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Receptor-binding proteins (RBPs) of bacteriophages initiate the infection of their corresponding bacterial host and act as the primary determinant for host specificity. The ever-increasing amount of sequence data enables the development of predictive models for the automated identification of RBP sequences. However, the development of such models is challenged by the inconsistent or missing annotation of many phage proteins. Recently developed tools have started to bridge this gap but are not specifically focused on RBP sequences, for which many different annotations are available. We have developed two parallel approaches to alleviate the complex identification of RBP sequences in phage genomic data. The first combines known RBP-related hidden Markov models (HMMs) from the Pfam database with custom-built HMMs to identify phage RBPs based on protein domains. The second approach consists of training an extreme gradient boosting classifier that can accurately discriminate between RBPs and other phage proteins. We explained how these complementary approaches can reinforce each other in identifying RBP sequences. In addition, we benchmarked our methods against the recently developed PhANNs tool. Our best performing model reached a precision-recall area-under-the-curve of 93.8% and outperformed PhANNs on an independent test set, reaching an F1-score of 84.0% compared to 69.8%. [ABSTRACT FROM AUTHOR]

Nowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs. [ABSTRACT FROM AUTHOR]

The Belgian Society for Viruses of Microbes (BSVoM) was founded on 9 June 2022 to capture and enhance the collaborative spirit among the expanding community of microbial virus researchers in Belgium. The sixteen founders are affiliated to fourteen different research entities across academia, industry and government. Its inaugural symposium was held on 23 September 2022 in the Thermotechnical Institute at KU Leuven. The meeting program covered three thematic sessions launched by international keynote speakers: (1) virus–host interactions, (2) viral ecology, evolution and diversity and (3) present and future applications. During the one-day symposium, four invited keynote lectures, ten selected talks and eight student pitches were given along with 41 presented posters. The meeting hosted 155 participants from twelve countries. [ABSTRACT FROM AUTHOR]

The International Virus Bioinformatics Meeting 2022 took place online, on 23–25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus–host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting. [ABSTRACT FROM AUTHOR]

Citrobacter koseri is an emerging Gram-negative bacterial pathogen, which causes urinary tract infections. We isolated and characterized a novel S16-like myovirus CKP1 (vB_CkoM_CkP1), infecting C. koseri. CkP1 has a host range covering the whole C. koseri species, i.e., all strains that were tested, but does not infect other species. Its linear 168,463-bp genome contains 291 coding sequences, sharing sequence similarity with the Salmonella phage S16. Based on surface plasmon resonance and recombinant green florescence protein fusions, the tail fiber (gp267) was shown to decorate C. koseri cells, binding with a nanomolar affinity, without the need of accessory proteins. Both phage and the tail fiber specifically bind to bacterial cells by the lipopolysaccharide polymer. We further demonstrate that CkP1 is highly stable towards different environmental conditions of pH and temperatures and is able to control C. koseri cells in urine samples. Altogether, CkP1 features optimal in vitro characteristics to be used both as a control and detection agent towards drug-resistant C. koseri infections. Key points: • CkP1 infects all C. koseri strains tested • CkP1 recognizes C. koseri lipopolysaccharide through its long tail fiber • Both phage CkP1 and its tail fiber can be used to treat or detect C. koseri pathogens [ABSTRACT FROM AUTHOR]