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Quorum sensing is a mechanism of bacterial communication that enables coordinated gene expression in response to changes in population density. This facilitates collective behaviors that enhance survival, resource acquisition, and ecological adaptation. This process relies on small signaling molecules called autoinducers that accumulate as bacterial populations grow. When a critical threshold concentration of autoinducers is reached, bacterial cells collectively modify gene expression,...
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TG-CDDPM:基于条件无声扩散概率模型的文本导向抗微生物生成.

Junhang Cao1, Jun Zhang2, Qiyuan Yu1

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.

Briefings in bioinformatics
|December 12, 2024
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概括

一个新的文本导向条件否定扩散概率模型 (TG-CDDPM) 产生了多种抗菌 (AMP). 这种方法克服了传统发现和其他深度学习模型的局限性,显示了新型抗生素开发的前景.

关键词:
抗微生物类的抗微生物.扩散模型的扩散模型.精细调整 精细调整预先培训的培训前培训文本指导 文本指导

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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
  • 药物发现 药物发现 药物发现

背景情况:

  • 抗微生物 (AMP) 是抗生素的一个有希望的替代品,因为它们具有广泛的活性,低耐药性和毒性.
  • 传统的AMP发现方法是低效和昂贵的.
  • 现有的AMP深度生成模型往往缺乏生成序列的多样性.

研究的目的:

  • 开发一种用于生产多样化和同源抗微生物 (AMP) 的新型生成模型.
  • 解决现有的深度学习模型在生成各种AMP序列方面的局限性.
  • 验证建议模型在AMP发现中的有效性和能力.

主要方法:

  • 提出了一个三阶段的文本导向条件否定扩散概率模型 (TG-CDDPM).
  • 在指导AMP生成的初始阶段使用了对比学习和推断模型.
  • 采用预先训练的条件否定扩散概率模型来丰富和微调知识.

主要成果:

  • 与最先进的生成模型相比,TG-CDDPM表现出具有竞争力或优越的性能.
  • 该模型成功生成了具有增强多样性的新型和同类AMP.
  • 由TG-CDDPM识别的候选AMP通过分子动力学实验显示了经过验证的膜透能力.

结论:

  • TG-CDDPM是一种有效的深度生成模型,用于发现各种抗微生物.
  • 文本导向生成显著提高了AMP的质量和多样性.
  • 这种方法为加速发现新型AMP作为潜在抗生素提供了一个强大的工具.