使用前体-产物离子对图的图形嵌入来预测小分子的并列质谱
Fujian Zheng1,2,3, Lei You1,2,3, Xinjie Zhao1,2,3
1CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China.
Analytical chemistry
|November 22, 2024
概括
PPGB-MS2通过将碎片化转化为强度预测来提高双重质谱 (MS/MS) 预测的准确性. 这种方法通过更好地将预测的MS / MS数据与化学结构相关联,提高了代谢学识别.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 代谢学依赖于高质量的双重质谱 (MS/MS) 数据来准确识别化合物.
- 当前的MS/MS预测方法在准确性,分辨率和结构相关性方面面临挑战.
- 开发强大的MS/MS预测对于推进代谢学研究至关重要.
研究的目的:
- 开发一种新的MS/MS预测方法,PPGB-MS2,用于增强代谢物识别.
- 提高MS/MS碎片化预测的准确性,分辨率和结构相关性.
- 利用图形神经网络 (GNN) 进行精确的断片强度预测.
主要方法:
- 引入了前体-产品离子对图表袋 (PPGBs),用于统一地表示分子结构和碎片化数据.
- 将MS/MS预测转化为使用PPGBs的碎片强度预测.
- 利用图形神经网络 (GNN) 进行基于机器学习的MS/MS碎片强度预测.
- 通过使用NIST 20协同MS数据库中的[M+H]+和[M-H]-数据来训练和评估模型.
主要成果:
- 在测试组中获得了0.71的平均等号相似性,超过了经典的MS/MS预测方法.
- 通过有效的碎片结构对应,证明了高分辨率的MS/MS预测能力.
- 成功地将化学结构信息集成到机器学习模型中进行预测.
结论:
- PPGB-MS2在MS/MS预测准确性和代谢学可靠性方面取得了重大进展.
- PPGB 代表和 GNN 提供了一个强大的框架来预测 MS/MS 碎片化模式.
- 这种方法通过提高光谱数据的质量来提高识别代谢物的能力.
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