调查基于深度学习的结构预测能力,以推断和/或丰富抗体CDR法典形式的抗体集
Alexander Greenshields-Watson1, Brennan Abanades1, Charlotte M Deane1
1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.
Frontiers in immunology
|March 14, 2024
概括
深度学习模型可以预测新的抗体结构,但不能产生完全新的CDR循环形状. 即使是最小的数据也可以准确预测已知的结构形状.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 深度学习模型可以从序列中准确预测蛋白质结构.
- 研究人员的目标是探索超越培训数据的新型结构特征.
研究的目的:
- 调查深度学习模型是否可以预测培训数据中不存在的新环形状.
- 评估抗体结构预测模型的推断能力.
主要方法:
- 使用ABodyBuilder2预测大约1.5M个抗体序列的结构.
- 在预测结构中分析了正统的CDR循环构造.
- 重新训练的模型不包括特定的CDR循环长度或规范形式来测试概括.
主要成果:
- 大多数预测的CDR循环符合已知的法典形式.
- 识别了具有共同的序列动图和循环构造的新型集群,源自训练数据中的低频或更短的序列.
- 模型在不同的CDR长度上进行了概括,但没有推断到高度不同的循环形状.
- 准确预测法典形式是可能的,与最小的训练数据.
结论:
- 用于蛋白质结构预测的深度学习模型无法生成完全新的CDR循环构造.
- 这些模型展示了跨变异的概括,但缺乏对不同构造的真正域外推断.
- 即使对特定结构形状的有限数据也可以进行准确的预测.
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