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Bacteriophages

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Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.

Microbiome
BACKGROUND: Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising...

Prediction of Klebsiella phage-host specificity at the strain level.

Nature communications
Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools d...

Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant cha...

DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Briefings in bioinformatics
Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temper...

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes.

Briefings in bioinformatics
Phages, the natural predators of bacteria, were discovered more than 100 years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments...

Improving viral annotation with artificial intelligence.

mBio
Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environme...

Predicting phage-host interactions via feature augmentation and regional graph convolution.

Briefings in bioinformatics
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their...

Bacteriophage-Activated DNAzyme Hydrogels Combined with Machine Learning Enable Point-of-Use Colorimetric Detection of Escherichia coli.

Advanced materials (Deerfield Beach, Fla.)
Developing cost-effective, consumer-accessible platforms for point-of-use environmental and clinical pathogen testing is a priority, to reduce reliance on laborious, time-consuming culturing approaches. Unfortunately, a system offering ultrasensitive...

PhageDPO: A machine-learning based computational framework for identifying phage depolymerases.

Computers in biology and medicine
Bacteriophages (phages) are the most predominant and genetically diverse biological entities on Earth. Phages are viruses that infect bacteria and encode numerous proteins with potential biotechnological application. However, most phage-encoded prote...

PharaCon: a new framework for identifying bacteriophages via conditional representation learning.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. Ho...