Hepatitis C virus (HCV) infection remains a significant global health burden, contributing to progressive hepatic pathologies including chronic hepatitis, cirrhosis, and hepatocellular carcinoma. While anti-hepatitis C peptides (AHCPs) have emerged as promising therapeutic candidates with distinct antiviral mechanisms, conventional wet-lab approaches for AHCP discovery face critical limitations in throughput and scalability. To overcome these constraints, we present Stack-Ahcp, an innovative stacked ensemble learning framework that synergistically integrates multiple machine learning algorithms through a meta-classification strategy. Our model achieves unprecedented predictive performance with 93.1% accuracy and an MCC of 0.863, substantially outperforming existing computational methods. Through comprehensive Shapley additive explanations (SHAP) analysis, we further delineate critical key intrinsic features and determinants governing AHCP bioactivity, enhancing the mechanistic interpretability of the prediction system. To facilitate translational applications, we have implemented an intuitive web interface (accessible at https://awi.cuhk.edu.cn/~biosequence/StackAHCP/index.php) that enables rapid screening and prioritization of candidate peptides. This resource is anticipated to streamline the identification of next-generation peptide therapeutics against HCV while reducing experimental validation costs. Beyond virology applications, our methodological framework establishes a paradigm for interpretable machine learning in biological sequence analysis, with potential adaptability to diverse multi-omics investigation scenarios.

RNA-protein interactions (RPIs) are essential for many biological functions and are associated with various diseases. Traditional methods for detecting RPIs are labor-intensive and costly, necessitating efficient computational methods. In this study, we proposed a novel sequence-based RPI prediction framework based on graph neural networks (GNNs) that addressed key limitations of existing methods, such as inadequate feature integration and negative sample construction. Our method represented RNAs and proteins as nodes in a unified interaction graph, enhancing the representation of RPI pairs through multi-feature fusion and employing self-supervised learning strategies for model training. The model's performance was validated through five-fold cross-validation, achieving accuracy of 0.880, 0.811, 0.950, 0.979, 0.910, and 0.924 on the RPI488, RPI369, RPI2241, RPI1807, RPI1446, and RPImerged datasets, respectively. Additionally, in cross-species generalization tests, our method outperformed existing methods, achieving an overall accuracy of 0.989 across 10 093 RPI pairs. Compared with other state-of-the-art RPI prediction methods, our approach demonstrates greater robustness and stability in RPI prediction, highlighting its potential for broad biological applications and large-scale RPI analysis.

Neuropeptides are essential signaling molecules produced in the nervous system that regulate diverse physiological processes and are closely implicated in the pathogenesis of neurodegenerative and neuropsychiatric disorders. Investigating neuropeptides contributes to a better understanding of their regulatory mechanisms and offers new insights into therapeutic strategies for related diseases. Therefore, accurate identification of neuropeptides is crucial for advancing biomedical research and drug development. Due to the high cost of experimental validation, various artificial intelligence methods have been developed for rapid neuropeptide identification. However, existing approaches often suffer from high computational resource consumption, slow processing speed, and poor deploy ability. Moreover, a user-friendly web server for practical application is still lacking. To this end, we propose MSKDNP, a neuropeptide prediction model based on a multi-stage knowledge distillation framework. With only 1.2% of the parameters, MSKDNP attains performance comparable to a fully fine-tuned protein language model while achieving state-of-the-art results in neuropeptide recognition. Moreover, MSKDNP provides favorable interpretability, facilitating biological understanding. A freely accessible web server is available at https://awi.cuhk.edu.cn/∼biosequence/MSKDNP/index.php.

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

Cancer is a severe illness that significantly threatens human life and health. Anticancer peptides (ACPs) represent a promising therapeutic strategy for combating cancer. In silico methods enable rapid and accurate identification of ACPs without extensive human and material resources. This study proposes a two-stage computational framework called ACP-CapsPred, which can accurately identify ACPs and characterize their functional activities across different cancer types. ACP-CapsPred integrates a protein language model with evolutionary information and physicochemical properties of peptides, constructing a comprehensive profile of peptides. ACP-CapsPred employs a next-generation neural network, specifically capsule networks, to construct predictive models. Experimental results demonstrate that ACP-CapsPred exhibits satisfactory predictive capabilities in both stages, reaching state-of-the-art performance. In the first stage, ACP-CapsPred achieves accuracies of 80.25% and 95.71%, as well as F1-scores of 79.86% and 95.90%, on benchmark datasets Set 1 and Set 2, respectively. In the second stage, tasked with characterizing the functional activities of ACPs across five selected cancer types, ACP-CapsPred attains an average accuracy of 90.75% and an F1-score of 91.38%. Furthermore, ACP-CapsPred demonstrates excellent interpretability, revealing regions and residues associated with anticancer activity. Consequently, ACP-CapsPred presents a promising solution to expedite the development of ACPs and offers a novel perspective for other biological sequence analyses.

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.

Substrate permissiveness has long been regarded as the raw materials for the evolution of new enzymatic functions. In land plants, hydroxycinnamoyltransferase (HCT) is an essential enzyme of the phenylpropanoid metabolism. Although essential enzymes are normally associated with high substrate specificity, HCT can utilize a variety of non-native substrates. To examine the structural and dynamic basis of substrate permissiveness in this enzyme, we report the crystal structure of HCT from Selaginella moellendorffii and molecular dynamics (MD) simulations performed on five orthologous HCTs from several major lineages of land plants. Through altogether 17-μs MD simulations, we demonstrate the prevalent swing motion of an arginine handle on a submicrosecond timescale across all five HCTs, which plays a key role in native substrate recognition by these intrinsically promiscuous enzymes. Our simulations further reveal how a non-native substrate of HCT engages a binding site different from that of the native substrate and diffuses to reach the catalytic center and its co-substrate. By numerically solving the Smoluchowski equation, we show that the presence of such an alternative binding site, even when it is distant from the catalytic center, always increases the reaction rate of a given substrate. However, this increase is only significant for enzyme-substrate reactions heavily influenced by diffusion. In these cases, binding non-native substrates 'off-center' provides an effective rationale to develop substrate permissiveness while maintaining the native functions of promiscuous enzymes.