核酸上下文模型在预测抗体亲和力成熟时的表现优于蛋白质语言模型.
Mackenzie M Johnson1, Kevin Sung1, Hugh K Haddox1
1Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
使用核酸语境准确建模体变异 (SHM) 显著改善了对抗体亲和力成熟的预测. 基于核酸的模型在预测B细胞受体演变方面优于先进的蛋白质语言模型.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 抗体是适应性免疫的关键,从B细胞上的B细胞受体 (BCR) 发展.
- BCRs经历亲和力成熟,这是一种涉及体质突变 (SHM) 和选择的过程,以增强抗原结合.
- 亲和力成熟的计算模型是从分子进化和语言建模的角度出现的.
研究的目的:
- 为了比较分子进化和语言建模方法对抗体亲和力成熟的预测能力.
- 通过使用BCR序列数据来评估SHM,选择性SHM和蛋白质语言模型.
- 引入EPAM,用于对抗体进化预测模型进行基准测试的框架.
主要方法:
- 基于核酸的SHM模型与蛋白质语言模型在人类BCR数据和小鼠实验中进行了比较.
- 利用BCR序列的遗传树来评估模型性能.
- 包含了来自深度突变扫描实验的选择估计.
主要成果:
- 精确的SHM建模,结合核酸背景,大大改善了亲和力成熟的预测.
- 一个基于核酸的卷积神经网络建模SHM超越了最先进的蛋白质语言模型.
- 包括选择估计只为预测能力提供了适度的改善.
结论:
- 核酸背景对于准确建模体变异和预测抗体亲和力成熟度至关重要.
- 基于核酸的模型为蛋白质语言模型提供了一个强大的替代方案,用于理解B细胞受体进化.
- EPAM框架促进了对抗体进化和预测建模的进一步研究.
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