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相关概念视频

Bacterial Translocation and Protein Secretion01:26

Bacterial Translocation and Protein Secretion

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Bacterial protein secretion involves translocation systems to ensure proteins reach their designated locations, including the plasma membrane, periplasm, outer membrane, or the external environment. These translocation systems are vital for bacterial physiology, supporting processes like membrane assembly, enzymatic activity in the periplasm, and interactions with the external environment. The division of labor between Sec and Tat pathways ensures efficiency in handling proteins with diverse...
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Gram-negative Bacterial Protein Secretion Systems01:17

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Gram-negative bacteria utilize sophisticated protein secretion systems to transport proteins across their double-membrane envelope into the extracellular environment or host cells. Based on their mechanism of action, these systems are classified into one-step and two-step pathways.One-Step Secretion Systems (Types I, III, IV, and VI)One-step secretion systems bypass the periplasm entirely, forming a continuous channel that spans both the inner and outer membranes:Type I Secretion System (T1SS):...
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Updated: Feb 20, 2026

Modifying Baculovirus Expression Vectors to Produce Secreted Plant Proteins in Insect Cells
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一个基于深度学习的服务器SecEff-Pred用于预测信号分泌效率,以改善Bacillus subtilis中的蛋白质生产.

Hui Sun1, Xiangbo Meng1, Linhao Meng1

  • 1Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, P. R. China.

Journal of agricultural and food chemistry
|February 19, 2026
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概括

一个新的工具SecEff-Pred准确地预测了Bacillus subtilis蛋白质生产中的信号 (SP) 效率. 这种计算方法通过识别工业应用的有效信号来增强蛋白质分泌.

关键词:
这种细菌是 Bacillus subtilis.深度学习是一种深度学习.的分泌效率提高了分泌效率.信号是一种信号.

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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 信号 (SP) 对于细菌物种的蛋白质分泌至关重要,影响生产效率.
  • 当前的计算工具在预测信号分泌效率方面缺乏可靠性.
  • 精确预测SP效率对于优化重组蛋白质生产至关重要.

研究的目的:

  • 开发一种新的计算工具SecEff-Pred,用于预测Bacillus subtilis中的信号效率.
  • 使用先进的机器学习技术,提高信号效率预测的准确性.
  • 为研究Bacillus物种蛋白质分泌工作的研究人员提供可靠的Web服务器.

主要方法:

  • 开发 SecEff-Pred,一个使用基于 ESM-2 的预测器的 Web 服务器.
  • 实施创新的数据模拟策略和多任务学习框架.
  • 使用像脂酶D (PLD) 这样的记者蛋白质验证SecEff-Pred.

主要成果:

  • 对于各种蛋白质,SecEff-Pred的预测准确度很高:α-amylase的85.59%,性西兰酶的81.58%和cutinase的74.68%.
  • 使用PLD验证显示,信号效率的整体预测准确率为72%.
  • 该工具显示,高效的SP的准确率为80%,低效的SP为62.50%.

结论:

  • SecEff-Pred是一种强大而准确的计算工具,用于预测Bacillus subtilis中的信号效率.
  • 开发的工具可以显著指导和优化B. subtilis.中的蛋白质分泌过程.
  • SecEff-Pred提供了一种可靠的解决方案,通过改进的信号选择来增强蛋白质的生产.