Viruses are ubiquitous in nature, yet our understanding of them remains limited. High-throughput sequencing technology facilitates the unbiased revelation of genetic composition in samples; however, viral sequences typically make up a small proportion of the entire sequencing data, making it challenging to accurately identify the few or fragmented viral sequences present in a sample. The limited features and information provided by short sequences result in insufficient resolution of viral sequences by existing models. Therefore, we propose a new model, VirNucPro, for short viral sequence identification. Based on a six-frame translation strategy and large language models, we combine nucleotide and amino acid sequence information to enhance feature extraction for short sequences, achieving high accuracy in identifying short viral sequences. Ablation experiments compared the contributions of nucleotide and amino acid sequence features to the model, confirming that the introduced amino acid features significantly contribute to the classification results. Our model outperforms others, such as GCNFrame, DeepVirFinder, DETIRE, and Virtifier, which have demonstrated good performance in identifying short viral sequences of 300 and 500 bp. Our model demonstrates excellent performance on carefully created real-world datasets. Additionally, it can scan for prophage regions within long bacterial fragments, offering a wide range of applications. The codes are available at: https://github.com/Li-Jing-1997/VirNucPro.

BACKGROUND: Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability. RESULTS: This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC. CONCLUSIONS: ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.

Antiviral therapies targeting the pandemic coronavirus disease 2019 (COVID-19) are urgently required. We studied an already-approved botanical drug cepharanthine (CEP) in a cell culture model of GX_P2V, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related virus. RNA-sequencing results showed the virus perturbed the expression of multiple genes including those associated with cellular stress responses such as endoplasmic reticulum (ER) stress and heat shock factor 1 (HSF1)-mediated heat shock response, of which heat shock response-related genes and pathways were at the core. CEP was potent to reverse most dysregulated genes and pathways in infected cells including ER stress/unfolded protein response and HSF1-mediated heat shock response. Additionally, single-cell transcriptomes also confirmed that genes of cellular stress responses and autophagy pathways were enriched in several peripheral blood mononuclear cells populations from COVID-19 patients. In summary, this study uncovered the transcriptome of a SARS-CoV-2-related coronavirus infection model and anti-viral activities of CEP, providing evidence for CEP as a promising therapeutic option for SARS-CoV-2 infection.

Upon the original publication of this article, two author names were incorrectly given as ``Ren zhong Qiao'' and ``Yi-gang Tong''. These should read: ``Renzhong Qiao'' and ``Yigang Tong''. As well as this, separating semi-colons were missing from the keywords. These errors have now been corrected online.

Targeting the interaction between severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)-receptor-binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2) is believed to be an effective strategy for drug design to inhibit the infection of SARS-CoV-2. Herein, several ultrashort peptidase inhibitors against the RBD-ACE2 interaction were obtained by a computer-aided approach based on the RBD-binding residues on the protease domain (PD) of ACE2. The designed peptides were tested on a model coronavirus GX_P2V, which has 92.2 and 86% amino acid identity to the SARS-CoV-2 spike protein and RBD, respectively. Molecular dynamics simulations and binding free energy analysis predicted a potential binding pocket on the RBD of the spike protein, and this was confirmed by the specifically designed peptides SI5α and SI5α-b. They have only seven residues, showing potent antiviral activity and low cytotoxicity. Enzyme-linked immunosorbent assay result also confirmed their inhibitory ability against the RBD-ACE2 interaction. The ultrashort peptides are promising precursor molecules for the drug development of Corona Virus Disease 2019, and the novel binding pocket on the RBD may be helpful for the design of RBD inhibitors or antibodies against SARS-CoV-2.