For any given bacteriophage genome or phage-derived sequences in metagenomic data sets, we are unable to assign a function to 50–90% of genes, or more. Structural protein-encoding genes constitute a large fraction of the average phage genome and are among the most divergent and difficult-to-identify genes using homology-based methods. To understand the functions encoded by phages, their contributions to their environments, and to help gauge their utility as potential phage therapy agents, we have developed a new approach to classify phage ORFs into ten major classes of structural proteins or into an “other” category. The resulting tool is named PhANNs (Phage Artificial Neural Networks). We built a database of 538,213 manually curated phage protein sequences that we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering method that ensures there are no homologous proteins between sets yet maintains the maximum sequence diversity for training. An Artificial Neural Network ensemble trained on features extracted from those sets reached a test F₁-score of 0.875 and test accuracy of 86.2%. PhANNs can rapidly classify proteins into one of the ten structural classes or, if not predicted to fall in one of the ten classes, as “other,” providing a new approach for functional annotation of phage proteins. PhANNs is open source and can be run from our web server or installed locally.

Motivation: Phage therapy offers a viable alternative for bacterial infections amid rising antimicrobial resistance. Its success relies on selecting safe and effective phage candidates that require comprehensive genomic screening to identify potential risks. However, this process is often labor intensive and time-consuming, hindering rapid clinical deployment. Results: We developed Sphae, an automated bioinformatics pipeline designed to streamline the therapeutic potential of a phage in under 10 minutes. Using Snakemake workflow manager, Sphae integrates tools for quality control, assembly, genome assessment, and annotation tailored specifically for phage biology. Sphae automates the detection of key genomic markers, including virulence factors, antimicrobial resistance genes, and lysogeny indicators such as integrase, recombinase, and transposase, which could preclude therapeutic use. Among the 65 phage sequences analyzed, 28 showed therapeutic potential, 8 failed due to low sequencing depth, 22 contained prophage or virulent markers, and 23 had multiple phage genomes. This workflow produces a report to assess phage safety and therapy suitability quickly. Sphae is scalable and portable, facilitating efficient deployment across most high-performance computing and cloud platforms, accelerating the genomic evaluation process. Availability and implementation: Sphae source code is freely available at https://github.com/linsalrob/sphae, with installation supported on Conda, PyPi, Docker containers.

Volume 61, no. 10, e00954-17, 2017, [https://doi.org/10.1128/AAC.00954-17][1]. Materials and Methods, 2nd paragraph, 2nd sentence: “Phages provided by the Biological Defense Research Directorate (BDRD) of the Naval Medical Research Center (AB-Navy1, AB-Navy4, AB-Navy71, AB-Navy97, and AbTP3Φ1)

For any given bacteriophage genome or phage sequences in metagenomic data sets, we are unable to assign a function to 50-90% of genes. Structural protein-encoding genes constitute a large fraction of the average phage genome and are among the most divergent and difficult-to-identify genes using homology-based methods. To understand the functions encoded by phages, their contributions to their environments, and to help gauge their utility as potential phage therapy agents, we have developed a new approach to classify phage ORFs into ten major classes of structural proteins or into an “other” category. The resulting tool is named PhANNs (Phage Artificial Neural Networks). We built a database of 538,213 manually curated phage protein sequences that we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering method that ensures there are no homologous proteins between sets yet maintains the maximum sequence diversity for training. An Artificial Neural Network ensemble trained on features extracted from those sets reached a test F ₁ -score of 0.875 and test accuracy of 86.2%. PhANNs can rapidly classify proteins into one of the ten classes, and non-phage proteins are classified as “other”, providing a new approach for functional annotation of phage proteins. PhANNs is open source and can be run from our web server or installed locally. Author Summary: Bacteriophages (phages, viruses that infect bacteria) are the most abundant biological entity on Earth. They outnumber bacteria by a factor of ten. As phages are very different within them and from bacteria, and we have comparatively few phage genes in our database, we are unable to assign function to 50%-90% of phage genes. In this work, we developed PhANNs, a machine learning tool that can classify a phage gene as one of ten structural roles, or “other”. This approach does not require a similar gene to be known.

Shiga toxin-producing Escherichia coli (STEC) colonizes the human intestine, causing haemorrhagic colitis and haemolytic uraemic syndrome (HUS). Treatment options are limited to intravenous fluids in part because sublethal doses of some antibiotics have been shown to stimulate increased toxin release and enhance the risk of progression to HUS. Preventative antimicrobial agents, especially those that build on the natural antimicrobial action of the host defence, may provide a better option. In order to survive the acid stress of gastric passage, STEC is equipped with numerous acid resistance and DNA repair mechanisms. Inhibition of acid-induced DNA repair may offer a strategy to target survival of ingested STEC. We report here that brief pretreatment with a novel antimicrobial peptide, which was previously shown to inhibit bacterial DNA repair, significantly and profoundly reduces survival of acid-stressed O157 : H7 and non-O157 : H7 STEC seropathotypes that are highly associated with HUS. Reduction in survival rates of STEC range from 3 to 5 log. We also show that peptide/acid treatment results in little or no increase in toxin production, thereby reducing the risk of progression to HUS. This study identifies the peptide wrwycr as a potential new candidate for a preventative antimicrobial for STEC infection.