表面诱导的展开揭示了独特的结构特征,并增强了机器学习分类模型.
Rowan Matney1, Gabrielle Blake1, Varun V Gadkari1
1Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, United States.
Analytical chemistry
|March 14, 2025
概括
表面诱导的展开 (SIU) 为蛋白质分析提供了与碰撞诱导的展开 (CIU) 相似的可复制性. SIU增强了敏感性,并改善了蛋白质子类的分化,使其成为生物治疗特征的有价值工具.
科学领域:
- 生物物理化学 生物物理化学
- 分析化学 分析化学
- 结构生物学 结构生物学
背景情况:
- 具有碰撞诱导展开 (CIU) 的原生离子运动质谱 (IM-MS) 是蛋白质表征的关键,为结构稳定提供了洞察力.
- 表面诱导解离 (SID) 装置现在已经商业化,使得表面诱导展开 (SIU) 测量能够得到更广泛的使用.
- 与CIU对SIU的性能和可重复性进行评估对于其在蛋白质分析中的采用至关重要.
研究的目的:
- 为了比较表面诱导展开 (SIU) 与碰撞诱导展开 (CIU) 的可重现性和性能.
- 评估SIU在区分密切相关的蛋白质子类和增强机器学习模型方面的能力.
- 建立SIU作为蛋白质表征的补充分析方法.
主要方法:
- 在使用模型蛋白质的Waters CyclicIMS仪器上对SIU和CIU进行比较分析:β-乳糖球蛋白 (β-lac),牛血清白蛋白 (BSA) 和免疫球蛋白G1 kappa (IgG1κ).
- 在多个电荷状态中使用根平均平方偏差 (RMSD) 进行可复制性评估.
- 使用SIU和CIU数据应用监督机器学习模型进行IgG子类分类.
主要成果:
- SIU和CIU的可复制性相似,平均RMSD值低于4%.
- SIU在较低的能量下诱导了展开,揭示了微妙的结构差异和独特的展开特征.
- 基于SIU的机器学习模型在分类IgG子类和像Adalimumab和Nivolumab这样的生物治疗药物方面取得了高的交叉验证准确率 (>90%),表现优于或匹配CIU模型.
结论:
- 表面诱导的展开 (SIU) 是一种可重复和高效的替代方案,用于蛋白质的特征化碰撞诱导展开 (CIU).
- SIU提供了增强的灵敏度和分析深度,改善了蛋白质子类和密切相关变体的差异化.
- 将SIU集成到工作流中,特别是在高通量和机器学习应用中,提供了显著的分析优势.
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