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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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相关实验视频

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DG-Affinity:通过语言模型从序列中预测抗原-抗体亲和力.

Ye Yuan1, Qushuo Chen2, Jun Mao2

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China. yuanye_auto@sjtu.edu.cn.

BMC bioinformatics
|November 14, 2023
PubMed
概括
此摘要是机器生成的。

一种新的方法,DG-Affinity,只使用序列和深度学习,准确地预测抗原-抗体亲和力. 这促进了治疗开发的抗体设计.

关键词:
亲密关系 (Affinity) 是一种关系.抗体抗原相互作用深度学习是一种深度学习.序列嵌入方式 序列嵌入

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科学领域:

  • 生物技术是生物技术.
  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学

背景情况:

  • 抗体介导免疫对于人类的防御至关重要.
  • 生物工程抗体衍生药物在癌症和自身免疫性疾病方面表现有前途.
  • 对抗原-抗体亲和力的准确预测对于抗体的发展至关重要.

研究的目的:

  • 引入DG-Affinity,这是一种基于序列的新方法,用于预测抗原-抗体亲和力.
  • 在不需要结构信息的情况下证明DG-Affinity的有效性.

主要方法:

  • 利用深度神经网络和预训练的语言模型将抗体和抗原序列转化为嵌入载体.
  • 采用一个ConvNeXt框架与回归任务来预测来自连接嵌入的亲和力.

主要成果:

  • DG-Affinity仅从序列来准确预测抗原-抗体亲和力.
  • 在一个独立的测试数据集上实现了超过0.65的皮尔森相关系数.
  • 超过现有的基于结构和基于序列的预测方法.

结论:

  • 与基线方法相比,DG-Affinity显示出更高的性能.
  • 该方法可以显著推进抗体的设计和开发.
  • 为了方便使用,可以通过免费的Web服务器访问DG-Affinity.