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Protein and Protein Structure02:15

Protein and Protein Structure

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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穆兰:用于编码序列和结构的多模式蛋白质语言模型.

Daria Frolova1,2, Marina Pak1,3, Anna Litvin1,3,4

  • 1Ligand Pro, Moscow 121205, Russia.

Bioinformatics advances
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概括
此摘要是机器生成的。

穆兰是一个多式蛋白质语言模型 (PLM),通过高效地结合序列和结构数据来增强蛋白质表示. 这种结构意识的PLM可以改善预测,特别是蛋白质-蛋白质相互作用,而无需进行广泛的再培训.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 生物学中的人工智能

背景情况:

  • 蛋白质语言模型 (PLM) 主要利用蛋白质序列进行高质量的表示.
  • 纳入蛋白质结构信息对于各种预测任务至关重要,这引发了对结构感知PLM的兴趣.
  • 现有的结构意识的PLM通常需要从头开始进行训练或为结构编码增加大量的参数.

研究的目的:

  • 引入MULAN,一种多式蛋白语言模型 (PLM),集成了基于序列和角度的结构信息.
  • 开发一个参数有效的方法,将结构意识纳入预先训练的PLM中.
  • 与现有模型相比,评估MULAN在各种下游任务中的表现.

主要方法:

  • 开发了MULAN,这是一款多模式的PLM,具有预训练的序列编码器和参数高效的结构适配器.
  • 融合了序列编码器和结构适配器,将它们一起训练用于多式模式表示学习.
  • 在九个下游预测任务中对MULAN进行了评估,根据仅序列和结构意识的基线评估其性能.

主要成果:

  • 与仅序列ESM2和结构感知SaProt.Prot.相比,MULAN模型在各种尺寸中表现出更好的性能.
  • 在蛋白质-蛋白质相互作用预测方面观察到显著改善,AUROC增加了高达0.12.
  • 穆兰通过高效微调现有PLM来提高结构意识,避免了从头开始昂贵的培训.

结论:

  • 穆兰提供了一种有效且计算成本低廉的方法,以赋予蛋白质表示结构意识.
  • 拟议的结构适配器提供了一个参数高效的方式,将结构信息集成到PLM中.
  • 穆兰代表了结构意识蛋白质建模和预测任务的宝贵进步.