NLRP3 (Nucleotide-binding oligomerization domain, LRR and pyrin domain-containing protein 3) is a pivotal regulator of inflammation, with strong implications in gout, neurodegenerative diseases, and various inflammatory conditions. Consequently, the exploration of NLRP3 inhibitors is of great significance for the treatment of diseases. MCC950, NP3-146, compound (3), and YQ128 are four highly bioactive NLRP3 inhibitors that show great potential; however, their mechanism of action is currently limited to targeting the ATP binding region (NACHT site) of the NLRP3 protein. To gain deeper insights into the defining features of NLRP3 inhibitors and to develop more potent inhibitors, it is imperative to elucidate the interaction mechanism between NLRP3 and these inhibitors. In this study, we employ a comprehensive computational approach to investigate the binding mechanism between NLRP3 and representative inhibitors. Utilizing the molecular mechanics/generalized Born surface area (MM/GBSA) method, we calculate the binding free energy and pinpoint the key residues involved in the binding of the four inhibitors to NLRP3. The decomposition of binding free energy by the MM/GBSA method reveals that the residues Val71, Arg195, Ile255, Phe419, Arg422, and Met505, situated around the binding pocket, play a crucial role in conferring the high bioactivity of NLRP3 inhibitors. Furthermore, pharmacophore analysis of the four NLRP3 complexes indicates that the primary interaction between the inhibitors and NLRP3 was mainly hydrophobic interaction. Our study provides a profound understanding of the interaction mechanism between NLRP3 and its inhibitors, identifies the key residues involved, and provides theoretical guidance for the design of more efficient NLRP3 inhibitors.

Peptides are able to bind to difficult disease targets with high potency and specificity, providing great opportunities to meet unmet medical requirements. Nevertheless, the unique features of peptides, such as their small size, high structural flexibility, and scarce data availability, bring extra challenges to the design process. Firstly, this review sums up the application of peptide drugs in treating diseases. Then, the review probes into the advantages of Deep Neural Networks (DNNs) in predicting and designing peptide structures. DNNs have demonstrated remarkable capabilities in structural prediction, enabling accurate three-dimensional modeling of peptide drugs through models like AlphaFold and its successors. Finally, the review deliberates on the challenges and coping strategies of DNNs in the development of peptide drugs, along with future research directions. Future research directions focus on further improving the accuracy and efficiency of DNN-based peptide drug design, exploring novel applications of peptide drugs, and accelerating their clinical translation. With continuous advancements in technology and data accumulation, DNNs are poised to play an increasingly crucial role in the field of peptide drug development.

Rapid and precise identification of strawberry diseases is paramount for securing crop yield and quality. To address challenges arising from significant scale variations, diminutive early-stage lesions, and high inter-class visual similarity, this study proposes a parameter-efficient multi-scale feature fusion network derived from YOLO26, termed LMF-YOLO. The architecture incorporates three strategic enhancements: First, leveraging the end-to-end NMS-free framework of YOLO26, a Triple Feature Encoder (TFE) is developed to aggregate multi-scale features in a one-shot manner, preserving critical high resolution spatial details. Second, a lightweight Multi-scale Initialization Module (MSInit), utilizing separable dilated convolutions, captures contextual information across diverse receptive fields, enhancing the model’s discriminative power against visually similar diseases. Third, a P2 micro-detection head is added to provide explicit supervision for extremely early tiny lesions. Evaluations on a custom strawberry disease dataset (7 categories, 6,246 annotations) demonstrate that the proposed model LMF-YOLO (YOLO26n + LMF neck + P2 head) achieves an mAP50 of 82.0%, marking a 1.9 percentage point increase over the baseline YOLO26n. Notably, the model yields an 11.5% improvement in AP_small for early lesions. Following TensorRT acceleration on the Jetson Nano, the model achieves an inference latency of 33.6 ms (~30 FPS), meeting the real-time constraints for mobile-based recognition. Ablation and comparative studies confirm that LMF-YOLO outperforms mainstream YOLO variants in balancing computational efficiency with detection sensitivity, providing a robust technical solution for mobile-intelligent strawberry disease diagnostics.