Macao Polytechnic University

Through long-term natural selection, a co-evolutionary relationship has formed between plants and pests. However, pathogens and pests can also undermine plant resistance by releasing certain substances such as effectors. Helicoverpa armigera R-like protein 1 (HARP1), an effector in oral secretions, is capable of interacting with JASMONATE-ZIM DOMAIN (JAZ) protein. This interaction inhibits the degradation of JAZ and prevents the activation of jasmonic acid (JA) signalling in response to biotic stress. Nevertheless, the mechanism by which HARP1 interacts with JAZ to suppress JA signalling remains elusive. In this study, we first confirm that the ZIM domain within JAZ is sufficient for the HARP1-JAZ interaction. To gain mechanistic insight, we determined the crystal structure of HARP1 and utilised AlphaFold2 to predict its binding mode with JAZ3. The structure analysis reveals that HARP1 is a β-sandwich fold composed of seven strands, which directly binds to JAZ homo- or hetero-dimers. This binding prevents the degradation of the JAZ repressor, consequently ensuring the repressed JA signalling pathway in the plant. Our structural and functional studies provide new insights into the JA signalling transcriptional repression mechanism by effectors released by pests that suppress JA signalling.

Accurate prediction of drug metabolism and pharmacokinetics (ADMET) properties is crucial in drug discovery. Here, we present a novel approach to enhance ADMET property predictions using Cross-Aligned Multimodal Attention (CMA) mechanisms, pretrained models, and multimodal techniques. ADMET data is collected and processed using image processing, graph neural networks, and chemical fingerprinting. Pretrained models like GROVER and ResNet generate a multi-channel data format, and the CMA mechanism aligns and correlates the data modalities. Grad-CAM technology interprets the model's predictions, visually demonstrating the relationship between compound properties and fragments. Our ADMET property prediction server ( http://guolab.mpu.edu.mo/CMA ) implements the CMA-based model and a substantial language model for ADMET property prediction. The innovation lies in the integration of multimodal data, the application of pretrained models, and the development of cross-modal alignment. This approach improves the efficiency and accuracy of ADMET property predictions and opens new avenues for research in molecular science, particularly in drug design and evaluation.

Signal peptides play essential roles in protein secretion and localization, and their accurate identification is critical for understanding protein synthesis, transport, and functional regulation. However, severe class imbalance in signal peptide data sets leads to substantially lower recognition performance for minor classes compared with major classes. Here, we propose a structure-aware multimodal signal peptide prediction network (SaSPNet), which incorporates structural modality information into conventional sequence modeling and uses a graph convolutional network (GCN)-based structure encoder to learn structural representations of signal peptides for both signal peptide type and cleavage-site prediction. SaSPNet significantly improves the prediction performance for minor signal peptide classes on the USPNet data set, achieving more than a 10% gain over existing methods on key minor-class metrics. Feature visualization and explainability analyses show that the structure encoder learns more discriminative structural patterns for minor signal peptides, revealing the mechanism by which the structural modality enhances model performance. In addition, comparative analyses using three-dimensional structures generated by different structure prediction models demonstrate that SaSPNet is robust to variations in structural data quality. We further construct an independent test set, SP-MinorEval, specifically for minor signal peptides, and evaluations on this data set show that SaSPNet maintains strong performance across domains, providing an effective tool for minor-class signal peptide prediction, protein secretion mechanism studies, and functional protein discovery.

This paper systematically studies the non-invasive continuous blood pressure estimation and cardiovascular risk assessment methods based on photoplethysmography (PPG) signals. A complete signal processing flow was constructed using the PhysioNet public dataset, including preprocessing, feature extraction, model construction and validation. The experimental results show that the extraction of heart rate and respiratory rate based on PPG has high accuracy, preliminarily verifying the correlation between the morphological characteristics of PPG and blood pressure parameters. The research further proposed a hierarchical processing architecture to balance the computing efficiency and monitoring depth of wearable devices, providing a reference for the engineering implementation of health monitoring systems.

