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相关概念视频

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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Viral Replication: Lysogenic Cycle01:16

Viral Replication: Lysogenic Cycle

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The lysogenic cycle is a crucial viral replication strategy that allows bacteriophages to persist within host cells without immediately destroying them. This process is primarily observed in temperate phages, such as bacteriophage lambda (λ), which infects Escherichia coli. The cycle allows the viral genome to persist across bacterial generations while keeping host cells viable.Integration of the Viral GenomeUpon infection, bacteriophage lambda attaches to the bacterial surface and injects...
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Lysogenic Cycle of Bacteriophages00:43

Lysogenic Cycle of Bacteriophages

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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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DNA Bacteriophages01:26

DNA Bacteriophages

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Bacteriophages, or phages, are viruses that specifically infect bacteria, utilizing their genetic material to hijack host cellular machinery for replication. DNA bacteriophages employ single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA) genomes. These phages exhibit diverse replication strategies and host interactions, influencing their ecological roles and applications in biotechnology and medicine.ssDNA BacteriophagesssDNA phages, with their small genomes, utilize unique strategies to...
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相关实验视频

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Author Spotlight: Investigating Bacteriophage-Induced Immune Responses in Gnotobiotic Mice
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PTBGRP:通过在微生物异质信息网络上的图形表示学习来预测菌-细菌相互作用.

Jie Pan1, Zhuhong You2, Wencai You1

  • 1Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China.

Briefings in bioinformatics
|September 24, 2023
PubMed
概括

我们开发了PTBGRP,这是一种新的计算模型,集成微生物网络来预测用于治疗细菌感染的菌体 (菌体). 这种方法通过考虑菌体-细菌相互作用中的更高阶连接模式来提高准确性.

关键词:
图表表示学习学习学习图表表示学习微生物异质相互作用网络的微生物.菌细菌的相互作用

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 菌体 (菌体) 对于对抗细菌感染至关重要,需要有效的预测方法.
  • 目前用于菌体预测的计算方法往往忽略了复杂的,高阶的生物网络模式.
  • 整合多样化的生物信息可以提高预测菌-细菌相互作用的准确性.

研究的目的:

  • 开发一种新的计算模型,PTBGRP,用于预测菌候选人对抗细菌宿主.
  • 通过结合更高阶网络特征来提高菌-细菌相互作用 (PBI) 预测的准确性.
  • 为 PTBGRP 预测器提供免费访问的 Web 服务器.

主要方法:

  • 构建了一个微生物异质相互作用网络 (MHIN),集成PBI和细菌-细菌相互作用数据.
  • 员工代表学习从MHIN中提取高级生物和拓特征.
  • 利用深度神经网络分类器来根据融合特征预测未知的PBI对.

主要成果:

  • 与最先进的方法相比,PTBGRP在ESKAPE病原体数据集上的表现优越.
  • 对Klebsiella pneumoniae和Staphylococcus aureus的案例研究验证了PTBGRP的预测准确性.
  • 该模型的有效性归因于对异质生物信息的全面整合.

结论:

  • PTBGRP模型提供了一种先进的计算方法,用于识别潜在的菌体疗法.
  • 整合更高层次的网络连接可显著提高菌体-细菌相互作用的预测.
  • PTBGRP为研究人员在对抗细菌病原体方面提供了宝贵的工具.