wall and is produced by 1,3-ß-glucan synthase located in the plasma membrane. This synthase is a molecular target for antifungal drugs such as echinocandins and the triterpenoid ibrexafungerp. In this study, we present the cryo-electron microscopy structure of Saccharomyces cerevisiae 1,3-ß-glucan synthase (Fks1) at 2.47-Å resolution. The structure reveals a central catalytic region adopting a cellulose synthase fold with a cytosolic conserved GT-A-type glycosyltransferase domain and a closed transmembrane channel responsible for glucan transportation. Two extracellular disulfide bonds are found to be crucial for Fks1 enzymatic activity. Through structural comparative analysis with cellulose synthases and structure-guided mutagenesis studies, we gain previously unknown insights into the molecular mechanisms of fungal 1,3-ß-glucan synthase.

Structure, catalysis, chitin transport, and selective inhibition of chitin synthase.

Chitin is one of the most abundant natural biopolymers and serves as a critical structural component of extracellular matrices, including fungal cell walls and insect exoskeletons. As a linear polymer of ß-(1,4)-linked N-acetylglucosamine, chitin is synthesized by chitin synthases, which are recognized as targets for antifungal and anti-insect drugs. In this study, we determine seven different cryo-electron microscopy structures of a Saccharomyces cerevisiae chitin synthase in the absence and presence of glycosyl donor, acceptor, product, or peptidyl nucleoside inhibitors. Combined with functional analyses, these structures show how the donor and acceptor substrates bind in the active site, how substrate hydrolysis drives self-priming, how a chitin-conducting transmembrane channel opens, and how peptidyl nucleoside inhibitors inhibit chitin synthase. Our work provides a structural basis for understanding the function and inhibition of chitin synthase.

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm.

The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.