Objective Non-alcoholic fatty liver disease (NAFLD) constitutes a significant global health burden with rising prevalence. While Traditional Chinese Medicine (TCM) exhibits growing potential in NAFLD intervention, no domain-specific bibliometric evaluation currently exists. Utilising the most recent data from authoritative bibliographic databases, this study conducts a comprehensive bibliometric analysis to delineate the knowledge structure, research fronts, and collaborative networks in this field. Methods We searched for publications from 2000 to 2024 in the Web of Science Core Collection (WoSCC) database, encompassing a total of 855 papers. In addition, a supplementary search was conducted in the PubMed database to identify and analyze eligible clinical trials. Bibliometric analyses were performed utilising R software, VOSviewer, and CiteSpace. Results Investigations into TCM about NAFLD have indicated a general upward trajectory. China leads in research output, succeeded by the United States and South Korea. Shanghai University of Traditional Chinese Medicine is the preeminent cooperative institution. Ji G ranks as the most productive author in this field, whereas Younossi ZM emerges as the most frequently co-cited scholar. Among journals, Journal of Ethnopharmacology publishes the largest number of articles, while Hepatology receives the highest citation frequency. Key research themes include gut microbiota, network pharmacology, inflammation, insulin resistance, and lipid metabolism. Research hotspots primarily concentrate on the mechanisms by which TCM compounds, like berberine and Lingguizhugan Decoction, have garnered considerable attention, and the utilisation of contemporary research methodologies, such as network pharmacology, has markedly intensified. Conclusion This bibliometric analysis thoroughly outlines the current status and developmental tendencies of TCM research in NAFLD for the first time, offering significant references for future investigations in this domain.

Noncanonical amino acids (NCAAs) have emerged as essential building blocks in protein engineering and peptide drug development owing to their advantages in enhancing metabolic stability, membrane permeability, and resistance to proteolytic degradation. Accurate construction of 3D protein structures containing NCAAs is crucial for elucidating their functions, understanding molecular interactions, and enabling rational design. However, integrating NCAAs into state-of-the-art protein structure prediction frameworks─such as AlphaFold3─often results in chirality violations, steric clashes, and local geometric distortions. These issues likely reflect limited parametrization of nonstandard residues within current models. To address these challenges, we expanded the AMBER force field covering 139 NCAAs, and we developed an enhanced Amber-relax protocol named HighRelax. Unlike conventional workflows that are restricted to linear peptides composed of canonical amino acids, HighRelax is compatible with complex systems containing NCAAs and cyclic peptides and can be seamlessly integrated with structures generated by state-of-the-art models such as AlphaFold3. Our results demonstrate that HighRelax effectively reduces steric clashes, restores residue chirality, and improves overall structural quality. This method provides a general postprocessing strategy for refining NCAA-containing structures, facilitating their applications in molecular simulation, peptide drug design, and protein engineering.

Accurate drug toxicity prediction is vital for drug discovery, but existing atom-based methods are often computationally inefficient for complex molecules. This limitation creates a need for more efficient yet powerful predictive models. We introduce FGMA, a novel framework designed to address this challenge. FGMA integrates functional groups with molecular fingerprints for a multi-view molecular encoding. The framework extracts key functional groups, treating them as unified entities to reduce computational overhead. These are then combined with MACCS fingerprints using a crossattention mechanism to capture both local chemical motifs and global structural properties. Experimental results demonstrate that FGMA achieves high accuracy and robustness in toxicity prediction tasks. In addition, interpretability analyses successfully identified how specific structural features and functional groups within drug molecules contribute to toxicity outcomes. Our findings validate that combining functional groups with fingerprints is an effective strategy that balances computational efficiency and representational power. The model's interpretability offers valuable, actionable insights into structure-toxicity relationships, aiding in the design of safer compounds. FGMA offers an effective, efficient, and interpretable tool for drug toxicity analysis. It stands to accelerate the drug discovery pipeline by enabling faster and more insightful safety assessments of potential drug candidates.

We propose Mamba - Physics-Informed Neural Network(Mamba-PINN), a novel data–physics integrated resilience assessment model, to evaluate the anti-typhoon disturbance ability of coastal sewage treatment systems in Zhuhai. The increasing frequency of extreme weather events poses significant challenges to urban infrastructure; yet, existing methods often fail to capture the complex spatio-temporal dynamics of typhoon impacts. Our approach combines a Mamba neural network for high-frequency monitoring data processing with Physics-Informed Neural Networks (PINN) to quantify process recovery dynamics under typhoon conditions. The Mamba network extracts critical storm impact features, while the PINN embeds fluid inertia and microbial activity inhibition mechanisms to model system responses. Furthermore, we introduce a disturbance–recovery resilience index to provide a quantitative measure of system robustness, enabling targeted adaptive transformations for coastal sewage plants. The proposed method addresses the limitations of purely data-driven or physics-based models by integrating both paradigms, offering a more comprehensive understanding of resilience mechanisms. Experimental results demonstrate the model’s effectiveness in capturing nonlinear interactions between typhoon disturbances and treatment process recovery. This work contributes to the sustainable development of coastal cities by providing a scientifically grounded framework for infrastructure adaptation under climate change.