通过使用结构模型进行改进的计算表位素分析,可以识别与同一表位素结合的更广泛的抗体多样性
Fabian C Spoendlin1, Brennan Abanades1, Matthew I J Raybould1
1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.
Frontiers in molecular biosciences
|October 4, 2023
概括
抗体功能取决于表位细胞结合. 使用机器学习的新方法SPACE2在结构上聚合了多种抗体,以识别那些与相同表位素结合的抗体,优于基于序列的方法.
科学领域:
- 免疫学 免疫学 免疫学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 抗体功能是由其表位参与决定的.
- 目前的克隆聚类方法按序列分组抗体,忽略了可以结合共同表位的多样化序列.
- 之前的结构聚类 (SPACE1) 被模型不准确性和有限的基准测试所限制.
研究的目的:
- 开发和对SPACE2进行基准测试,这是一种改进的结构聚类抗体方法,用于预测表位细胞结合.
- 使用表位层分辨率数据评估SPACE2与克隆集群和SPACE1的性能.
主要方法:
- SPACE2 将基于机器学习的先进结构预测与一种新的集群协议集成在一起.
- 该方法在六个不同的抗原特异性抗体数据集上进行了基准测试,具有表位层分辨率.
- 基于准确的结合常见表位和数据集覆盖范围的抗体聚类来评估性能.
主要成果:
- SPACE2 准确地聚合了在不同数据集中参与共同表位的抗体.
- 与克隆集群和SPACE1.1相比,SPACE2的数据集覆盖率显著更高.
- 通过SPACE2进行结构识别的集群显示出比SPACE1.1更大的序列,遗传和物种多样性.
结论:
- 结构数据显著提高了与同一表位素结合的抗体的识别,补充了基于序列的方法.
- SPACE2有效地利用结构信息来分组功能上相似的抗体,特别是在不同的抗体数据集中.
- SPACE2提供了一个开源工具 (GitHub),用于改进抗体表位预测和分析.
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