Post-translational modifications are important for regulating cellular functions. Although traditional experimental methods accurately identify PTM sites, they are time-consuming. In this study, we propose a novel model capable of predicting 17 types of PTMs through multi-modal integration and AlphaFold predictions. Our model employs an enhanced CNN-transformer architecture to capture local dependencies within the sequence, while incorporating structural features and evolutionary patterns to effectively capture complex spatial relationships and global contextual dependencies. Through rigorous cross-validation and testing, our model demonstrates exceptional performance, achieving area under the curve scores of 96.5%, 91.6%, 91.0%, and 89.5% for the prediction of hydroxylation, malonylation, O-linked glycosylation, and phosphorylation, respectively. Notably, our model accurately identified known phosphorylation sites on tau and two recently identified residues linked to pre-tangle stages and early Alzheimer's disease pathology. This work not only deepens the understanding of PTMs but also holds promise for advancing future research in the prediction of PTM sites and functional annotation.

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.

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently, the deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this paper, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool. We formulate the objective function as a constrained optimization problem and solve it using the alternating direction method of multipliers algorithm. Both simulation data and hybrid real data are used to evaluate the proposed method. Quantification results show that our proposed iterative neural network method can outperform the neural network denoising and conventional penalized maximum likelihood methods.