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Related Concept Videos

Lytic Cycle of Bacteriophages01:30

Lytic Cycle of Bacteriophages

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Bacteriophages, also known as phages, are specialized viruses that infect bacteria. A key characteristic of phages is their distinctive “head-tail” morphology. A phage begins the infection process (i.e., lytic cycle) by attaching to the outside of a bacterial cell. Attachment is accomplished via proteins in the phage tail that bind to specific receptor proteins on the outer surface of the bacterium. The tail injects the phage’s DNA genome into the bacterial cytoplasm. In the...
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Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...
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Lysogenic Cycle of Bacteriophages00:43

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In contrast to the lytic cycle, phages infecting bacteria via the lysogenic cycle do not immediately kill their host cell. Instead, they combine their genome with the host genome, allowing the bacteria to replicate the phage DNA along with the bacterial genome. The incorporated copy of the phage genome is called the prophage. Some prophages can re-activate and enter the lytic cycle. This often occurs in response to a perturbation, such as DNA damage, but can also transpire in the absence of...
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Optimizing phage therapy with artificial intelligence: a perspective.

Michael B Doud1, Jacob M Robertson2, Steffanie A Strathdee1

  • 1Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, San Diego, CA, United States.

Frontiers in Cellular and Infection Microbiology
|June 11, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing phage therapy by analyzing phage-host interactions to design better treatments. This approach aims to overcome limitations in manually screening phages for antimicrobial resistance, paving the way for optimized phage therapeutics.

Keywords:
artificial intelligencegene discoverymachine learningphage engineeringphage specificityphage therapysynthetic biology

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Synthetic Biology

Background:

  • Phage therapy offers a promising alternative to antibiotics for combating antimicrobial resistance.
  • Current phage therapy relies on laborious manual screening of phages against specific bacterial isolates, often yielding suboptimal results.
  • The inherent diversity of phages and bacteria complicates the development of effective phage therapeutics.

Purpose of the Study:

  • To review recent advancements in artificial intelligence (AI) for studying phage-host interactions.
  • To explore how AI can accelerate the design of effective phage therapeutics.
  • To envision the integration of AI with synthetic biology for next-generation phage development.

Main Methods:

  • Review of current literature on AI applications in phage biology.
  • Analysis of AI-driven approaches for predicting and understanding phage-host dynamics.
  • Discussion of synthetic biology tools for phage genetic engineering.

Main Results:

  • AI significantly enhances the understanding of complex phage-host interactions.
  • AI-driven insights can guide the selection and optimization of phages for therapeutic use.
  • The synergy between AI and synthetic biology holds potential for creating tailored phage treatments.

Conclusions:

  • AI is a powerful tool for advancing phage therapy research and development.
  • AI-driven approaches can streamline the identification of potent phage candidates.
  • Future phage therapeutics may be genetically optimized using AI-derived knowledge, leading to more effective treatments against resistant bacteria.