相反的学习使得针对针对性抗体发现的表位重叠预测成为可能
Clinton M Holt1,2,3, Alexis K Janke1,4, Parastoo Amlashi1,4
1Vanderbilt Center for Antibody Therapeutics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Patterns (New York, N.Y.)
|February 23, 2026
概括
预测抗体表位对药物开发至关重要. 使用抗体序列的新计算方法在识别重叠的表位上显示出高精度,有助于治疗性抗体的发现.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 准确的表位预测对于治疗性抗体开发至关重要.
- 目前用于表位预测的计算方法需要改进.
研究的目的:
- 开发和验证用于从抗体序列预测抗体表位的新型计算方法.
- 加强对表位向治疗抗体的发现.
主要方法:
- 对1800万个抗体对进行分析,以将重链互补性决定区域3 (CDRH3) 序列标识与表位重叠相关联.
- 对抗体大型语言模型的监督对比微调框架的开发,以纳入表位信息.
- 创建了AbLang-PDB,这是一个用于表位预测的通用模型.
主要成果:
- 超过70%的重链互补性决定区域3 (CDRH3) 序列标识预测了共享V基因的抗体中的重叠表位.
- 对比式学习框架在预测SARS-CoV-2抗体的结构重叠方面实现了97%的准确性.
- 在AbLang-PDB中,平均精度比基于序列的方法提高了5倍,并且与表位重叠有很强的相关性 (ρ = 0.81).
- 实验验证显示70%的HIV-1特异性和50%的有约束力的竞争对被选中的候选人.
结论:
- 基于抗体序列的计算方法可以可靠地预测表位关系.
- 相反的学习有效地将表位信息编码到抗体语言模型中.
- 这些模型为加速表位向抗体发现提供了强大的工具.
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