Bacteriophages are viruses that target bacteria, playing a crucial role in microbial ecology. Phage proteins are important in understanding phage biology, such as virus infection, replication, and evolution. Although a large number of new phages have been identified via metagenomic sequencing, many of them have limited protein function annotation. Accurate function annotation of phage proteins presents several challenges, including their inherent diversity and the scarcity of annotated ones. Existing tools have yet to fully leverage the unique properties of phages in annotating protein functions. In this work, we propose a new protein function annotation tool for phages by leveraging the modular genomic structure of phage genomes. By employing embeddings from the latest protein foundation models and Transformer to capture contextual information between proteins in phage genomes, GOPhage surpasses state-of-the-art methods in annotating diverged proteins and proteins with uncommon functions by 6.78% and 13.05% improvement, respectively. GOPhage can annotate proteins lacking homology search results, which is critical for characterizing the rapidly accumulating phage genomes. We demonstrate the utility of GOPhage by identifying 688 potential holins in phages, which exhibit high structural conservation with known holins. The results show the potential of GOPhage to extend our understanding of newly discovered phages.

MOTIVATION: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. There exist some plasmids that can transfer, replicate, or persist in multiple organisms. Identifying the relatively complete host range of these plasmids provides insights into how plasmids promote bacterial evolution. To achieve this, we can apply multi-label learning models for plasmid host range prediction. However, there are no databases providing the detailed and complete host labels of these broad-host-range plasmids. Without adequate well-annotated training samples, learning models can fail to extract discriminative feature representations for plasmid host prediction. RESULTS: To address this problem, we propose a self-correction multi-label learning model called MOSTPLAS. We design a pseudo label learning algorithm and a self-correction asymmetric loss to facilitate the training of multi-label learning model with samples containing some unknown missing labels. We conducted a series of experiments on the NCBI RefSeq plasmid database, the PLSDB 2025 database, plasmids with experimentally determined host labels, the Hi-C dataset, and the DoriC dataset. The benchmark results against other plasmid host range prediction tools demonstrated that MOSTPLAS recognized more host labels while keeping a high precision. AVAILABILITY AND IMPLEMENTATION: MOSTPLAS is implemented with Python, which can be downloaded at https://github.com/wzou96/MOSTPLAS. All relevant data we used in the experiments can be found at https://zenodo.org/doi/10.5281/zenodo.14708999.

MOTIVATION: Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods. RESULTS: We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ∼70% and maintains ±1.5% accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently. AVAILABILITY AND IMPLEMENTATION: The source code of MTDP is available via https://github.com/KennthShang/MTDP.

MOTIVATION: As prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts' evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs. RESULTS: In this work, we construct a hierarchical classification tool named HOTSPOT, whose backbone is a phylogenetic tree of the bacterial hosts from phylum to species. By incorporating the state-of-the-art language model, Transformer, in each node's taxon classifier, the top-down tree search achieves an accurate host taxonomy prediction for the input plasmid contigs. We rigorously tested HOTSPOT on multiple datasets, including RefSeq complete plasmids, artificial contigs, simulated metagenomic data, mock metagenomic data, the Hi-C dataset, and the CAMI2 marine dataset. All experiments show that HOTSPOT outperforms other popular methods. AVAILABILITY AND IMPLEMENTATION: The source code of HOTSPOT is available via: https://github.com/Orin-beep/HOTSPOT.

The platoon-based driving pattern, a cooperative driving pattern for a leader and a number of followers, is a good way to deal with some traditional traffic issues and brings many benefits when performing special platoon tasks. However, it's possible for each platoon member to be fully controlled by attackers, especially the leader, which may cause severe damage to the platoon stability. In this paper, we propose a vehicle platoon leader updating scheme in vehicular networks based on blockchain techniques and reputation management mechanism, so as to ensure that the most trusted platoon member acts as leader. In this scheme, according to received warning messages of traffic events, each platoon member evaluates others' reputations in the form of offsets. Using the designed reputation blockchain with Delegated Proof-of-Stake (DPoS) consensus scheme, miners generate blocks in turn and wait for others' verification. If a block successfully passes the consensus process, it will be formally added to the blockchain, which can reflect all platoon members' current reputation values. In addition, we also have to guarantee that the miner group is composed of several platoon members with high reputation and the leader serves as a miner. In this way, the scheme both meets the real-time requirement of reputation management and saves communication overhead compared with existing schemes. Finally, we analyze the proper functioning and security of our proposed scheme.