预测碰撞诱导解离并联质谱 (CID-MS/MS) 使用ab initio分子动力学
Jesi Lee1,2, Dean Joseph Tantillo1, Lee-Ping Wang1
1Department of Chemistry, University of California, Davis, California 95616, United States.
Journal of chemical information and modeling
|September 27, 2024
概括
预测未知化合物的质谱在代谢学中至关重要. 通过分子动力学 (CIDMD) 的新碰撞诱导解离框架使用量子化学准确地建模小代谢物的碰撞诱导解离质谱.
科学领域:
- 计算化学的计算化学
- 代谢学 代谢学 代谢学
- 质谱测量质量谱测量
背景情况:
- 在代谢学中,化合物识别依赖于将实验性质谱与图书馆相匹配.
- 大多数化合物缺乏商业可用性,需要MS/MS光谱预测.
- 现有的预测模型,除了脂质,只有有限的成功.
研究的目的:
- 通过分子动力学 (CIDMD) 的碰撞诱导解离框架来预测MS/MS光谱.
- 模拟分子碰撞的物理过程在CID串联质谱学使用第一原则分子动力学.
- 评估CIDMD预测与小代谢物的实验性MS/MS光谱的准确性.
主要方法:
- 模拟分子离子与原子的碰撞,使用密度函数理论和分子动力学.
- 从多个碰撞方向随着时间的推移 (≥1,000 fs) 建模键断裂.
- 通过从重复模拟中积累碎片来生成in silico质谱.
主要成果:
- CIDMD预测了12种小代谢物 (<205Da) 的261种光谱.
- 平均相似度得分达到624±189,在最佳参数下改善到828±77.
- 预测准确度因原体,碰撞器速度和分子质量而异;较高的速度改善了较大分子的结果.
结论:
- CIDMD是一种可行的工具,用于预测小代谢物的碰撞诱导解离质谱.
- 框架的准确性受质体选择和模拟参数的影响.
- 了解碎片化途径为代谢物识别提供了机理性的洞察力.
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