Neuropeptides are a diverse and complex class of signaling molecules that regulate a variety of biological processes. Neuropeptides provide many opportunities for the discovery of new drugs and targets for the treatment of a wide range of diseases, and thus, computational tools for the rapid and accurate large-scale identification of neuropeptides are of great significance for peptide research and drug development. Although several machine learning-based prediction tools have been developed, there is room for improvement in the performance and interpretability of the proposed methods. In this work, we developed an interpretable and robust neuropeptide prediction model, named NeuroPred-PLM. First, we employed a language model (ESM) of proteins to obtain semantic representations of neuropeptides, which could reduce the complexity of feature engineering. Next, we adopted a multi-scale convolutional neural network to enhance the local feature representation of neuropeptide embeddings. To make the model interpretable, we proposed a global multi-head attention network that could be used to capture the position-wise contribution to neuropeptide prediction via the attention scores. In addition, NeuroPred-PLM was developed based on our newly constructed NeuroPep 2.0 database. Benchmarks based on the independent test set show that NeuroPred-PLM achieves superior predictive performance compared with other state-of-the-art predictors. For the convenience of researchers, we provide an easy-to-install PyPi package (https://pypi.org/project/NeuroPredPLM/) and a web server (https://huggingface.co/spaces/isyslab/NeuroPred-PLM).

In recent years, viral pneumonia has become a significant challenge to global public health, particularly during the COVID-19 pandemic. Viral pneumonia can be caused by various viruses, including influenza virus, RSV, and adenovirus. These viruses trigger inflammatory responses by invading the respiratory epithelial cells, leading to lung damage. Existing antiviral drugs such as ribavirin, adobiravir, and oseltamivir exert their therapeutic effects by inhibiting different stages of the viral life cycle but face issues such as increasing drug resistance. Natural components like astragalus saponins, Houttuynia cordata flavonoids, and tea theaflavin-gallates have demonstrated supportive roles in antiviral treatments, capable of not only enhancing immune responses but also potentially inhibiting viral replication through multiple pathways, thereby alleviating lung damage. Although natural components cannot entirely replace traditional antiviral drugs, their role in comprehensive treatment regimens is becoming increasingly important. This review summarizes the current applications and limitations of antiviral drugs and explores the research progress and potential mechanisms of natural components in the treatment of viral pneumonia.

We analyzed eight oral microbiota shotgun metagenomic sequencing cohorts from five countries and three continents, identifying 54 species biomarkers and 26 metabolic biomarkers consistently altered in health and disease states across three or more cohorts. Additionally, machine learning models based on taxonomic profiles achieved high accuracy in distinguishing periodontitis patients from controls (internal and external areas under the receiver operating characteristic curves of 0.86 and 0.85, respectively). These results support metagenome-based diagnosis of periodontitis and provide a foundation for further research and effective treatment strategies.

Transmembrane proteins (TMPs) are essential for cell recognition and communication, and they serve as important drug targets in humans. Transmembrane proteins' 3D structures are critical for determining their functions and drug design but are hard to determine even by experimental methods. Although some computational methods have been developed to predict transmembrane helices (TMHs) and orientation, there is still room for improvement. Considering that the pre-trained language model can make full use of massive unlabeled protein sequences to obtain latent feature representation for TMPs and reduce the dependence on evolutionary information, we proposed DeepTMpred, which used pre-trained self-supervised language models called ESM, convolutional neural networks, attentive neural network and conditional random fields for alpha-TMP topology prediction. Compared with the current state-of-the-art tools on a non-redundant dataset of TMPs, DeepTMpred demonstrated superior predictive performance in most evaluation metrics, especially at the TMH level. Furthermore, DeepTMpred could also obtain reliable prediction results for TMPs without much evolutionary feature in a few seconds. A tutorial on how to use DeepTMpred can be found in the colab notebook (https://colab.research.google.com/github/ISYSLAB-HUST/DeepTMpred/blob/master/notebook/test.ipynb).