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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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通过KNIME工作流程检查抗微生物分类中的进化规模建模衍生的不同维的嵌入.

Karla L Martínez-Mauricio1, César R García-Jacas2, Greneter Cordoves-Delgado1

  • 1Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Mexico.

Protein science : a publication of the Protein Society
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概括

这项研究表明,来自进化规模建模 (ESM-2) 模型的特定嵌入有效地分类抗微生物 (AMP). 使用这些特征的非深度学习量化结构-活动关系 (QSAR) 模型的性能与深度学习方法相比或更好.

关键词:
欧洲经济机制-2 (ESM-2) 是一个.一种类型的机械.在QSAR中使用QSAR.抗微生物类的抗微生物.深度学习是一种深度学习.整体分类器 整体分类器进化规模建模 进化规模建模浅级分类器是浅级分类器.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 化学信息学 化学信息学
  • 计算生物学 计算生物学
  • 机器学习在药物发现中的作用

背景情况:

  • 分子特征对于定量结构-活动关系 (QSAR) 建模至关重要.
  • 预训练的模型,如进化规模建模 (ESM-2),为下游任务提供了强大的嵌入.
  • 在蛋白质结构预测方面,ESM-2模型显示出了前景.

研究的目的:

  • 评估ESM-2模型嵌入用于分类抗微生物 (AMP) 的实用性.
  • 将使用ESM-2嵌入式构建的QSAR模型与最先进的深度学习模型的性能进行比较.
  • 开发可重现的工作流程,以公平地比较计算方法.

主要方法:

  • 使用KNIME工作流程来确保QSAR模型开发的一致方法.
  • 从不同的ESM-2模型 (30层和33层) 中提取了不同尺寸的嵌入物.
  • 使用单个ESM-2模型嵌入,融合嵌入构建和比较QSAR模型,并与深度学习模型进行比较.

主要成果:

  • 来自30层ESM-2的640维嵌入和来自33层ESM-2模型的1280维嵌入产生了最好的QSAR模型性能.
  • 与使用单个模型相比,来自多个ESM-2模型的融合功能改善了QSAR模型的性能.
  • 频率分析表明,只有ESM-2嵌入的子集 (43-66%) 被积极用于建模.
  • 非深度学习的QSAR模型,当使用原则方法开发时,实现了与AMP预测深度学习模型相比的或更高的性能.

结论:

  • 特定的ESM-2嵌入对于基于QSAR的AMP分类非常有价值.
  • 来自多个ESM-2模型的特征融合增强了预测能力.
  • 开发的KNIME工作流程促进了公平的比较,并提出了新的非深度学习QSAR模型.
  • 从方法上讲,非深度学习的QSAR模型仍然可以与AMP预测的深度学习方法竞争.