一种自动化的软件辅助方法,用于探索使用高分辨率质谱仪在宏分子中的代谢敏感性和降解产物
Paula Cifuentes1,2,3, Ismael Zamora3, Tatiana Radchenko2
1Pompeu Fabra University, Barcelona Spain.
PloS one
|August 13, 2025
概括
本研究引入了使用LC-HRMS的大分子自动代谢物识别 (MetID) 的新算法. 新的MaM方法和非扩展可视化显著提高了药物开发的速度和准确性.
科学领域:
- 生物化学 生物化学
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 准确了解药物代谢对于药物开发至关重要.
- 对宏分子的自动化代谢物识别 (MetID) 面临着由于大小,复杂性和计算需求的挑战.
研究的目的:
- 开发和验证用于宏分子MetID.自动化LC-HRMS数据分析的新算法.
- 将新的质量为质量 (MaM) 峰值检测方法与传统的单同位素质量 (MiM) 方法进行比较.
- 评估不同结构可视化策略的处理时间和可解释性.
主要方法:
- 开发了针对宏分子量身定制的自动化LC-HRMS数据分析的新算法.
- 实施并比较了一种新的质量为质量 (MaM) 峰值检测算法与单同位素质量 (MiM) 方法.
- 评估了三个结构可视化策略 (扩展,非扩展,混合) 以提高处理效率和清晰度.
主要成果:
- 该工作流被验证在六个不同的数据集 (,寡核酸) 上,确定了970种代谢物.
- 与MiM相比,MaM算法表现出优异的性能,得分更高,匹配量更多.
- 非扩展可视化大大减少了处理时间 (分钟到不到一个小时),并使代谢变化的清晰定位成为可能.
结论:
- 拟议的自动化工作流通过提高处理速度,检测一致性和可视化功能来增强宏分子MetID.
- 该MaM算法提供了更高的信心在代谢物结构预测.
- 这一进步通过对大分子中药物代谢的改进分析,支持更有效的药物开发.
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