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通过转换器编码器对个体抗体进行自然选择的定位模型
Frederick A Matsen1,2,3,4, Kevin Sung1, Mackenzie M Johnson1
1Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Molecular biology and evolution
|August 12, 2025
概括
这项研究引入了一种高分辨率模型,用于预测单个抗体氨基酸位点上的自然选择. 该模型揭示了超出传统抗体区域的复杂选择模式,有助于抗体工程.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 抗体亲和力成熟包括选择折叠和抗原结合.
- 之前的选择研究分组了序列,限制了分辨率.
- 了解特定部位的选择对于抗体工程至关重要.
研究的目的:
- 开发一个高分辨率模型来预测抗体序列中的每个氨基酸位点的选择强度.
- 分析抗体区域的净化和多样化选择的模式.
- 研究选择,抗体结构和进化史之间的关系.
主要方法:
- 开发了一个变压器编码器模型来预测单个氨基酸位点的选择强度.
- 该模型预测进化是否比中性突变更慢 (净化) 或更快 (多样化).
- 模型的性能是用持有数据来评估的.
主要成果:
- 该模型准确地预测了抗体序列上的自然选择模式.
- 选择模式并不严格地与确定互补性和框架区域保持一致.
- 在亲和力成熟过程中,净化选择增加,特别是在第三个确定互补性的区域.
结论:
- 使用变压器模型,对抗体特定部位选择的高分辨率预测是可行的.
- 抗体中的自然选择比以前分类的更复杂.
- 亲和力成熟驱动着对特定抗体区域的净化选择的重大转变.
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