在抗体语言模型中,将选择与突变分开
Frederick A Matsen1,2,3,4, Will Dumm1, Kevin Sung1
1Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
这项研究引入了深度氨基酸选择模型 (DASM),以改善抗体功能预测. DASM将突变和选择过程分开,提高了抗体工程的准确性.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 抗体通过V(D) J重组,突变和选择进化.
- 目前的抗体语言模型侧重于氨基酸序列,隐式学习突变过程.
- 这种隐式学习降低了预测突变功能影响的性能.
研究的目的:
- 开发一种用于抗体语言建模的新框架,明确分离突变和选择.
- 改善对抗体中氨基酸突变的功能影响的预测.
- 为了创建一个更有效和可解释的抗体建模方法.
主要方法:
- 设计了一个深度氨基酸选择模型 (DASM) 框架.
- 显然排除了核酸水平突变过程.
- 适合选择效应作为一个与突变过程独立的术语.
主要成果:
- 在标准抗体功能基准测试中,DASM显著提高了性能.
- 该模型仅量化突变的功能效应.
- 获得了对抗体功能突变效应的改进预测.
结论:
- 在抗体语言模型中分离突变和选择过程可以提高功能预测.
- DASM为抗体工程提供了一种更准确,更有效的方法.
- 该模型的可解释性有助于理解抗体的进化和功能.
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