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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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NABP-BERT:基于来自变压器 (BERT) 架构的双向编码器表示的NANOBODY®抗原结合预测.

Fatma S Ahmed1,2, Saleh Aly3, Xiangrong Liu1

  • 1Department of Computer Science and Technology, Xiamen University, Xiamen 361005, China.

Briefings in bioinformatics
|December 17, 2024
PubMed
概括

使用序列数据,NABP-BERT预测了纳米体与抗原的结合. 这种深度学习模型通过克服实验识别的局限性来加速纳米体 (Nbs) 的开发,用于各种应用.

关键词:
贝尔特 (BERT) 公司纳米人体 (NANOBODY®) 是一种抗原是一种抗原.有约束力的预测预测.深度学习是一种深度学习.序列嵌入方式 序列嵌入方式

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科学领域:

  • 免疫学 免疫学 免疫学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 抗体介导免疫在脊椎动物中至关重要.
  • 纳米体 (Nbs) 或单域抗体 (sdAbs) 与传统抗体相比具有优势,但对特定抗原 (Ags) 的可用性有限.
  • 预测Nb-Ag相互作用对于提高Nb疗效至关重要,但在实验上具有挑战性和耗时.

研究的目的:

  • 开发一种计算方法,从序列数据中预测纳米体-抗原结合.
  • 创建一个深度学习模型,NABP-BERT,用于准确的Nb-Ag相互作用预测.
  • 为蛋白质相关任务建立一个一般的预训练模型,包括蛋白质与蛋白质的相互作用.

主要方法:

  • 使用基于BERT的深度学习架构 (NABP-BERT).
  • 专注于分析氨基酸序列背景以进行结合预测.
  • 开发了一个一般的预训练模型,用于蛋白质任务的转移学习能力.

主要成果:

  • 仅使用序列信息,NABP-BERT可以准确预测纳米体-抗原结合.
  • 实现了高性能指标:AUROC为0.986和AUPR为0.985. 这两个指标是:
  • 与现有方法相比,表现出优越的性能.

结论:

  • NABP-BERT有效地预测了纳米体-抗原相互作用,解决了有限的Nb可用性的挑战.
  • 该模型的基于序列的方法为实验方法提供了具有成本效益和效率的替代方案.
  • 开发的预训练模型在蛋白质-蛋白质相互作用研究中具有广泛的适用性.