在PDBBind数据库中的策划错误对蛋白质-蛋白质结合亲和力的机器学习预测的影响
Jason D Gans1, Justin E Miller1, Esen Sokullu1
1Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Database : the journal of biological databases and curation
|September 25, 2025
概括
数据集的准确性对于计算建模至关重要. 对PDBBind数据库的手动分析显示,约19%的蛋白质-蛋白质结合亲和度数据是不准确的,影响了机器学习预测.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 蛋白质数据库绑定 (PDBBind) 数据库是计算预测蛋白质-蛋白质结合亲和力的关键资源.
- 之前的研究报告了PDBBind中蛋白质 - 连接体结合亲和性的化精度,但不是蛋白质 - 蛋白质相互作用.
研究的目的:
- 为了手动分析PDBBind数据库中的蛋白质-蛋白质相互作用的结合亲和力数据 (KD) 的修复精度.
- 评估潜在的策划错误对预测蛋白质-蛋白质结合亲缘关系的机器学习模型的影响.
主要方法:
- 手动审查与PubMed中央开放访问出版物相关的PDBBind记录.
- 对平衡分离常数 (KD) 值与初级出版物数据的评估.
- 机器学习模型 (随机森林) 在纠正策划错误之前和之后的绩效评估.
主要成果:
- 大约19%的分析的PDBBind记录中的蛋白质与蛋白质相互作用含有KD值,其主要出版物不支持.
- 纠正这些校准错误使得测量和预测的log10 (KD) 值之间的皮尔森相关系数提高了约8个百分点.
结论:
- 数据集的准确性显著影响了蛋白质-蛋白质结合亲缘关系的计算建模的可靠性.
- 为了从科学文献中提取和利用具有约束力的亲和数据,更严格的策划过程是必不可少的.
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