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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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简单,高效和可扩展的结构意识适配器提高了蛋白质语言模型.

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概括
此摘要是机器生成的。

通过整合结构信息,SES-Adapter 增强了蛋白质语言模型 (PLM),提高了下游任务性能,加快了融合. 这种方法甚至在低质量的结构数据中也提供了显著的收益.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 微调预训练的蛋白质语言模型 (PLMs) 对于下游预测任务至关重要.
  • 来自自然语言处理的参数高效微调技术显示出希望,但在生命科学中面临挑战.
  • 调整这些技术需要解决培训策略和数据格式的差异.

研究的目的:

  • 引入SES-Adapter,这是一种用于增强生命科学中PLM表示学习的新方法.
  • 通过将PLM嵌入与结构序列嵌入相结合,创建结构意识的蛋白质表示.
  • 为了证明SES-Adapter在各种PLM架构和任务中的效率和可扩展性.

主要方法:

  • 开发了SES-Adapter,这是一个参数高效的微调方法.
  • 集成的蛋白质语言模型嵌入与结构序列嵌入.
  • 通过使用9个基准数据集和2种类型的折叠结构,对各种下游任务进行SES-Adapter的评估.

主要成果:

  • 与香草PLM相比,SES-Adapter的下游任务性能平均提高了3%至11%.
  • 实现了趋同速度的显著加快,平均为362%,高达1034%.
  • 证明了提高训练效率 (大约2倍) 和稳定性与低质量的预测结构.

结论:

  • SES-Adapter是一种兼容且有效的方法,可以在不同的架构和任务中增强PLM.
  • 这种方法显著提高了绩效,加速了融合,提高了培训效率.
  • 通过SES-Adapter,即使有不完美的结构数据,也可以促进蛋白质表示学习.