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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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EPIPDLF:一个预训练的深度学习框架,用于预测增强器-促进器相互作用.

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  • 1School of Computer Science and Technology, Xidian University, Xi'an 710075, China.

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概括
此摘要是机器生成的。

一个新的深度学习模型,EPIPDLF,只使用基因组序列,准确预测增强剂-促进剂相互作用 (EPI). 这种可解释的方法为理解基因调节提供了比实验方法更快,更具成本效益的替代方案.

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 增强剂和促进剂是控制基因表达,平衡和疾病的关键调节性DNA元素.
  • 远端增强剂可以与促进剂相互作用以调节基因表达,这对于生物学理解至关重要.
  • 检测增强剂-促进剂相互作用 (EPI) 的实验方法通常耗时且昂贵.

研究的目的:

  • 开发一种准确和可解释的深度学习方法,用于从基因组序列中预测增强剂-促进剂相互作用 (EPI).
  • 为昂贵和耗时的实验技术提供一个计算效率高的替代方案.

主要方法:

  • 开发了EPIPDLF,这是一种使用基因组序列进行EPI预测的新型深度学习模型.
  • 在深度学习框架内内置可解释的分析机制.
  • 在六个基准数据集中评估模型性能.

主要成果:

  • 与现有方法相比,EPIPDLF在EPI预测方面表现优越且一致.
  • 该模型的可解释特征有助于识别和分析具有生物意义的序列.
  • 该方法提供了一种具有成本效益和快速的方法来预测EPI.

结论:

  • EPIPDLF提供了一个强大的,可解释的深度学习工具,用于预测增强器-促进器相互作用.
  • 这种方法通过使基因序列的有效分析,促进了对基因调节的理解.
  • 开发的模型是基因组研究和疾病机制研究的宝贵资源.